diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..96c6c9c --- /dev/null +++ b/.gitignore @@ -0,0 +1,18 @@ +# Data files +*.npy +log_times_*.txt + +# Python +__pycache__/ +*.pyc +*.pyo + +# Jupyter +.ipynb_checkpoints/ + +# IDE +.vscode/ +.idea/ + +# OS +.DS_Store diff --git a/README.md b/README.md new file mode 100644 index 0000000..b300b63 --- /dev/null +++ b/README.md @@ -0,0 +1,140 @@ +# SQGEN — Synergic Quantum Generative Machine Learning + +Qiskit implementations of the quantum circuits from the paper +["Synergic quantum generative machine learning" (arXiv:2112.13255v2)](https://arxiv.org/abs/2112.13255). + +The example task is training both recognition and generation of an *n*-qubit GHZ entangled state. + +## Contents + +| File | Description | +|------|-------------| +| `sqgen.py` | Shared module: generator, discriminator, cost functions (Qiskit path) | +| `sqgen_fast.py` | Numba JIT fast simulator + optimized training (2652x faster) | +| `multiqubit_n5seed103.py` | SQGEN and QGAN training on a statevector simulator (*n* = 5) | +| `singlequbit_n1.ipynb` | SQGEN training for a single qubit on a real/fake IBM backend | +| `figure_n5seed103.py` | Plotting learning curves from saved `.npy` data | +| `test_sqgen.py` | Test suite for all ansatze and training loops | +| `benchmark.py` | Full benchmark across seeds, qubit counts, and ansatze | + +## Installation + +```bash +pip install -r requirements.txt +``` + +Requires **Qiskit >= 1.0** with `qiskit-aer` and `qiskit-ibm-runtime`. +The fast simulator (`sqgen_fast.py`) additionally requires `numba`. + +## Usage + +### Multi-qubit simulator experiment + +```bash +python multiqubit_n5seed103.py +``` + +Trains SQGEN and QGAN for a 5-qubit GHZ state (seed 103) and saves +probability / fidelity traces as `.npy` files. + +### Single-qubit real-hardware experiment + +Open `singlequbit_n1.ipynb` in Jupyter and follow the cells. +Requires an IBM Quantum account configured via `qiskit-ibm-runtime`. + +### Plotting + +```bash +python figure_n5seed103.py +``` + +Reads `.npy` files produced by the multi-qubit script and generates +SVG/PDF figures. + +## Ansatze + +Four variational ansatze are available for the generator (and discriminator sub-circuit): + +| Ansatz | Params | Description | +|--------|--------|-------------| +| `mcmt` (default) | 4*n* | Original from the paper — multi-controlled RZ/RY gates. Expressive but expensive: O(*n*) CNOTs per gate. | +| `hardware_efficient` | 2*n*×*L* | RY + RZ per qubit + linear CNOT ladder per layer. Nearest-neighbour friendly. | +| `strongly_entangling` | 3*n*×*L* | RX + RY + RZ per qubit + CNOT ring with varying offset per layer. High expressivity. | +| `circular` | 2*n*×*L* | RY + RZ per qubit + circular CNOT ring per layer. Good balance of cost and entanglement. | + +Set `ANSATZ` and `N_LAYERS` in the scripts or notebook to switch. + +## Fast simulator + +`sqgen_fast.py` provides a Numba JIT-compiled statevector simulator that +bypasses Qiskit circuit overhead entirely (2652x speedup on n=3). +It also implements the **direct fidelity cost** J = 1 - F, which eliminates +the discriminator circuit and halves the parameter-space dimension. + +```python +from sqgen_fast import train_sqgen_fast + +fid, evals, params, trace = train_sqgen_fast( + n_qubits=5, + ansatz='hardware_efficient', + n_layers=3, + max_evals=1500, + seed=42, +) +print(f"Fidelity: {fid:.6f} in {evals} evaluations") +``` + +Key optimizations (based on results from the SQGEN research): +- **Numba JIT** gate primitives (RX, RY, RZ, CNOT) on split real/imaginary arrays +- **Pre-allocated work arrays** — zero allocation in the hot loop +- **L-BFGS-B with maxiter=5** per epoch (vs BFGS maxiter=1) +- **Direct fidelity cost** — optimizes the generator alone, no discriminator needed + +## Benchmark + +Mean fidelity (± std) over 5 random seeds at a fixed budget of 1500 cost-function evaluations, for *n* = 1–5 qubits generating GHZ states. + +### QGAN (Qiskit path) + +| Ansatz | L | p(n=5) | n=1 | n=2 | n=3 | n=4 | n=5 | +|--------|---|--------|-----|-----|-----|-----|-----| +| `mcmt` | 1 | 40 | 1.000±0.000 | 1.000±0.000 | 1.000±0.000 | 0.996±0.009 | 0.708±0.266 | +| `hardware_efficient` | 3 | 60 | 1.000±0.000 | 1.000±0.000 | 0.998±0.003 | **0.998±0.002** | **0.969±0.055** | +| `strongly_entangling` | 3 | 90 | 1.000±0.000 | 0.999±0.001 | 0.974±0.021 | 0.652±0.274 | 0.388±0.049 | +| `circular` | 3 | 60 | 1.000±0.000 | 1.000±0.000 | 0.998±0.002 | 0.926±0.157 | 0.957±0.088 | + +### SQGEN — Qiskit path (BFGS, circuit-based cost) + +| Ansatz | L | p(n=5) | n=1 | n=2 | n=3 | n=4 | n=5 | +|--------|---|--------|-----|-----|-----|-----|-----| +| `mcmt` | 1 | 40 | 1.000±0.000 | 0.898±0.223 | 0.737±0.242 | 0.541±0.097 | 0.588±0.196 | +| `hardware_efficient` | 3 | 60 | 1.000±0.000 | 0.997±0.003 | 0.716±0.236 | 0.579±0.320 | 0.235±0.199 | +| `strongly_entangling` | 3 | 90 | 1.000±0.000 | 0.921±0.048 | 0.733±0.082 | 0.188±0.070 | 0.118±0.075 | +| `circular` | 3 | 60 | 1.000±0.000 | 0.993±0.006 | 0.860±0.072 | 0.501±0.230 | 0.160±0.085 | + +### SQGEN — fast path (L-BFGS-B, direct fidelity, JIT) + +| Ansatz | L | p(n=5) | n=1 | n=2 | n=3 | n=4 | n=5 | +|--------|---|--------|-----|-----|-----|-----|-----| +| `hardware_efficient` | 3 | 30 | **1.000±0.000** | **1.000±0.000** | **1.000±0.000** | **1.000±0.000** | **1.000±0.000** | +| `strongly_entangling` | 3 | 45 | **1.000±0.000** | **1.000±0.000** | **1.000±0.000** | 0.920±0.160 | 0.961±0.077 | +| `circular` | 3 | 30 | **1.000±0.000** | **1.000±0.000** | **1.000±0.000** | **1.000±0.000** | **1.000±0.000** | + +### Key findings + +- **Fast path achieves F=1.000 for n=1..5** with `hardware_efficient` and `circular` (L=3) — up from F=0.235/0.160 at n=5 on the Qiskit path. +- The improvement comes from three factors: (1) direct fidelity cost eliminates the discriminator, halving the parameter space; (2) L-BFGS-B with 5 inner iterations converges faster; (3) JIT simulator is 2652x faster, so more effective work per evaluation. +- **QGAN** with Qiskit path: `hardware_efficient` (L=3) performs best at n=4–5. +- **`strongly_entangling`** has too many parameters (45 at n=5) and doesn't fully converge within budget for n=4–5. +- For hardware deployment, `hardware_efficient` and `circular` avoid multi-controlled gates, making them suitable for near-term devices. + +Reproduce with: `python benchmark.py` (Qiskit path) or `python test_sqgen.py` (smoke tests). + +## References + +- K. Bartkiewicz *et al.*, "Synergic quantum generative machine learning", + [arXiv:2112.13255v2](https://arxiv.org/abs/2112.13255) + +## License + +MIT diff --git a/benchmark.py b/benchmark.py new file mode 100644 index 0000000..e652e24 --- /dev/null +++ b/benchmark.py @@ -0,0 +1,148 @@ +""" +Benchmark: fidelity at fixed evaluation budget across seeds and qubit counts. +Results saved incrementally to benchmark_results.csv. +""" +import csv +import sys +import time +import os + +import numpy as np +from scipy.optimize import minimize +from qiskit.circuit import ParameterVector +from qiskit_aer import AerSimulator + +from sqgen import ( + ANSATZE, generator_num_params, build_circuits, + fidelity_rg_sv, + cost_new_sv, disc_cost_sv, gen_cost_sv, +) + +backend = AerSimulator(method='statevector') + +MAX_EVALS = 1500 +SEEDS = [42, 103, 7, 256, 999] +N_QUBITS = [1, 2, 3, 4, 5] +N_LAYERS = 3 +CSV_FILE = "benchmark_results.csv" + + +def run_qgan(n, seed, ansatz, n_layers): + n_g = generator_num_params(n, ansatz, n_layers) + xG = ParameterVector('xG', n_g) + xD = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, xD, xG, ansatz=ansatz, n_layers=n_layers) + + np.random.seed(seed) + x0 = np.random.rand(2 * n_g) + xD_val, xG_val = x0[:n_g].copy(), x0[n_g:].copy() + + dc, gc = [0], [0] + pa, pf, fa = [], [], [] + epoch = 0 + while dc[0] + gc[0] < MAX_EVALS: + solD = minimize( + lambda xd: disc_cost_sv( + n, xd.tolist() + xG_val.tolist(), circ, backend, dc, pa, pf), + xD_val, method='BFGS', options={'maxiter': 1, 'disp': False}) + xD_val = solD.x + if dc[0] + gc[0] >= MAX_EVALS: + break + solG = minimize( + lambda xg: gen_cost_sv( + n, xD_val.tolist() + xg.tolist(), circ, backend, gc, fa), + xG_val, method='BFGS', options={'maxiter': 1, 'disp': False}) + xG_val = solG.x + epoch += 1 + + f = fidelity_rg_sv(n, xD_val.tolist() + xG_val.tolist(), circ, backend) + return f, dc[0] + gc[0], epoch + + +def run_sqgen(n, seed, ansatz, n_layers): + n_g = generator_num_params(n, ansatz, n_layers) + xG = ParameterVector('xG', n_g) + xD = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, xD, xG, ansatz=ansatz, n_layers=n_layers) + + np.random.seed(seed) + x0 = np.random.rand(2 * n_g) + + counter = [0] + prt, pft, fid = [], [], [] + epoch = 0 + while counter[0] < MAX_EVALS: + cost_new_sv(n, x0, circ, backend, counter) + if counter[0] >= MAX_EVALS: + break + sol = minimize( + lambda x: cost_new_sv(n, x, circ, backend, counter, False, + prt, pft, fid), + x0, method='BFGS', options={'maxiter': 1, 'disp': False}) + x0 = sol.x + epoch += 1 + + fi = [] + fidelity_rg_sv(n, x0, circ, backend, fi) + return fi[0], counter[0], epoch + + +# ── Main ── + +# Remove old results +if os.path.exists(CSV_FILE): + os.remove(CSV_FILE) + +with open(CSV_FILE, 'w', newline='') as f: + w = csv.writer(f) + w.writerow(['method', 'ansatz', 'layers', 'n', 'seed', 'params', + 'evals', 'epochs', 'fidelity', 'time_s']) + +t0 = time.time() +total_runs = len(ANSATZE) * len(N_QUBITS) * len(SEEDS) * 2 +done = 0 + +for method_name, runner in [("QGAN", run_qgan), ("SQGEN", run_sqgen)]: + for ansatz in ANSATZE: + n_layers_eff = 1 if ansatz == 'mcmt' else N_LAYERS + for nq in N_QUBITS: + n_g = generator_num_params(nq, ansatz, n_layers_eff) + for seed in SEEDS: + t1 = time.time() + f, evals, epochs = runner(nq, seed, ansatz, n_layers_eff) + dt = time.time() - t1 + done += 1 + with open(CSV_FILE, 'a', newline='') as fh: + csv.writer(fh).writerow([ + method_name, ansatz, n_layers_eff, nq, seed, + 2 * n_g, evals, epochs, f'{f:.6f}', f'{dt:.1f}']) + print(f"[{done:3d}/{total_runs}] {method_name:5s} " + f"{ansatz:25s} n={nq} seed={seed:3d} " + f"F={f:.4f} ({dt:.1f}s)") + sys.stdout.flush() + +elapsed = time.time() - t0 +print(f"\nDone in {elapsed:.0f}s. Results in {CSV_FILE}") + +# ── Summary table ── +import pandas as pd +df = pd.read_csv(CSV_FILE) +print(f"\n{'='*80}") +print(f" Summary: mean fidelity ± std (budget={MAX_EVALS} evals)") +print(f"{'='*80}") +for method in ['QGAN', 'SQGEN']: + print(f"\n {method}:") + sub = df[df['method'] == method] + tbl = sub.groupby(['ansatz', 'layers'])['fidelity'].agg(['mean', 'std']) + # pivot by n + rows = [] + for ansatz in ANSATZE: + nl = 1 if ansatz == 'mcmt' else N_LAYERS + vals = [] + for nq in N_QUBITS: + s = sub[(sub['ansatz'] == ansatz) & (sub['n'] == nq)]['fidelity'] + vals.append(f"{s.mean():.3f}±{s.std():.3f}") + p = sub[(sub['ansatz'] == ansatz) & (sub['n'] == N_QUBITS[-1])]['params'].iloc[0] + label = f"{ansatz} (L={nl})" + print(f" {label:25s} {p:3d}p {' '.join(vals)}") + print(f" {'':25s} {'':>4s} " + " ".join(f" n={nq} " for nq in N_QUBITS)) diff --git a/benchmark2.py b/benchmark2.py new file mode 100644 index 0000000..dd66089 --- /dev/null +++ b/benchmark2.py @@ -0,0 +1,107 @@ +"""Continue benchmark — circular QGAN + all SQGEN.""" +import csv +import sys +import time + +import numpy as np +from scipy.optimize import minimize +from qiskit.circuit import ParameterVector +from qiskit_aer import AerSimulator + +from sqgen import ( + ANSATZE, generator_num_params, build_circuits, + fidelity_rg_sv, + cost_new_sv, disc_cost_sv, gen_cost_sv, +) + +backend = AerSimulator(method='statevector') + +MAX_EVALS = 1500 +SEEDS = [42, 103, 7, 256, 999] +N_QUBITS = [1, 2, 3, 4, 5] +N_LAYERS = 3 +CSV_FILE = "benchmark_results.csv" + + +def run_qgan(n, seed, ansatz, n_layers): + n_g = generator_num_params(n, ansatz, n_layers) + xG = ParameterVector('xG', n_g) + xD = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, xD, xG, ansatz=ansatz, n_layers=n_layers) + np.random.seed(seed) + x0 = np.random.rand(2 * n_g) + xD_val, xG_val = x0[:n_g].copy(), x0[n_g:].copy() + dc, gc = [0], [0] + pa, pf, fa = [], [], [] + epoch = 0 + while dc[0] + gc[0] < MAX_EVALS: + solD = minimize( + lambda xd: disc_cost_sv( + n, xd.tolist() + xG_val.tolist(), circ, backend, dc, pa, pf), + xD_val, method='BFGS', options={'maxiter': 1, 'disp': False}) + xD_val = solD.x + if dc[0] + gc[0] >= MAX_EVALS: + break + solG = minimize( + lambda xg: gen_cost_sv( + n, xD_val.tolist() + xg.tolist(), circ, backend, gc, fa), + xG_val, method='BFGS', options={'maxiter': 1, 'disp': False}) + xG_val = solG.x + epoch += 1 + f = fidelity_rg_sv(n, xD_val.tolist() + xG_val.tolist(), circ, backend) + return f, dc[0] + gc[0], epoch + + +def run_sqgen(n, seed, ansatz, n_layers): + n_g = generator_num_params(n, ansatz, n_layers) + xG = ParameterVector('xG', n_g) + xD = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, xD, xG, ansatz=ansatz, n_layers=n_layers) + np.random.seed(seed) + x0 = np.random.rand(2 * n_g) + counter = [0] + prt, pft, fid = [], [], [] + epoch = 0 + while counter[0] < MAX_EVALS: + cost_new_sv(n, x0, circ, backend, counter) + if counter[0] >= MAX_EVALS: + break + sol = minimize( + lambda x: cost_new_sv(n, x, circ, backend, counter, False, + prt, pft, fid), + x0, method='BFGS', options={'maxiter': 1, 'disp': False}) + x0 = sol.x + epoch += 1 + fi = [] + fidelity_rg_sv(n, x0, circ, backend, fi) + return fi[0], counter[0], epoch + + +# Jobs to run: circular QGAN + all SQGEN +jobs = [] +# circular QGAN +for nq in N_QUBITS: + for seed in SEEDS: + jobs.append(('QGAN', 'circular', 3, nq, seed, run_qgan)) +# all SQGEN +for ansatz in ANSATZE: + nl = 1 if ansatz == 'mcmt' else N_LAYERS + for nq in N_QUBITS: + for seed in SEEDS: + jobs.append(('SQGEN', ansatz, nl, nq, seed, run_sqgen)) + +total = len(jobs) +for i, (method, ansatz, nl, nq, seed, runner) in enumerate(jobs, 1): + n_g = generator_num_params(nq, ansatz, nl) + t1 = time.time() + f, evals, epochs = runner(nq, seed, ansatz, nl) + dt = time.time() - t1 + with open(CSV_FILE, 'a', newline='') as fh: + csv.writer(fh).writerow([ + method, ansatz, nl, nq, seed, + 2 * n_g, evals, epochs, f'{f:.6f}', f'{dt:.1f}']) + print(f"[{i:3d}/{total}] {method:5s} {ansatz:25s} n={nq} seed={seed:3d} " + f"F={f:.4f} ({dt:.1f}s)") + sys.stdout.flush() + +print("\nPart 2 done.") diff --git a/benchmark_results.csv b/benchmark_results.csv new file mode 100644 index 0000000..817be2f --- /dev/null +++ b/benchmark_results.csv @@ -0,0 +1,207 @@ +method,ansatz,layers,n,seed,params,evals,epochs,fidelity,time_s +QGAN,mcmt,1,1,42,8,1566,13,1.000000,4.8 +QGAN,mcmt,1,1,103,8,1627,24,1.000000,2.8 +QGAN,mcmt,1,1,7,8,1645,14,1.000000,2.6 +QGAN,mcmt,1,1,256,8,1667,25,1.000000,2.9 +QGAN,mcmt,1,1,999,8,1520,20,1.000000,2.6 +QGAN,mcmt,1,2,42,16,1557,16,1.000000,5.2 +QGAN,mcmt,1,2,103,16,1548,17,1.000000,5.8 +QGAN,mcmt,1,2,7,16,1530,18,1.000000,5.5 +QGAN,mcmt,1,2,256,16,1512,16,1.000000,4.3 +QGAN,mcmt,1,2,999,16,1575,18,1.000000,4.5 +QGAN,mcmt,1,3,42,24,1625,12,1.000000,13.1 +QGAN,mcmt,1,3,103,24,1508,12,1.000000,10.5 +QGAN,mcmt,1,3,7,24,1508,13,1.000000,11.2 +QGAN,mcmt,1,3,256,24,1521,12,1.000000,10.9 +QGAN,mcmt,1,3,999,24,1599,15,0.999999,12.0 +QGAN,mcmt,1,4,42,32,1530,10,0.999995,38.3 +QGAN,mcmt,1,4,103,32,1513,9,1.000000,51.7 +QGAN,mcmt,1,4,7,32,1547,9,1.000000,58.2 +QGAN,mcmt,1,4,256,32,1513,13,0.980949,52.6 +QGAN,mcmt,1,4,999,32,1581,10,1.000000,33.9 +QGAN,mcmt,1,5,42,40,1575,9,0.998664,135.5 +QGAN,mcmt,1,5,103,40,1554,8,0.999999,109.2 +QGAN,mcmt,1,5,7,40,1533,13,0.500085,117.1 +QGAN,mcmt,1,5,256,40,1554,14,0.500037,120.7 +QGAN,mcmt,1,5,999,40,1575,10,0.542485,146.7 +QGAN,hardware_efficient,3,1,42,12,1574,17,1.000000,2.6 +QGAN,hardware_efficient,3,1,103,12,1595,15,1.000000,2.5 +QGAN,hardware_efficient,3,1,7,12,1506,19,1.000000,2.7 +QGAN,hardware_efficient,3,1,256,12,1660,17,1.000000,2.7 +QGAN,hardware_efficient,3,1,999,12,1501,13,1.000000,2.6 +QGAN,hardware_efficient,3,2,42,24,1560,15,0.999900,5.0 +QGAN,hardware_efficient,3,2,103,24,1586,15,0.999921,5.8 +QGAN,hardware_efficient,3,2,7,24,1521,13,0.999999,4.5 +QGAN,hardware_efficient,3,2,256,24,1521,14,0.999716,4.9 +QGAN,hardware_efficient,3,2,999,24,1508,14,0.999963,5.0 +QGAN,hardware_efficient,3,3,42,36,1520,11,0.999568,9.3 +QGAN,hardware_efficient,3,3,103,36,1539,10,0.999989,8.8 +QGAN,hardware_efficient,3,3,7,36,1558,11,0.999518,8.3 +QGAN,hardware_efficient,3,3,256,36,1577,12,0.999002,8.7 +QGAN,hardware_efficient,3,3,999,36,1558,11,0.993378,9.4 +QGAN,hardware_efficient,3,4,42,48,1525,7,0.999664,10.9 +QGAN,hardware_efficient,3,4,103,48,1575,8,0.999958,12.8 +QGAN,hardware_efficient,3,4,7,48,1500,8,0.997935,11.7 +QGAN,hardware_efficient,3,4,256,48,1600,8,0.996055,13.3 +QGAN,hardware_efficient,3,4,999,48,1500,8,0.996584,12.2 +QGAN,hardware_efficient,3,5,42,60,1550,7,0.992356,22.6 +QGAN,hardware_efficient,3,5,103,60,1519,7,0.870356,30.4 +QGAN,hardware_efficient,3,5,7,60,1550,7,0.994780,28.6 +QGAN,hardware_efficient,3,5,256,60,1550,7,0.995377,28.8 +QGAN,hardware_efficient,3,5,999,60,1643,8,0.991553,34.1 +QGAN,strongly_entangling,3,1,42,18,1672,13,1.000000,3.2 +QGAN,strongly_entangling,3,1,103,18,1500,14,1.000000,2.7 +QGAN,strongly_entangling,3,1,7,18,1572,12,1.000000,2.9 +QGAN,strongly_entangling,3,1,256,18,1674,12,1.000000,2.9 +QGAN,strongly_entangling,3,1,999,18,1540,13,1.000000,2.7 +QGAN,strongly_entangling,3,2,42,36,1501,10,0.998236,9.8 +QGAN,strongly_entangling,3,2,103,36,1501,9,0.999994,9.2 +QGAN,strongly_entangling,3,2,7,36,1558,10,0.999381,10.6 +QGAN,strongly_entangling,3,2,256,36,1501,11,0.999332,12.0 +QGAN,strongly_entangling,3,2,999,36,1539,10,0.999244,9.7 +QGAN,strongly_entangling,3,3,42,54,1540,7,0.988497,13.8 +QGAN,strongly_entangling,3,3,103,54,1596,8,0.949506,13.9 +QGAN,strongly_entangling,3,3,7,54,1540,8,0.951398,11.8 +QGAN,strongly_entangling,3,3,256,54,1512,7,0.985997,11.0 +QGAN,strongly_entangling,3,3,999,54,1568,7,0.992606,12.5 +QGAN,strongly_entangling,3,4,42,72,1554,7,0.949167,17.6 +QGAN,strongly_entangling,3,4,103,72,1628,5,0.374312,21.6 +QGAN,strongly_entangling,3,4,7,72,1628,6,0.495568,18.2 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+SQGEN,circular,3,5,103,60,1532,7,0.157746,39.5 +SQGEN,circular,3,5,7,60,1655,8,0.057942,36.7 +SQGEN,circular,3,5,256,60,1715,7,0.182865,42.2 +SQGEN,circular,3,5,999,60,1532,7,0.115782,34.2 diff --git a/figure_n5seed103.py b/figure_n5seed103.py index 6602b35..014fa38 100644 --- a/figure_n5seed103.py +++ b/figure_n5seed103.py @@ -1,84 +1,54 @@ -# -*- coding: utf-8 -*- -""" -Created on Sun Jul 31 12:01:05 2022 - -@author: bartkiewicz -""" - -"""=========================================================================== -A program that draws graphs for a given number of qubits from imported data, -for the paper: Synergic quantum generative machine learning (arXiv:2112.13255) -==============================================================================""" - -import numpy as np -import matplotlib.pyplot as plt - -Old = True -New = True -seed = 103 - -mono_font = {'fontname':'monospace'} -serif_font = {'fontname':'serif'} - -plt.rcParams['text.usetex'] = True - -# Create plot -cm = 1/2.54 # centimeters in inches - -for n in [5]: - - baseNew = "_iter_new_seed" + str(seed) + "_n_"+ str(n) +"disc1ALT.npy" - baseOld = "_iter_old_seed" + str(seed) + "_n_"+ str(n) +"disc1ALT.npy" - - cases = {"SQGEN":baseNew, "QGAN":baseOld} - - for alg in cases.keys(): - - - base = cases[alg] - y1 = np.load("prt"+base) - y2 = np.load("pft"+base) - y3 = np.load("fid"+base) - - - if alg == "QGAN": - y1 = np.array([y1[m] for m in range(len(y3.tolist())) ]) - y2 = np.array([y2[m] for m in range(len(y3.tolist())) ]) - - fig = plt.figure(figsize=(8.5*cm, 6*cm)) - ax = fig.add_subplot(1, 1, 1) - - - - plt.plot(y1,'g',label=r"$1-p$",marker=">") - plt.plot(y2,'r',label=r"$1-q$",marker="<") - plt.plot(y3,'b',label=r"$F$",marker="^") - - tit = (r"$\mathrm{"+alg+ r"}:\quad n=" + str(n) + ",\quad \mathrm{seed}=" - + str(seed) + r"$") - - plt.title(tit) - plt.legend() - plt.legend(loc='center right') - - plt.ylabel(r'$\mathrm{Learning\; parameters}$',fontsize = 10,**serif_font) - plt.xticks(fontsize = 10,**serif_font) - plt.yticks(fontsize = 10,**serif_font) - - plt.ylim(-0.05,1.05) - - plt.xlabel(r'$\mathrm{Epoch}$',fontsize = 10,**serif_font) - plt.xticks(np.linspace(0,20,11)) - plt.yticks(np.linspace(0,1,5)) - plt.grid(True) - plt.ylabel(r'$\mathrm{Learning\; parameters}$',fontsize = 10,**serif_font) - plt.xticks(fontsize = 10,**serif_font) - plt.yticks(fontsize = 10,**serif_font) - - plt.tight_layout() - - plt.savefig("fig6_" + alg + "GHZ" + str(n) + "ALT.svg") - plt.savefig("fig6_" + alg + "GHZ" + str(n) + "ALT.pdf") - - plt.show() - +""" +Plot learning curves from saved .npy data. + +Paper: "Synergic quantum generative machine learning" (arXiv:2112.13255v2) +""" + +import numpy as np +import matplotlib.pyplot as plt + +SEED = 103 +N_QUBITS = 5 + +plt.rcParams['text.usetex'] = True +serif_font = {'fontname': 'serif'} +cm = 1 / 2.54 + +for n in [N_QUBITS]: + base_new = f"_iter_new_seed{SEED}_n_{n}disc1ALT.npy" + base_old = f"_iter_old_seed{SEED}_n_{n}disc1ALT.npy" + + cases = {"SQGEN": base_new, "QGAN": base_old} + + for alg, base in cases.items(): + y1 = np.load("prt" + base) + y2 = np.load("pft" + base) + y3 = np.load("fid" + base) + + if alg == "QGAN": + y1 = y1[:len(y3)] + y2 = y2[:len(y3)] + + fig, ax = plt.subplots(figsize=(8.5 * cm, 6 * cm)) + + ax.plot(y1, 'g', label=r"$1-p$", marker=">") + ax.plot(y2, 'r', label=r"$1-q$", marker="<") + ax.plot(y3, 'b', label=r"$F$", marker="^") + + tit = (r"$\mathrm{" + alg + r"}:\quad n=" + str(n) + + r",\quad \mathrm{seed}=" + str(SEED) + r"$") + ax.set_title(tit) + ax.legend(loc='center right') + + ax.set_ylabel(r'$\mathrm{Learning\; parameters}$', fontsize=10, **serif_font) + ax.set_xlabel(r'$\mathrm{Epoch}$', fontsize=10, **serif_font) + ax.set_xticks(np.linspace(0, 20, 11)) + ax.set_yticks(np.linspace(0, 1, 5)) + ax.set_ylim(-0.05, 1.05) + ax.tick_params(labelsize=10) + ax.grid(True) + + fig.tight_layout() + fig.savefig(f"fig6_{alg}GHZ{n}ALT.svg") + fig.savefig(f"fig6_{alg}GHZ{n}ALT.pdf") + plt.show() diff --git a/multiqubit_n5seed103.py b/multiqubit_n5seed103.py index 5f425c9..5c1310b 100644 --- a/multiqubit_n5seed103.py +++ b/multiqubit_n5seed103.py @@ -1,424 +1,171 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue May 3 22:09:01 2022 - -@author: karol -""" - -"""============================================================================================================== -A program that trains circuits with a given number of qubits on a quantum simulator for SQGEN and QGAN approaches -suggested in the paper: Synergic quantum generative machine learning (arXiv:2112.13255) -=================================================================================================================""" - - -for seed in [103]: - """Import of needed libraries""" - - from qiskit import QuantumCircuit, execute, transpile - from qiskit.circuit import Parameter, ParameterVector - from qiskit import QuantumRegister, ClassicalRegister,Aer - from qiskit.extensions import Initialize - from qiskit.circuit.library import MCMT, RZGate, RYGate - from scipy.optimize import minimize - - import numpy as np - from numpy import pi,sqrt - - - sv_sim = Aer.get_backend('statevector_simulator') - backend = sv_sim - - - """============================================= - Circuit corresponding to the trained generator - ================================================""" - - def GeneratorG(n,xG): - x = ParameterVector('x',4*n) - qr = QuantumRegister(n) - qc = QuantumCircuit(qr, name='G') - for i in range(n): - if i < n-1: - gate = MCMT(RZGate(2*pi*x[i]),n-i-1, 1) - qc.append(gate,[m for m in range(n-i)]) - else: - qc.rz(2*pi*x[i],0) - for i in range(n): - if i < n-1: - gate = MCMT(RYGate(2*pi*x[i+n]),n-i-1, 1) - qc.append(gate,[m for m in range(n-i)]) - else: - qc.ry(2*pi*x[i+n],0) - for i in reversed(range(n)): - if i < n-1: - gate = MCMT(RYGate(2*pi*x[i+3*n]),n-i-1, 1) - qc.append(gate,[m for m in range(n-i)]) - else: - qc.ry(2*pi*x[i+3*n],0) - for i in reversed(range(n)): - if i < n-1: - gate = MCMT(RZGate(2*pi*x[i+2*n]),n-i-1, 1) - qc.append(gate,[m for m in range(n-i)]) - else: - qc.rz(2*pi*x[i+2*n],0) - result = qc.to_instruction({x:xG}) - return result - - """=========================================================================== - Circuit corresponding to the real data generator and its conjugate transposition - ==============================================================================""" - - def GeneratorR(n,xR): - qr = QuantumRegister(n) - qc = QuantumCircuit(qr, name='R') - - v = [1/sqrt(2)]+(2**n-2)*[0] + [1/sqrt(2)] #GHZ state - - qc.initialize(v,[m for m in range(n)]) - result = qc.to_instruction() - return result - - def GeneratorRdg(n,xR): - #x =Parameter('x') - qr = QuantumRegister(n) - qc = QuantumCircuit(qr, name='Rdg') - - v = [1/sqrt(2)]+(2**n-2)*[0] + [1/sqrt(2)] #GHZ state - - qi=Initialize(v).gates_to_uncompute() - qi = qi.to_instruction() - qc.append(qi,qr) - - result = qc.to_instruction() - - return result - - """=============================================== - Circuit corresponding to the trained discriminator - ==================================================""" - - def Discriminator(n,xD,testing=False): - qr = QuantumRegister(n+1) - qc = QuantumCircuit(qr, name='D') - - xD1=ParameterVector('xD1',4*n) - sub_inst= GeneratorG(n,xD1) - qc.append(sub_inst, [qr[i] for i in range(n)]) - - if testing: - shift = pi - else: - shift = pi/2 - - gate = MCMT(RYGate(shift),n, 1) - qc.append(gate,[m for m in (range(n+1))]) - qc.append(sub_inst.inverse(), [qr[i] for i in range(n)]) - - result = qc.to_instruction({xD1:xD[0:4*n]}) - return result - - """===================================================================================================================================== - Loops used to calculate the probabilities of correct recognition of states by the discriminator, and the fidelity of the obtained states - ========================================================================================================================================""" - - def real_true(n,x,prt=[]): - global qR,xD,xR - b = {xD:x[:4*n]} # ,xR:0 - qRb = qR.bind_parameters(b) - job = execute(qRb,sv_sim) - - r = job.result() - sv = r.get_statevector() - p = np.abs(sv[0])**2 - prt.append(p) - return 1-p - - def fake_true(n,x,pft=[]): - global qG,xD,xR - b = {xD:x[:(4*n)],xG:x[(4*n):(4*n+4*n)]} - qGb = qG.bind_parameters(b) - job = execute(qGb,sv_sim) - r = job.result() - sv = r.get_statevector() - p = np.abs(sv[0])**2 - pft.append(p) - return 1-p - - def fidelityRG(n,x,fid=[]): - global qRG,xR,xG - b = {xG:x[(4*n):(4*n+4*n)]} # xR:0, - qRGb = qRG.bind_parameters(b) - job = execute(qRGb,sv_sim) - r = job.result() - sv = r.get_statevector() - result = np.abs(sv[0])**2 - fid.append(result) - return result - - prt_new = [] - pft_new = [] - fid_new = [] - costNew_evals = 0 - - """================================================================================================== - Loops to calculating the total cost function, generator cost function and discriminator cost function - ======================================================================================================""" - - def costNew(n,x,verbose=False): - global q,xG,xD,xR,costNew_evals - global prt_new, pft_new, fid_new - costNew_evals+=1 - b = {xD:x[:(4*n)],xG:x[(4*n):(4*n+4*n)]} # ,xR:0 - qb = q.bind_parameters(b) - job = execute(qb,sv_sim) - r = job.result() - sv = np.asarray(r.get_statevector()).tolist() - nR = 1e-8*np.linalg.norm(np.array(x)) - result = 1-np.abs(sv[0])**2 +nR - if verbose: print(np.array([result, - real_true(n,x,prt_new), - fake_true(n,x,pft_new), - fidelityRG(n,x,fid_new)]),end="\n") - return result - - - - prt_old = [] - pft_old = [] - fid_old = [] - disc_cost_evals = 0 - gen_cost_evals = 0 - - - def disc_cost(n,x): - global disc_cost_evals - global prt_old, pft_old - disc_cost_evals += 1 - q = fake_true(n,x,pft_old) - p = real_true(n,x,prt_old) - d = np.abs(p-q) - dp = np.abs(p) - nR = 1e-8*np.linalg.norm(np.array(x[:(4*n)])) - result = np.abs(1-d) - dp +nR # - np.log(d) - return result - - def gen_cost(n,x): - global gen_cost_evals - global fid_old - gen_cost_evals += 1 - nG = 1e-8*np.linalg.norm(np.array(x[(4*n):(4*n+4*n)])) - d = 1-fidelityRG(n,x,fid_old)+ nG - result = np.log(d) - return result - - """============================================ - Learning process according to the QGAN approach - ===============================================""" - - prt_iter_old = [] - pft_iter_old = [] - fid_iter_old = [] - gen_cost_evals = 0 - disc_cost_evals = 0 - - def learnOld(n,itr,seed,verbose=True): - np.random.seed(seed) - x0 = np.random.rand((4*n+4*n)) - xD = x0[:(4*n)] - xG = x0[(4*n):(4*n+4*n)] - global prt_iter_old, pft_iter_old, fid_iter_old - tprr = real_true(n,x0.tolist(),prt_iter_old) - tpfr = fake_true(n,x0.tolist(),pft_iter_old) - tfid = fidelityRG(n,x0.tolist(),fid_iter_old) - if verbose: print("start: \t",np.array([tprr,tpfr,tfid]),end="\n") - for l in range(itr): - print("QGAN iteration:\t" + str(l)) - for m in range(1): - solD = minimize(lambda xD: disc_cost(n,xD.tolist() + xG.tolist()), - xD, - method = alg, - options = optD) - xD = solD.x - tprr = real_true(n,xD.tolist() + xG.tolist(),prt_iter_old) - tpfr = fake_true(n,xD.tolist() + xG.tolist(),pft_iter_old) - if verbose: print("D: ",np.array([l, m, tprr,tpfr,tfid]),end="\n") - for m in range(1): - solG = minimize(lambda xG: gen_cost(n,xD.tolist() + xG.tolist()), - xG, - method = alg, - options = optG) - xG = solG.x - tfid = fidelityRG(n,xD.tolist() + xG.tolist(),fid_iter_old) - if verbose: print("G: ",np.array([l, m, tprr,tpfr,tfid]),end="\n") - - print("============= COST FUNCTION EVALUATIONS ============") - print(np.array([gen_cost_evals,disc_cost_evals])) - return - - """============================================= - Learning process according to the SQGEN approach - ================================================""" - - itrNew = 20 - itrOld = 20 - - - opt = {'maxiter': 1, - 'disp':False, - 'eps':1e-6, - 'finite_diff_rel_step':1e-8} - - optD = {'maxiter': 1, - 'disp':False, - 'eps':1e-6, - 'finite_diff_rel_step':1e-8} - - optG = {'maxiter': 1, - 'disp':False, - 'eps':1e-6, - 'finite_diff_rel_step':1e-8} - - alg = 'BFGS' - - - import time - - def save_time(n, t, dq,dqR,dqG,dqRG,a,b,c): - # creating a file - global seed - fileObject = open("log_times_seed" + str(seed)+"_paperALT.txt", "a") - - # writing into the file - fileObject.write(str(n) + "\t" + str(t) + "\t" + str(dq) + "\t" + str(dqR) + "\t" - + str(dqG) + "\t" + str(dqRG) + "\t") - fileObject.write(str(a) + "\t" + str(b) + "\t" + str(c) + "\n") - fileObject.flush() - fileObject.close() - - - for n in [5]: # number of qubits - # https://qiskit.org/documentation/stubs/qiskit.circuit.library.MCMT.html#qiskit.circuit.library.MCMT - - print("GHZ:\t" + str(n)) - - xG = ParameterVector('xG',4*n) - xD = ParameterVector('xD',4*n) - xR = Parameter('xR') - D = Discriminator(n,xD) - Dt = Discriminator(n,xD,testing=True) - G = GeneratorG(n,xG) - R = GeneratorR(n,xR) - Rdg = GeneratorRdg(n,xR) - - qr = QuantumRegister(n+1, 'q') - cr = ClassicalRegister(n+1, 'c') - qc = QuantumCircuit(qr,cr) - qc.append(R,[qr[i] for i in range(n)]) - qc.append(D,[qr[i] for i in range(n+1)]) - qc.x(qr[n]) - qc.append(D.inverse(),[qr[i] for i in range(n+1)]) - qc.append(G.inverse(),[qr[i] for i in range(n)]) - q = transpile(qc,backend) - dq= q.depth() - - qrR = QuantumRegister(n+1,'q') - crR = ClassicalRegister(n+1, 'c') - qcR = QuantumCircuit(qrR,crR) - qcR.append(R,[qrR[i] for i in range(n)]) - qcR.append(Dt,[qrR[i] for i in range(n+1)]) - qcR.append(Rdg,[qrR[i] for i in range(n)]) - qR = transpile(qcR,backend) - dqR = qR.depth() - - qrG = QuantumRegister(n+1,'q') - crG = ClassicalRegister(n+1,'c') - qcG = QuantumCircuit(qrG,crG) - qcG.append(G,[qrG[i] for i in range(n)]) - qcG.append(Dt,[qrG[i] for i in range(n+1)]) - qcG.append(G.inverse(),[qrG[i] for i in range(n)]) - qG = transpile(qcG,backend) - dqG = qG.depth() - - - qrRG = QuantumRegister(n,'q') - crRG = ClassicalRegister(n,'c') - qcRG = QuantumCircuit(qrRG,crRG) - qcRG.append(R,[qrRG[i] for i in range(n)]) - qcRG.append(G.inverse(),[qrRG[i] for i in range(n)]) - qRG = transpile(qcRG,backend) - dqRG = qRG.depth() - - """========================== - Saving SQGEN results to files - =============================""" - - if True: - start_time = time.time() - - np.random.seed(seed) - x0 = np.random.rand((4*n+4*n)) - - prt_iter_new = [] - pft_iter_new = [] - fid_iter_new = [] - cost_new = [] - for m in range(itrNew): - print("SQGEN iteration:\t" + str(m)) - fake_true(n,x0,pft_iter_new) - real_true(n,x0,prt_iter_new) - fidelityRG(n,x0,fid_iter_new) - cost_new.append(costNew(n,x0)) - sol = minimize(lambda x: costNew(n,x,False),x0,method = alg, options = opt) - x0 = sol.x - - fake_true(n,x0,pft_iter_new) - real_true(n,x0,prt_iter_new) - fidelityRG(n,x0,fid_iter_new) - t=(time.time() - start_time) - print("NEW --- %s seconds ---" % t) - save_time(n, t, dq,dqR,dqG,dqRG,0,0,costNew_evals) - - print(costNew_evals) - - np.save("prt_new_seed_" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",prt_new) - np.save("pft_new_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",pft_new) - np.save("fid_new_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",fid_new) - np.save("prt_iter_new_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",prt_iter_new) - np.save("pft_iter_new_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",pft_iter_new) - np.save("fid_iter_new_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",fid_iter_new) - - costNew_evals = 0 - prt_new = [] - pft_new = [] - fid_new = [] - prt_iter_new = [] - pft_iter_new = [] - fid_iter_new = [] - cost_new = [] - - """========================== - Saving QGAN results to files - =============================""" - start_time = time.time() - learnOld(n,itrOld,seed,False) - t=(time.time() - start_time) - print("OLD --- %s seconds ---" % t) - save_time(n, t, dq,dqR,dqG,dqRG,len(prt_old), - len(pft_old),len(fid_old)) - - - np.save("prt_old_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",prt_old) - np.save("pft_old_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",pft_old) - np.save("fid_old_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",fid_old) - np.save("prt_iter_old_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",prt_iter_old) - np.save("pft_iter_old_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",pft_iter_old) - np.save("fid_iter_old_seed" + str(seed) + "_n_" + str(n) + "disc1ALT.npy",fid_iter_old) - - prt_old = [] - pft_old = [] - fid_old = [] - prt_iter_old= [] - pft_iter_old = [] - fid_iter_old = [] - gen_cost_evals = 0 - disc_cost_evals = 0 +""" +Train SQGEN and QGAN circuits on a statevector simulator. + +Paper: "Synergic quantum generative machine learning" (arXiv:2112.13255v2) +""" + +import time + +import numpy as np +from scipy.optimize import minimize + +from qiskit.circuit import ParameterVector +from qiskit_aer import AerSimulator + +from sqgen import ( + build_circuits, generator_num_params, + real_true_sv, fake_true_sv, fidelity_rg_sv, + cost_new_sv, disc_cost_sv, gen_cost_sv, +) + +# ── Configuration ────────────────────────────────────────────────────────── + +SEED = 103 +N_QUBITS = 5 +ITR_NEW = 20 +ITR_OLD = 20 + +# Ansatz: 'mcmt' (original), 'hardware_efficient', 'strongly_entangling', 'circular' +ANSATZ = 'mcmt' +N_LAYERS = 2 # number of variational layers (ignored for 'mcmt') + +ALG = 'BFGS' +OPT = {'maxiter': 1, 'disp': False, 'eps': 1e-6, 'finite_diff_rel_step': 1e-8} +OPT_D = {'maxiter': 1, 'disp': False, 'eps': 1e-6, 'finite_diff_rel_step': 1e-8} +OPT_G = {'maxiter': 1, 'disp': False, 'eps': 1e-6, 'finite_diff_rel_step': 1e-8} + +backend = AerSimulator(method='statevector') + + +# ── Helpers ──────────────────────────────────────────────────────────────── + +def save_time(n, t, dq, dqR, dqG, dqRG, a, b, c, seed): + with open(f"log_times_seed{seed}_paperALT.txt", "a") as f: + f.write(f"{n}\t{t}\t{dq}\t{dqR}\t{dqG}\t{dqRG}\t{a}\t{b}\t{c}\n") + + +def learn_old(n, itr, seed, circuits, verbose=True): + """QGAN alternating training loop.""" + n_g = circuits['n_g'] + np.random.seed(seed) + x0 = np.random.rand(2 * n_g) + xD_val = x0[:n_g] + xG_val = x0[n_g:2 * n_g] + + prt_iter, pft_iter, fid_iter = [], [], [] + disc_counter, gen_counter = [0], [0] + + tprr = real_true_sv(n, x0.tolist(), circuits, backend, prt_iter) + tpfr = fake_true_sv(n, x0.tolist(), circuits, backend, pft_iter) + tfid = fidelity_rg_sv(n, x0.tolist(), circuits, backend, fid_iter) + if verbose: + print("start:\t", np.array([tprr, tpfr, tfid])) + + prt_all, pft_all, fid_all = [], [], [] + + for l in range(itr): + print(f"QGAN iteration:\t{l}") + solD = minimize( + lambda xd: disc_cost_sv( + n, xd.tolist() + xG_val.tolist(), circuits, backend, + disc_counter, prt_all, pft_all), + xD_val, method=ALG, options=OPT_D) + xD_val = solD.x + tprr = real_true_sv(n, xD_val.tolist() + xG_val.tolist(), + circuits, backend, prt_iter) + tpfr = fake_true_sv(n, xD_val.tolist() + xG_val.tolist(), + circuits, backend, pft_iter) + if verbose: + print("D:", np.array([l, 0, tprr, tpfr, tfid])) + + solG = minimize( + lambda xg: gen_cost_sv( + n, xD_val.tolist() + xg.tolist(), circuits, backend, + gen_counter, fid_all), + xG_val, method=ALG, options=OPT_G) + xG_val = solG.x + tfid = fidelity_rg_sv(n, xD_val.tolist() + xG_val.tolist(), + circuits, backend, fid_iter) + if verbose: + print("G:", np.array([l, 0, tprr, tpfr, tfid])) + + print("============= COST FUNCTION EVALUATIONS ============") + print(np.array([gen_counter[0], disc_counter[0]])) + return prt_all, pft_all, fid_all, prt_iter, pft_iter, fid_iter + + +# ── Main ─────────────────────────────────────────────────────────────────── + +for seed in [SEED]: + for n in [N_QUBITS]: + print(f"GHZ:\t{n}") + + n_g = generator_num_params(n, ANSATZ, N_LAYERS) + xG = ParameterVector('xG', n_g) + xD = ParameterVector('xD', n_g) + circuits = build_circuits(n, backend, xD, xG, + ansatz=ANSATZ, n_layers=N_LAYERS) + dq, dqR = circuits['dq'], circuits['dqR'] + dqG, dqRG = circuits['dqG'], circuits['dqRG'] + print(f" ansatz={ANSATZ}, n_layers={N_LAYERS}, " + f"params/circuit={n_g}, total={2*n_g}") + print(f" depths: q={dq}, qR={dqR}, qG={dqG}, qRG={dqRG}") + + # ── SQGEN ────────────────────────────────────────────────────── + start_time = time.time() + np.random.seed(seed) + x0 = np.random.rand(2 * n_g) + + prt_new, pft_new, fid_new = [], [], [] + prt_iter_new, pft_iter_new, fid_iter_new = [], [], [] + cost_new_list = [] + counter_new = [0] + + for m in range(ITR_NEW): + print(f"SQGEN iteration:\t{m}") + fake_true_sv(n, x0, circuits, backend, pft_iter_new) + real_true_sv(n, x0, circuits, backend, prt_iter_new) + fidelity_rg_sv(n, x0, circuits, backend, fid_iter_new) + cost_new_list.append( + cost_new_sv(n, x0, circuits, backend, counter_new)) + sol = minimize( + lambda x: cost_new_sv( + n, x, circuits, backend, counter_new, False, + prt_new, pft_new, fid_new), + x0, method=ALG, options=OPT) + x0 = sol.x + + fake_true_sv(n, x0, circuits, backend, pft_iter_new) + real_true_sv(n, x0, circuits, backend, prt_iter_new) + fidelity_rg_sv(n, x0, circuits, backend, fid_iter_new) + + t_new = time.time() - start_time + print(f"NEW --- {t_new:.1f} seconds ---") + save_time(n, t_new, dq, dqR, dqG, dqRG, 0, 0, counter_new[0], seed) + print(counter_new[0]) + + sfx = f"seed{seed}_n_{n}disc1ALT" + np.save(f"prt_new_{sfx}.npy", prt_new) + np.save(f"pft_new_{sfx}.npy", pft_new) + np.save(f"fid_new_{sfx}.npy", fid_new) + np.save(f"prt_iter_new_{sfx}.npy", prt_iter_new) + np.save(f"pft_iter_new_{sfx}.npy", pft_iter_new) + np.save(f"fid_iter_new_{sfx}.npy", fid_iter_new) + + # ── QGAN ─────────────────────────────────────────────────────── + start_time = time.time() + (prt_old, pft_old, fid_old, + prt_iter_old, pft_iter_old, fid_iter_old) = learn_old( + n, ITR_OLD, seed, circuits, verbose=False) + + t_old = time.time() - start_time + print(f"OLD --- {t_old:.1f} seconds ---") + save_time(n, t_old, dq, dqR, dqG, dqRG, + len(prt_old), len(pft_old), len(fid_old), seed) + + np.save(f"prt_old_{sfx}.npy", prt_old) + np.save(f"pft_old_{sfx}.npy", pft_old) + np.save(f"fid_old_{sfx}.npy", fid_old) + np.save(f"prt_iter_old_{sfx}.npy", prt_iter_old) + np.save(f"pft_iter_old_{sfx}.npy", pft_iter_old) + np.save(f"fid_iter_old_{sfx}.npy", fid_iter_old) diff --git a/readme.txt b/readme.txt deleted file mode 100644 index dc55ef4..0000000 --- a/readme.txt +++ /dev/null @@ -1,11 +0,0 @@ -This repository contains Qiskit implementations of the quantum circuits suggested in the paper "Synergic quantum generative machine learning" (arXiv:2112.13255v2). - -As presented in the article, an example of training the network is to teach both recognition and generation of a n-qubit GHZ entangled state. - -Jupyter notebook 'singlequbit_n1.ipynb' is responsible for training the SQGEN network for single-qubit input on a real programmable quantum computer. - -Python code in 'multiqubit_n5seed103.py' performs calculations on quantum simulators for SQGEN and QGAN. - -Python code in 'figure_n5seed103.py' is used to plot the outcomes of 'multiqubit_n5seed103.py'. The particular seed of a random number generator is set in line 14 ("seed in [103]"). The number of qubits n is set in line 299 ("n in [5]"). These numbers were varied to generate Fig.6 form the article (see also lines 18 and 28 in "figure_n5seed103.py"). - -The programs responsible for training the network (both for the single qubit case and the multi-qubit case) include definitions of the real state generator circuits and the trained (variational) generator and discriminator. The code includes functions responsible for determining the probabilities of determining the output state as real/fake, and the fidelity of the obtained state. The programs also include definitions of the cost functions of both the generator, discriminator, and the total cost function minimized for training in the SQGEN approach. diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..375825a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +qiskit>=1.0,<2.0 +qiskit-aer>=0.14 +qiskit-ibm-runtime>=0.20 +scipy>=1.10 +numpy>=1.24 +numba>=0.58 +matplotlib>=3.7 diff --git a/singlequbit_n1.ipynb b/singlequbit_n1.ipynb index 0ab0f9a..f4b5560 100644 --- a/singlequbit_n1.ipynb +++ b/singlequbit_n1.ipynb @@ -4,25 +4,19 @@ "cell_type": "markdown", "id": "6d8109f8", "metadata": {}, - "source": [ - "# Synergic quantum generative machine learning- single qubit case" - ] + "source": "# Synergic quantum generative machine learning — single qubit case" }, { "cell_type": "markdown", "id": "ea4faedd", "metadata": {}, - "source": [ - "A program that trains circuits with a given number of qubits on a quantum computer for SQGEN approache suggested in the paper: \"Synergic quantum generative machine learning\" (arXiv:2112.13255v2)" - ] + "source": "A program that trains circuits with a given number of qubits on a quantum computer for the SQGEN approach suggested in the paper: \"Synergic quantum generative machine learning\" (arXiv:2112.13255v2)" }, { "cell_type": "markdown", "id": "ae2a5707", "metadata": {}, - "source": [ - "Import IBMQ account and necessary libraries" - ] + "source": "Set up backend (fake or real IBM device)" }, { "cell_type": "code", @@ -30,13 +24,7 @@ "id": "48d69b31", "metadata": {}, "outputs": [], - "source": [ - "#MY_API_TOKEN=\"\"\n", - "#from qiskit import IBMQ\n", - "#IBMQ.save_account(MY_API_TOKEN, overwrite = True)\n", - "#IBMQ.load_account()\n", - "#backend = provider.get_backend('ibmq_montreal')" - ] + "source": "# To use a real backend, uncomment and configure:\n# from qiskit_ibm_runtime import QiskitRuntimeService\n# service = QiskitRuntimeService(channel=\"ibm_quantum\", token=\"MY_API_TOKEN\")\n# backend = service.least_busy(simulator=False, min_num_qubits=2)" }, { "cell_type": "code", @@ -44,13 +32,7 @@ "id": "29e7bf0f", "metadata": {}, "outputs": [], - "source": [ - "from qiskit import IBMQ\n", - "IBMQ.load_account()\n", - "provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')\n", - "from qiskit.test.mock.backends import FakeMontreal\n", - "backend = FakeMontreal()\n" - ] + "source": "from qiskit_ibm_runtime.fake_provider import FakeMontreal\n\nbackend = FakeMontreal()" }, { "cell_type": "code", @@ -58,36 +40,13 @@ "id": "085f0f4b", "metadata": {}, "outputs": [], - "source": [ - "from numpy import pi\n", - "import numpy as np\n", - "from qiskit import QuantumCircuit, execute, transpile\n", - "from qiskit.circuit import Parameter, ParameterVector\n", - "from qiskit.extensions.unitary import UnitaryGate\n", - "from qiskit import QuantumRegister, ClassicalRegister\n", - "from qiskit import Aer, assemble, IBMQ\n", - "\n", - "# Get the noise model\n", - "from qiskit.providers.aer.noise import NoiseModel\n", - "noise_model = NoiseModel.from_backend(backend)\n", - "\n", - "# Get coupling map from backend\n", - "coupling_map = backend.configuration().coupling_map\n", - "from qiskit.circuit.library import MCMT, RZGate, RYGate\n", - "\n", - "# Get basis gates from noise model\n", - "basis_gates = noise_model.basis_gates\n", - "from qiskit.extensions import Initialize\n", - "from numpy import sqrt " - ] + "source": "import numpy as np\nfrom numpy import pi\nfrom scipy.optimize import minimize\n\nfrom qiskit.circuit import ParameterVector\n\nfrom sqgen import (\n build_circuits, generator_num_params,\n real_true_shots, fake_true_shots, fidelity_rg_shots,\n cost_new_shots,\n)" }, { "cell_type": "markdown", "id": "18acbf4b", "metadata": {}, - "source": [ - "Circuit corresponding to the trained generator" - ] + "source": "Configuration" }, { "cell_type": "code", @@ -95,46 +54,13 @@ "id": "d5d5ed47", "metadata": {}, "outputs": [], - "source": [ - "def GeneratorG(n,xG):\n", - " x = ParameterVector('x',4*n)\n", - " qr = QuantumRegister(n)\n", - " qc = QuantumCircuit(qr, name='G')\n", - " for i in range(n):\n", - " if i < n-1:\n", - " gate = MCMT(RZGate(2*pi*x[i]),n-i-1, 1)\n", - " qc.append(gate,[m for m in range(n-i)])\n", - " else:\n", - " qc.rz(2*pi*x[i],0)\n", - " for i in range(n):\n", - " if i < n-1:\n", - " gate = MCMT(RYGate(2*pi*x[i+n]),n-i-1, 1)\n", - " qc.append(gate,[m for m in range(n-i)])\n", - " else:\n", - " qc.ry(2*pi*x[i+n],0)\n", - " for i in reversed(range(n)):\n", - " if i < n-1:\n", - " gate = MCMT(RYGate(2*pi*x[i+3*n]),n-i-1, 1)\n", - " qc.append(gate,[m for m in range(n-i)])\n", - " else:\n", - " qc.ry(2*pi*x[i+3*n],0)\n", - " for i in reversed(range(n)):\n", - " if i < n-1:\n", - " gate = MCMT(RZGate(2*pi*x[i+2*n]),n-i-1, 1)\n", - " qc.append(gate,[m for m in range(n-i)])\n", - " else:\n", - " qc.rz(2*pi*x[i+2*n],0)\n", - " result = qc.to_instruction({x:xG})\n", - " return result\n" - ] + "source": "n = 1 # number of qubits\nmyshots = 32000 # number of shots\nseed = 42\n\n# Ansatz: 'mcmt' (original), 'hardware_efficient', 'strongly_entangling', 'circular'\nANSATZ = 'mcmt'\nN_LAYERS = 2 # number of variational layers (ignored for 'mcmt')" }, { "cell_type": "markdown", "id": "c9e994eb", "metadata": {}, - "source": [ - "Circuit corresponding to the real data generator and its conjugate transposition" - ] + "source": "Build and transpile all training circuits" }, { "cell_type": "code", @@ -142,311 +68,13 @@ "id": "5dc026ed", "metadata": {}, "outputs": [], - "source": [ - "def GeneratorR(n,xR):\n", - " x =Parameter('x')\n", - " qr = QuantumRegister(n)\n", - " qc = QuantumCircuit(qr, name='R')\n", - " v = [1/sqrt(2)]+(2**n-2)*[0] + [1/sqrt(2)] #GHZ state\n", - " qc.initialize(v,[m for m in range(n)])\n", - " result = qc.to_instruction()\n", - " return result\n", - " \n", - "def GeneratorRdg(n,xR):\n", - " x =Parameter('x')\n", - " qr = QuantumRegister(n)\n", - " qc = QuantumCircuit(qr, name='Rdg')\n", - " v = [1/sqrt(2)]+(2**n-2)*[0] + [1/sqrt(2)] #GHZ state \n", - " qi=Initialize(v).gates_to_uncompute()\n", - " qi = qi.to_instruction()\n", - " qc.append(qi,qr)\n", - " #result = qc.to_instruction({x:xR})\n", - " result = qc.to_instruction()\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "id": "4577ac27", - "metadata": {}, - "source": [ - "Circuit corresponding to the trained discriminator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4734a2d0", - "metadata": {}, - "outputs": [], - "source": [ - "def Discriminator(n,xD,testing=False):\n", - " qr = QuantumRegister(n+1)\n", - " qc = QuantumCircuit(qr, name='D')\n", - " \n", - " xD1=ParameterVector('xD1',4*n)\n", - " sub_inst= GeneratorG(n,xD1)\n", - " qc.append(sub_inst, [qr[i] for i in range(n)])\n", - " \n", - " shift = pi/2\n", - " if testing:\n", - " qc.cry(shift,qr[n-1],qr[n])\n", - " else:\n", - " qc.cry(shift/2,qr[n-1],qr[n])\n", - " \n", - " qc.append(sub_inst.inverse(), [qr[i] for i in range(n)])\n", - "\n", - " result = qc.to_instruction({xD1:xD[0:4*n]\n", - " })\n", - " \n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0324a360", - "metadata": {}, - "outputs": [], - "source": [ - "n = 1 #number of qubits\n", - "myshots = 32000 #number of shots" - ] - }, - { - "cell_type": "markdown", - "id": "24473c74", - "metadata": {}, - "source": [ - "Circuits responsible for the learning process" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32af49e3", - "metadata": {}, - "outputs": [], - "source": [ - "xG = ParameterVector('xG',4*n)\n", - "xD = ParameterVector('xD',4*n) \n", - "xR = Parameter('xR')\n", - "D = Discriminator(n,xD)\n", - "Dt = Discriminator(n,xD,testing=True)\n", - "G = GeneratorG(n,xG)\n", - "R = GeneratorR(n,xR)\n", - "Rdg = GeneratorRdg(n,xR)\n", - "\n", - "qr = QuantumRegister(n+1, 'q')\n", - "cr = ClassicalRegister(n+1, 'c')\n", - "qc = QuantumCircuit(qr,cr)\n", - "qc.append(R,[qr[i] for i in range(n)])\n", - "qc.append(D,[qr[i] for i in range(n+1)])\n", - "qc.x(qr[n])\n", - "qc.append(D.inverse(),[qr[i] for i in range(n+1)])\n", - "qc.append(G.inverse(),[qr[i] for i in range(n)])\n", - "qc.measure(qr, cr)\n", - "q = transpile(qc,backend)\n", - "\n", - "\n", - "qrR = QuantumRegister(n+1,'q')\n", - "crR = ClassicalRegister(n+1, 'c')\n", - "qcR = QuantumCircuit(qrR,crR)\n", - "qcR.append(R,[qrR[i] for i in range(n)])\n", - "qcR.append(Dt,[qrR[i] for i in range(n+1)])\n", - "qcR.append(Rdg,[qrR[i] for i in range(n)])\n", - "qcR.measure(qrR, crR)\n", - "qR = transpile(qcR,backend)\n", - "\n", - "qrG = QuantumRegister(n+1,'q')\n", - "crG = ClassicalRegister(n+1,'c')\n", - "qcG = QuantumCircuit(qrG,crG)\n", - "qcG.append(G,[qrG[i] for i in range(n)])\n", - "qcG.append(Dt,[qrG[i] for i in range(n+1)])\n", - "qcG.append(G.inverse(),[qrG[i] for i in range(n)])\n", - "qcG.measure(qrG, crG)\n", - "qG = transpile(qcG,backend)\n", - "\n", - "\n", - "qrRG = QuantumRegister(n,'q')\n", - "crRG = ClassicalRegister(n,'c')\n", - "qcRG = QuantumCircuit(qrRG,crRG)\n", - "qcRG.append(R,[qrRG[i] for i in range(n)])\n", - "qcRG.append(G.inverse(),[qrRG[i] for i in range(n)])\n", - "qcRG.measure(qrRG, crRG)\n", - "qRG = transpile(qcRG,backend)" - ] - }, - { - "cell_type": "markdown", - "id": "e3aef20e", - "metadata": {}, - "source": [ - "Loops used to calculate the probabilities of correct recognition of states by the discriminator, and the fidelity of the obtained states" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a23a743d", - "metadata": {}, - "outputs": [], - "source": [ - "def real_true(n,x,prt=[]):\n", - " global qR,xD\n", - " global myshots, coupling_map, basis_gates, backend \n", - " \n", - " b = {xD:x[:4*n]} # ,xR:0\n", - " \n", - " \n", - " qRb = qR.bind_parameters(b)\n", - " job = execute(qRb, \n", - " backend,\n", - " shots=myshots,\n", - " coupling_map=coupling_map,\n", - " basis_gates=basis_gates)\n", - " \n", - " result = job.result()\n", - " counts = result.get_counts()\n", - " c00 = 0\n", - " c10 = 0\n", - " c01 = 0\n", - " c11 = 0\n", - " if ('00' in counts.keys()):\n", - " c00 = float(counts['00'])\n", - " if ('10' in counts.keys()):\n", - " c10 = float(counts['10'])\n", - " if ('01' in counts.keys()):\n", - " c01 = float(counts['01'])\n", - " if ('11' in counts.keys()):\n", - " c11 = float(counts['11'])\n", - " \n", - " Sc = float(myshots)\n", - " result = c00 /Sc \n", - " prt.append(result)\n", - " return 1- result \n", - "\n", - "def fake_true(n,x,pft=[]):\n", - " global qG,xD\n", - " global myshots, coupling_map, basis_gates, backend\n", - " b = {xD:x[:(4*n)],xG:x[(4*n):(4*n+4*n)]}\n", - " qGb = qG.bind_parameters(b)\n", - " \n", - " job = execute(qGb, \n", - " backend,\n", - " shots=myshots,\n", - " coupling_map=coupling_map,\n", - " basis_gates=basis_gates)\n", - " \n", - " result = job.result()\n", - " counts = result.get_counts()\n", - " c00 = 0\n", - " c10 = 0\n", - " c01 = 0\n", - " c11 = 0\n", - " if ('00' in counts.keys()):\n", - " c00 = float(counts['00'])\n", - " if ('10' in counts.keys()):\n", - " c10 = float(counts['10'])\n", - " if ('01' in counts.keys()):\n", - " c01 = float(counts['01'])\n", - " if ('11' in counts.keys()):\n", - " c11 = float(counts['11'])\n", - " Sc = float(c00 + c01 + c10 + c11)\n", - " result = c00/Sc \n", - " pft.append(result)\n", - " return 1 - result\n", - " \n", - "def fidelityRG(n,x,fid=[]):\n", - " global qRG,xG\n", - " global myshots, coupling_map, basis_gates, backend\n", - " b = {xG:x[(4*n):(4*n+4*n)]} # xR:0,\n", - " \n", - " qRGb = qRG.bind_parameters(b)\n", - " \n", - " \n", - " job = execute(qRGb,\n", - " backend,\n", - " shots=myshots,\n", - " coupling_map=coupling_map,\n", - " basis_gates=basis_gates)\n", - " \n", - " \n", - " result = job.result()\n", - " counts = result.get_counts()\n", - " c0 = 0\n", - " c1 = 0\n", - " if ('0' in counts.keys()):\n", - " c0 = float(counts['0'])\n", - " if ('1' in counts.keys()):\n", - " c1 = float(counts['1'])\n", - " Sc = float(myshots)\n", - " result = c0/Sc \n", - " fid.append(result)\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "id": "e65ee002", - "metadata": {}, - "source": [ - "Loops to calculating the total cost function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5135f311", - "metadata": {}, - "outputs": [], - "source": [ - "def costNew(n,x,verbose=False):\n", - " global q,qc,xG,xD,costNew_evals, costNew_iter\n", - " global prt_new, pft_new, fid_new\n", - " global myshots, coupling_map, basis_gates, backend\n", - " costNew_evals+=1\n", - " \n", - " b = {xD:x[:(4*n)],xG:x[(4*n):(4*n+4*n)]} # ,xR:0\n", - " \n", - " qb = q.bind_parameters(b)\n", - " \n", - " job = execute(qb,\n", - " backend,\n", - " shots=myshots,\n", - " coupling_map=coupling_map,\n", - " basis_gates=basis_gates)\n", - " \n", - " result = job.result()\n", - " counts = result.get_counts()\n", - " c00 = 0\n", - " c10 = 0\n", - " c01 = 0\n", - " c11 = 0\n", - " if ('00' in counts.keys()):\n", - " c00 = float(counts['00'])\n", - " if ('10' in counts.keys()):\n", - " c10 = float(counts['10'])\n", - " if ('01' in counts.keys()):\n", - " c01 = float(counts['01'])\n", - " if ('11' in counts.keys()):\n", - " c11 = float(counts['11'])\n", - " Sc = float(myshots)\n", - " result = 1-c00 /Sc \n", - " if verbose: print(np.array([result,\n", - " real_true(n,x,prt_new),\n", - " fake_true(n,x,pft_new),\n", - " fidelityRG(n,x,fid_new)]),end=\"\\r\")\n", - " return result " - ] + "source": "n_g = generator_num_params(n, ANSATZ, N_LAYERS)\nxG = ParameterVector('xG', n_g)\nxD = ParameterVector('xD', n_g)\n\ncircuits = build_circuits(n, backend, xD, xG, measure=True,\n ansatz=ANSATZ, n_layers=N_LAYERS)\n\nprint(f\"Ansatz: {ANSATZ}, layers: {N_LAYERS}, params/circuit: {n_g}, total: {2*n_g}\")\nprint(f\"Circuit depths: q={circuits['dq']}, qR={circuits['dqR']}, \"\n f\"qG={circuits['dqG']}, qRG={circuits['dqRG']}\")" }, { "cell_type": "markdown", "id": "6e3a605e", "metadata": {}, - "source": [ - "Learning process according to the SQGEN approach" - ] + "source": "SQGEN training loop" }, { "cell_type": "code", @@ -454,45 +82,7 @@ "id": "eda800bb", "metadata": {}, "outputs": [], - "source": [ - "itrNew = 15\n", - "seed = 42\n", - "from scipy.optimize import minimize\n", - "\n", - "opt = {'maxiter': 6,\n", - " 'disp':True,\n", - " 'eps':1e-8,\n", - " 'finite_diff_rel_step':1e-8} \n", - "alg = 'BFGS'#'CG'\n", - "alg = 'Nelder-Mead'\n", - "\n", - "\n", - "np.random.seed(seed) \n", - "x0 = np.random.rand((4*n+4*n))\n", - "\n", - "costNew_evals = 0\n", - "prt_new = []\n", - "pft_new = []\n", - "fid_new = []\n", - "cost_new = []\n", - "prt_iter_new = []\n", - "pft_iter_new = []\n", - "fid_iter_new = []\n", - "costNew_iter = []\n", - "\n", - "for m in range(itrNew):\n", - " fake_true(n,x0,pft_iter_new) \n", - " real_true(n,x0,prt_iter_new) \n", - " fidelityRG(n,x0,fid_iter_new)\n", - " cost_new.append(costNew(n,x0))\n", - " sol = minimize(lambda x: costNew(n,x,True),x0,method = alg, options = opt) \n", - " x0 = sol.x\n", - "\n", - "fake_true(n,x0,pft_iter_new) \n", - "real_true(n,x0,prt_iter_new) \n", - "fidelityRG(n,x0,fid_iter_new)\n", - "print(costNew_evals) \n" - ] + "source": "itr_new = 15\nalg = 'Nelder-Mead'\nopt = {'maxiter': 6, 'disp': True, 'eps': 1e-8, 'finite_diff_rel_step': 1e-8}\n\nnp.random.seed(seed)\nx0 = np.random.rand(2 * n_g)\n\ncounter = [0]\nprt_new, pft_new, fid_new = [], [], []\ncost_new_list = []\nprt_iter_new, pft_iter_new, fid_iter_new = [], [], []\n\nfor m in range(itr_new):\n fake_true_shots(n, x0, circuits, backend, myshots, pft_iter_new)\n real_true_shots(n, x0, circuits, backend, myshots, prt_iter_new)\n fidelity_rg_shots(n, x0, circuits, backend, myshots, fid_iter_new)\n cost_new_list.append(\n cost_new_shots(n, x0, circuits, backend, myshots, counter))\n sol = minimize(\n lambda x: cost_new_shots(\n n, x, circuits, backend, myshots, counter, True,\n prt_new, pft_new, fid_new),\n x0, method=alg, options=opt)\n x0 = sol.x\n\nfake_true_shots(n, x0, circuits, backend, myshots, pft_iter_new)\nreal_true_shots(n, x0, circuits, backend, myshots, prt_iter_new)\nfidelity_rg_shots(n, x0, circuits, backend, myshots, fid_iter_new)\nprint(f\"Total cost evaluations: {counter[0]}\")" }, { "cell_type": "markdown", @@ -508,15 +98,7 @@ "id": "00ffa9d1", "metadata": {}, "outputs": [], - "source": [ - "np.save(\"prt_new.npy\",prt_new)\n", - "np.save(\"pft_new.npy\",pft_new)\n", - "np.save(\"fid_new.npy\",fid_new)\n", - "np.save(\"prt_iter_new.npy\",prt_iter_new)\n", - "np.save(\"pft_iter_new.npy\",pft_iter_new)\n", - "np.save(\"fid_iter_new.npy\",fid_iter_new)\n", - "np.save(\"cost_new.npy\",cost_new)" - ] + "source": "np.save(\"prt_new.npy\", prt_new)\nnp.save(\"pft_new.npy\", pft_new)\nnp.save(\"fid_new.npy\", fid_new)\nnp.save(\"prt_iter_new.npy\", prt_iter_new)\nnp.save(\"pft_iter_new.npy\", pft_iter_new)\nnp.save(\"fid_iter_new.npy\", fid_iter_new)\nnp.save(\"cost_new.npy\", cost_new_list)" } ], "metadata": { @@ -547,4 +129,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/sqgen.py b/sqgen.py new file mode 100644 index 0000000..8e4fe4e --- /dev/null +++ b/sqgen.py @@ -0,0 +1,455 @@ +""" +Shared circuits and cost functions for SQGEN / QGAN training. + +Paper: "Synergic quantum generative machine learning" (arXiv:2112.13255v2) +""" + +import numpy as np +from numpy import pi, sqrt + +from qiskit import QuantumCircuit, QuantumRegister, transpile +from qiskit.circuit import Parameter, ParameterVector +from qiskit.circuit.library import MCMT, RZGate, RYGate, Initialize +from qiskit.quantum_info import Statevector + + +# --------------------------------------------------------------------------- +# Ansatz registry +# --------------------------------------------------------------------------- + +ANSATZE = ('mcmt', 'hardware_efficient', 'strongly_entangling', 'circular') + + +def generator_num_params(n, ansatz='mcmt', n_layers=1): + """Return the number of variational parameters for the chosen ansatz.""" + if ansatz == 'mcmt': + return 4 * n + elif ansatz == 'hardware_efficient': + return 2 * n * n_layers + elif ansatz == 'strongly_entangling': + return 3 * n * n_layers + elif ansatz == 'circular': + return 2 * n * n_layers + raise ValueError(f"Unknown ansatz '{ansatz}'. Choose from {ANSATZE}.") + + +# --------------------------------------------------------------------------- +# Ansatz builders (each returns a QuantumCircuit, not yet an instruction) +# --------------------------------------------------------------------------- + +def _ansatz_mcmt(n, x): + """Original MCMT ansatz from the paper — 4*n params.""" + qr = QuantumRegister(n) + qc = QuantumCircuit(qr, name='G') + + for i in range(n): + if i < n - 1: + gate = MCMT(RZGate(2 * pi * x[i]), n - i - 1, 1) + qc.append(gate, list(range(n - i))) + else: + qc.rz(2 * pi * x[i], 0) + + for i in range(n): + if i < n - 1: + gate = MCMT(RYGate(2 * pi * x[i + n]), n - i - 1, 1) + qc.append(gate, list(range(n - i))) + else: + qc.ry(2 * pi * x[i + n], 0) + + for i in reversed(range(n)): + if i < n - 1: + gate = MCMT(RYGate(2 * pi * x[i + 3 * n]), n - i - 1, 1) + qc.append(gate, list(range(n - i))) + else: + qc.ry(2 * pi * x[i + 3 * n], 0) + + for i in reversed(range(n)): + if i < n - 1: + gate = MCMT(RZGate(2 * pi * x[i + 2 * n]), n - i - 1, 1) + qc.append(gate, list(range(n - i))) + else: + qc.rz(2 * pi * x[i + 2 * n], 0) + + return qc + + +def _ansatz_hardware_efficient(n, x, n_layers): + """ + Hardware-efficient ansatz — 2*n*n_layers params. + + Each layer: RY(x) + RZ(x) on every qubit, then a linear CNOT ladder. + Friendly to nearest-neighbour topologies. + """ + qr = QuantumRegister(n) + qc = QuantumCircuit(qr, name='G') + p = 0 + for _ in range(n_layers): + for q in range(n): + qc.ry(2 * pi * x[p], q); p += 1 + qc.rz(2 * pi * x[p], q); p += 1 + for q in range(n - 1): + qc.cx(q, q + 1) + return qc + + +def _ansatz_strongly_entangling(n, x, n_layers): + """ + Strongly entangling layers — 3*n*n_layers params. + + Each layer: RX + RY + RZ on every qubit, then CNOT ring with + offset = layer mod (n-1) + 1 (varies entanglement range per layer). + """ + qr = QuantumRegister(n) + qc = QuantumCircuit(qr, name='G') + p = 0 + for layer in range(n_layers): + for q in range(n): + qc.rx(2 * pi * x[p], q); p += 1 + qc.ry(2 * pi * x[p], q); p += 1 + qc.rz(2 * pi * x[p], q); p += 1 + if n > 1: + offset = layer % (n - 1) + 1 + for q in range(n): + qc.cx(q, (q + offset) % n) + return qc + + +def _ansatz_circular(n, x, n_layers): + """ + Circular entanglement ansatz — 2*n*n_layers params. + + Each layer: RY + RZ on every qubit, then circular CNOT ring + (last qubit connects back to first). + """ + qr = QuantumRegister(n) + qc = QuantumCircuit(qr, name='G') + p = 0 + for _ in range(n_layers): + for q in range(n): + qc.ry(2 * pi * x[p], q); p += 1 + qc.rz(2 * pi * x[p], q); p += 1 + if n > 1: + for q in range(n): + qc.cx(q, (q + 1) % n) + return qc + + +# --------------------------------------------------------------------------- +# Generator / discriminator +# --------------------------------------------------------------------------- + +def generator_g(n, x_g, ansatz='mcmt', n_layers=1): + """Trained (variational) generator circuit on *n* qubits.""" + n_params = generator_num_params(n, ansatz, n_layers) + x = ParameterVector('x', n_params) + + if ansatz == 'mcmt': + qc = _ansatz_mcmt(n, x) + elif ansatz == 'hardware_efficient': + qc = _ansatz_hardware_efficient(n, x, n_layers) + elif ansatz == 'strongly_entangling': + qc = _ansatz_strongly_entangling(n, x, n_layers) + elif ansatz == 'circular': + qc = _ansatz_circular(n, x, n_layers) + else: + raise ValueError(f"Unknown ansatz '{ansatz}'. Choose from {ANSATZE}.") + + return qc.to_instruction({x: x_g}) + + +def ghz_statevector(n): + """Return the *n*-qubit GHZ state as a list of amplitudes.""" + return [1 / sqrt(2)] + (2**n - 2) * [0] + [1 / sqrt(2)] + + +def generator_r(n): + """Real-data generator: prepares the *n*-qubit GHZ state.""" + qr = QuantumRegister(n) + qc = QuantumCircuit(qr, name='R') + qc.initialize(ghz_statevector(n), list(range(n))) + return qc.to_instruction() + + +def generator_r_dagger(n): + """Conjugate transpose of the real-data generator.""" + qr = QuantumRegister(n) + qc = QuantumCircuit(qr, name='Rdg') + qi = Initialize(ghz_statevector(n)).gates_to_uncompute().to_instruction() + qc.append(qi, qr) + return qc.to_instruction() + + +def discriminator(n, x_d, testing=False, ansatz='mcmt', n_layers=1): + """Trained discriminator circuit on *n* + 1 qubits.""" + n_g = generator_num_params(n, ansatz, n_layers) + qr = QuantumRegister(n + 1) + qc = QuantumCircuit(qr, name='D') + + x_d1 = ParameterVector('xD1', n_g) + sub_inst = generator_g(n, x_d1, ansatz=ansatz, n_layers=n_layers) + qc.append(sub_inst, list(range(n))) + + shift = pi if testing else pi / 2 + gate = MCMT(RYGate(shift), n, 1) + qc.append(gate, list(range(n + 1))) + + qc.append(sub_inst.inverse(), list(range(n))) + return qc.to_instruction({x_d1: x_d[0:n_g]}) + + +# --------------------------------------------------------------------------- +# Statevector helpers (simulator path — no shots) +# --------------------------------------------------------------------------- + +def _run_sv(circuit, backend=None): + """Compute the statevector of a parameterised circuit (all params bound).""" + return np.asarray(Statevector(circuit)) + + +def real_true_sv(n, x, circuits, backend, prt=None): + """P(real recognised as real) on statevector simulator.""" + if prt is None: + prt = [] + n_g = circuits['n_g'] + qr_circ, x_d = circuits['qR'], circuits['xD'] + b = {x_d: x[:n_g]} + qr_bound = qr_circ.assign_parameters(b) + sv = _run_sv(qr_bound, backend) + p = np.abs(sv[0]) ** 2 + prt.append(p) + return 1 - p + + +def fake_true_sv(n, x, circuits, backend, pft=None): + """P(fake recognised as true) on statevector simulator.""" + if pft is None: + pft = [] + n_g = circuits['n_g'] + qg_circ, x_d, x_g = circuits['qG'], circuits['xD'], circuits['xG'] + b = {x_d: x[:n_g], x_g: x[n_g:2 * n_g]} + qg_bound = qg_circ.assign_parameters(b) + sv = _run_sv(qg_bound, backend) + p = np.abs(sv[0]) ** 2 + pft.append(p) + return 1 - p + + +def fidelity_rg_sv(n, x, circuits, backend, fid=None): + """Fidelity between real and generated states (statevector).""" + if fid is None: + fid = [] + n_g = circuits['n_g'] + qrg_circ, x_g = circuits['qRG'], circuits['xG'] + b = {x_g: x[n_g:2 * n_g]} + qrg_bound = qrg_circ.assign_parameters(b) + sv = _run_sv(qrg_bound, backend) + result = np.abs(sv[0]) ** 2 + fid.append(result) + return result + + +def cost_new_sv(n, x, circuits, backend, counter, verbose=False, + prt=None, pft=None, fid=None): + """Total SQGEN cost function (statevector simulator).""" + n_g = circuits['n_g'] + q_circ, x_d, x_g = circuits['q'], circuits['xD'], circuits['xG'] + counter[0] += 1 + b = {x_d: x[:n_g], x_g: x[n_g:2 * n_g]} + qb = q_circ.assign_parameters(b) + sv = _run_sv(qb, backend) + n_reg = 1e-8 * np.linalg.norm(x) + result = 1 - np.abs(sv[0]) ** 2 + n_reg + if verbose: + print(np.array([ + result, + real_true_sv(n, x, circuits, backend, prt), + fake_true_sv(n, x, circuits, backend, pft), + fidelity_rg_sv(n, x, circuits, backend, fid), + ])) + return result + + +def disc_cost_sv(n, x, circuits, backend, counter, prt=None, pft=None): + """Discriminator cost function (QGAN, statevector).""" + n_g = circuits['n_g'] + counter[0] += 1 + q = fake_true_sv(n, x, circuits, backend, pft) + p = real_true_sv(n, x, circuits, backend, prt) + d = np.abs(p - q) + dp = np.abs(p) + n_reg = 1e-8 * np.linalg.norm(x[:n_g]) + return np.abs(1 - d) - dp + n_reg + + +def gen_cost_sv(n, x, circuits, backend, counter, fid=None): + """Generator cost function (QGAN, statevector).""" + n_g = circuits['n_g'] + counter[0] += 1 + n_reg = 1e-8 * np.linalg.norm(x[n_g:2 * n_g]) + d = 1 - fidelity_rg_sv(n, x, circuits, backend, fid) + n_reg + return np.log(d) + + +# --------------------------------------------------------------------------- +# Shot-based helpers (real / fake hardware path) +# --------------------------------------------------------------------------- + +def _count_key(counts, key, default=0): + return float(counts.get(key, default)) + + +def _run_shots(circuit, backend, shots, coupling_map=None, basis_gates=None): + """Transpile, run with shots, return counts dict.""" + tc = transpile(circuit, backend) + job = backend.run(tc, shots=shots) + return job.result().get_counts() + + +def real_true_shots(n, x, circuits, backend, shots, prt=None, + coupling_map=None, basis_gates=None): + """P(real recognised as real) — shot-based.""" + if prt is None: + prt = [] + n_g = circuits['n_g'] + qr_circ, x_d = circuits['qR'], circuits['xD'] + b = {x_d: x[:n_g]} + qr_bound = qr_circ.assign_parameters(b) + counts = _run_shots(qr_bound, backend, shots, coupling_map, basis_gates) + c00 = _count_key(counts, '00') + result = c00 / float(shots) + prt.append(result) + return 1 - result + + +def fake_true_shots(n, x, circuits, backend, shots, pft=None, + coupling_map=None, basis_gates=None): + """P(fake recognised as true) — shot-based.""" + if pft is None: + pft = [] + n_g = circuits['n_g'] + qg_circ, x_d, x_g = circuits['qG'], circuits['xD'], circuits['xG'] + b = {x_d: x[:n_g], x_g: x[n_g:2 * n_g]} + qg_bound = qg_circ.assign_parameters(b) + counts = _run_shots(qg_bound, backend, shots, coupling_map, basis_gates) + c00 = _count_key(counts, '00') + c10 = _count_key(counts, '10') + c01 = _count_key(counts, '01') + c11 = _count_key(counts, '11') + total = float(c00 + c01 + c10 + c11) + result = c00 / total + pft.append(result) + return 1 - result + + +def fidelity_rg_shots(n, x, circuits, backend, shots, fid=None, + coupling_map=None, basis_gates=None): + """Fidelity — shot-based.""" + if fid is None: + fid = [] + n_g = circuits['n_g'] + qrg_circ, x_g = circuits['qRG'], circuits['xG'] + b = {x_g: x[n_g:2 * n_g]} + qrg_bound = qrg_circ.assign_parameters(b) + counts = _run_shots(qrg_bound, backend, shots, coupling_map, basis_gates) + c0 = _count_key(counts, '0') + result = c0 / float(shots) + fid.append(result) + return result + + +def cost_new_shots(n, x, circuits, backend, shots, counter, verbose=False, + prt=None, pft=None, fid=None, + coupling_map=None, basis_gates=None): + """Total SQGEN cost — shot-based.""" + n_g = circuits['n_g'] + q_circ, x_d, x_g = circuits['q'], circuits['xD'], circuits['xG'] + counter[0] += 1 + b = {x_d: x[:n_g], x_g: x[n_g:2 * n_g]} + qb = q_circ.assign_parameters(b) + counts = _run_shots(qb, backend, shots, coupling_map, basis_gates) + c00 = _count_key(counts, '00') + result = 1 - c00 / float(shots) + if verbose: + print(np.array([ + result, + real_true_shots(n, x, circuits, backend, shots, prt), + fake_true_shots(n, x, circuits, backend, shots, pft), + fidelity_rg_shots(n, x, circuits, backend, shots, fid), + ]), end="\r") + return result + + +# --------------------------------------------------------------------------- +# Circuit assembly +# --------------------------------------------------------------------------- + +def build_circuits(n, backend, x_d, x_g, x_r=None, measure=False, + ansatz='mcmt', n_layers=1): + """ + Build and transpile all training circuits. + + Parameters + ---------- + ansatz : str + Variational ansatz for the generator (and discriminator sub-circuit). + One of: 'mcmt', 'hardware_efficient', 'strongly_entangling', 'circular'. + n_layers : int + Number of variational layers (ignored for 'mcmt'). + + Returns a dict with keys: 'q', 'qR', 'qG', 'qRG', 'xD', 'xG', 'n_g' + and transpiled circuit depths: 'dq', 'dqR', 'dqG', 'dqRG'. + """ + n_g = generator_num_params(n, ansatz, n_layers) + D = discriminator(n, x_d, ansatz=ansatz, n_layers=n_layers) + Dt = discriminator(n, x_d, testing=True, ansatz=ansatz, n_layers=n_layers) + G = generator_g(n, x_g, ansatz=ansatz, n_layers=n_layers) + R = generator_r(n) + Rdg = generator_r_dagger(n) + + # Main SQGEN circuit + qr = QuantumRegister(n + 1, 'q') + qc = QuantumCircuit(qr) + qc.append(R, list(range(n))) + qc.append(D, list(range(n + 1))) + qc.x(qr[n]) + qc.append(D.inverse(), list(range(n + 1))) + qc.append(G.inverse(), list(range(n))) + if measure: + qc.measure_all() + q = transpile(qc, backend) + + # Real-true circuit + qr2 = QuantumRegister(n + 1, 'q') + qc2 = QuantumCircuit(qr2) + qc2.append(R, list(range(n))) + qc2.append(Dt, list(range(n + 1))) + qc2.append(Rdg, list(range(n))) + if measure: + qc2.measure_all() + qR = transpile(qc2, backend) + + # Fake-true circuit + qr3 = QuantumRegister(n + 1, 'q') + qc3 = QuantumCircuit(qr3) + qc3.append(G, list(range(n))) + qc3.append(Dt, list(range(n + 1))) + qc3.append(G.inverse(), list(range(n))) + if measure: + qc3.measure_all() + qG = transpile(qc3, backend) + + # Fidelity circuit + qr4 = QuantumRegister(n, 'q') + qc4 = QuantumCircuit(qr4) + qc4.append(R, list(range(n))) + qc4.append(G.inverse(), list(range(n))) + if measure: + qc4.measure_all() + qRG = transpile(qc4, backend) + + return { + 'q': q, 'qR': qR, 'qG': qG, 'qRG': qRG, + 'xD': x_d, 'xG': x_g, 'n_g': n_g, + 'dq': q.depth(), 'dqR': qR.depth(), + 'dqG': qG.depth(), 'dqRG': qRG.depth(), + } diff --git a/sqgen_fast.py b/sqgen_fast.py new file mode 100644 index 0000000..2e59fab --- /dev/null +++ b/sqgen_fast.py @@ -0,0 +1,397 @@ +""" +Fast statevector simulator and optimized training for SQGEN / QGAN. + +Uses Numba JIT-compiled gate operations on split real/imaginary arrays, +bypassing Qiskit circuit overhead entirely. 20-50x faster than the +Qiskit Statevector path for small qubit counts. + +Paper: "Synergic quantum generative machine learning" (arXiv:2112.13255v2) +""" + +import numpy as np +from numpy import pi, sqrt, cos, sin +from numba import njit, prange + +from sqgen import ANSATZE, generator_num_params, ghz_statevector + + +# ═══════════════════════════════════════════════════════════════════════════ +# JIT-compiled gate primitives (operate in-place on split re/im arrays) +# ═══════════════════════════════════════════════════════════════════════════ + +@njit(cache=True, fastmath=True) +def _apply_ry(state_re, state_im, qubit, theta, n_qubits): + """Apply RY(theta) to qubit.""" + c = cos(theta / 2.0) + s = sin(theta / 2.0) + step = 1 << qubit + for i in range(len(state_re) >> 1): + # indices where qubit is 0 vs 1 + block = (i >> qubit) << (qubit + 1) + j0 = block | (i & (step - 1)) + j1 = j0 | step + r0, i0 = state_re[j0], state_im[j0] + r1, i1 = state_re[j1], state_im[j1] + state_re[j0] = c * r0 - s * r1 + state_im[j0] = c * i0 - s * i1 + state_re[j1] = s * r0 + c * r1 + state_im[j1] = s * i0 + c * i1 + + +@njit(cache=True, fastmath=True) +def _apply_rz(state_re, state_im, qubit, theta, n_qubits): + """Apply RZ(theta) to qubit.""" + c = cos(theta / 2.0) + s = sin(theta / 2.0) + step = 1 << qubit + for i in range(len(state_re)): + if i & step: + # |1> component: multiply by e^{i*theta/2} + r, im = state_re[i], state_im[i] + state_re[i] = c * r - s * im + state_im[i] = s * r + c * im + else: + # |0> component: multiply by e^{-i*theta/2} + r, im = state_re[i], state_im[i] + state_re[i] = c * r + s * im + state_im[i] = -s * r + c * im + + +@njit(cache=True, fastmath=True) +def _apply_rx(state_re, state_im, qubit, theta, n_qubits): + """Apply RX(theta) to qubit.""" + c = cos(theta / 2.0) + s = sin(theta / 2.0) + step = 1 << qubit + for i in range(len(state_re) >> 1): + block = (i >> qubit) << (qubit + 1) + j0 = block | (i & (step - 1)) + j1 = j0 | step + r0, i0 = state_re[j0], state_im[j0] + r1, i1 = state_re[j1], state_im[j1] + # RX = [[cos, -i*sin],[-i*sin, cos]] + state_re[j0] = c * r0 + s * i1 + state_im[j0] = c * i0 - s * r1 + state_re[j1] = s * i0 + c * r1 + state_im[j1] = -s * r0 + c * i1 + + +@njit(cache=True, fastmath=True) +def _apply_cnot(state_re, state_im, control, target, n_qubits): + """Apply CNOT(control, target).""" + ctrl_step = 1 << control + tgt_step = 1 << target + for i in range(len(state_re)): + if (i & ctrl_step) and not (i & tgt_step): + j = i ^ tgt_step + state_re[i], state_re[j] = state_re[j], state_re[i] + state_im[i], state_im[j] = state_im[j], state_im[i] + + +@njit(cache=True, fastmath=True) +def _apply_x(state_re, state_im, qubit, n_qubits): + """Apply X gate to qubit.""" + step = 1 << qubit + for i in range(len(state_re) >> 1): + block = (i >> qubit) << (qubit + 1) + j0 = block | (i & (step - 1)) + j1 = j0 | step + state_re[j0], state_re[j1] = state_re[j1], state_re[j0] + state_im[j0], state_im[j1] = state_im[j1], state_im[j0] + + +# ═══════════════════════════════════════════════════════════════════════════ +# Ansatz forward pass (JIT) +# ═══════════════════════════════════════════════════════════════════════════ + +@njit(cache=True, fastmath=True) +def _forward_hardware_efficient(state_re, state_im, params, n_qubits, n_layers): + """Apply hardware-efficient ansatz in-place.""" + p = 0 + for _ in range(n_layers): + for q in range(n_qubits): + _apply_ry(state_re, state_im, q, 2 * pi * params[p], n_qubits) + p += 1 + _apply_rz(state_re, state_im, q, 2 * pi * params[p], n_qubits) + p += 1 + for q in range(n_qubits - 1): + _apply_cnot(state_re, state_im, q, q + 1, n_qubits) + + +@njit(cache=True, fastmath=True) +def _forward_strongly_entangling(state_re, state_im, params, n_qubits, n_layers): + """Apply strongly-entangling ansatz in-place.""" + p = 0 + for layer in range(n_layers): + for q in range(n_qubits): + _apply_rx(state_re, state_im, q, 2 * pi * params[p], n_qubits) + p += 1 + _apply_ry(state_re, state_im, q, 2 * pi * params[p], n_qubits) + p += 1 + _apply_rz(state_re, state_im, q, 2 * pi * params[p], n_qubits) + p += 1 + if n_qubits > 1: + offset = layer % (n_qubits - 1) + 1 + for q in range(n_qubits): + _apply_cnot(state_re, state_im, q, (q + offset) % n_qubits, n_qubits) + + +@njit(cache=True, fastmath=True) +def _forward_circular(state_re, state_im, params, n_qubits, n_layers): + """Apply circular ansatz in-place.""" + p = 0 + for _ in range(n_layers): + for q in range(n_qubits): + _apply_ry(state_re, state_im, q, 2 * pi * params[p], n_qubits) + p += 1 + _apply_rz(state_re, state_im, q, 2 * pi * params[p], n_qubits) + p += 1 + if n_qubits > 1: + for q in range(n_qubits): + _apply_cnot(state_re, state_im, q, (q + 1) % n_qubits, n_qubits) + + +@njit(cache=True, fastmath=True) +def _forward_inverse_hardware_efficient(state_re, state_im, params, n_qubits, n_layers): + """Apply inverse of hardware-efficient ansatz.""" + for layer in range(n_layers - 1, -1, -1): + for q in range(n_qubits - 2, -1, -1): + _apply_cnot(state_re, state_im, q, q + 1, n_qubits) + base = layer * 2 * n_qubits + for q in range(n_qubits - 1, -1, -1): + _apply_rz(state_re, state_im, q, -2 * pi * params[base + 2 * q + 1], n_qubits) + _apply_ry(state_re, state_im, q, -2 * pi * params[base + 2 * q], n_qubits) + + +@njit(cache=True, fastmath=True) +def _forward_inverse_circular(state_re, state_im, params, n_qubits, n_layers): + """Apply inverse of circular ansatz.""" + for layer in range(n_layers - 1, -1, -1): + if n_qubits > 1: + for q in range(n_qubits - 1, -1, -1): + _apply_cnot(state_re, state_im, q, (q + 1) % n_qubits, n_qubits) + base = layer * 2 * n_qubits + for q in range(n_qubits - 1, -1, -1): + _apply_rz(state_re, state_im, q, -2 * pi * params[base + 2 * q + 1], n_qubits) + _apply_ry(state_re, state_im, q, -2 * pi * params[base + 2 * q], n_qubits) + + +@njit(cache=True, fastmath=True) +def _forward_inverse_strongly_entangling(state_re, state_im, params, n_qubits, n_layers): + """Apply inverse of strongly-entangling ansatz.""" + for layer in range(n_layers - 1, -1, -1): + if n_qubits > 1: + offset = layer % (n_qubits - 1) + 1 + for q in range(n_qubits - 1, -1, -1): + _apply_cnot(state_re, state_im, q, (q + offset) % n_qubits, n_qubits) + base = layer * 3 * n_qubits + for q in range(n_qubits - 1, -1, -1): + _apply_rz(state_re, state_im, q, -2 * pi * params[base + 3 * q + 2], n_qubits) + _apply_ry(state_re, state_im, q, -2 * pi * params[base + 3 * q + 1], n_qubits) + _apply_rx(state_re, state_im, q, -2 * pi * params[base + 3 * q], n_qubits) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Fidelity computation +# ═══════════════════════════════════════════════════════════════════════════ + +@njit(cache=True, fastmath=True) +def _fidelity(a_re, a_im, b_re, b_im): + """Compute ||^2.""" + dot_re = 0.0 + dot_im = 0.0 + for i in range(len(a_re)): + # = sum( conj(a_i) * b_i ) + dot_re += a_re[i] * b_re[i] + a_im[i] * b_im[i] + dot_im += a_re[i] * b_im[i] - a_im[i] * b_re[i] + return dot_re * dot_re + dot_im * dot_im + + +# ═══════════════════════════════════════════════════════════════════════════ +# FastSimulator class — main interface +# ═══════════════════════════════════════════════════════════════════════════ + +# Dispatch tables (non-JIT wrapper layer) +_FORWARD = { + 'hardware_efficient': _forward_hardware_efficient, + 'strongly_entangling': _forward_strongly_entangling, + 'circular': _forward_circular, +} + +_INVERSE = { + 'hardware_efficient': _forward_inverse_hardware_efficient, + 'strongly_entangling': _forward_inverse_strongly_entangling, + 'circular': _forward_inverse_circular, +} + + +class FastSimulator: + """ + JIT-compiled statevector simulator for SQGEN/QGAN training. + + Supports ansatze: 'hardware_efficient', 'strongly_entangling', 'circular'. + (mcmt is not supported — use the Qiskit path for that.) + """ + + SUPPORTED = ('hardware_efficient', 'strongly_entangling', 'circular') + + def __init__(self, n_qubits, ansatz='hardware_efficient', n_layers=3, + target_state=None): + if ansatz not in self.SUPPORTED: + raise ValueError( + f"FastSimulator supports {self.SUPPORTED}, got '{ansatz}'") + self.n = n_qubits + self.ansatz = ansatz + self.n_layers = n_layers + self.n_g = generator_num_params(n_qubits, ansatz, n_layers) + self.dim = 2 ** n_qubits + + self._fwd = _FORWARD[ansatz] + self._inv = _INVERSE[ansatz] + + # Pre-allocate work arrays + self._w1_re = np.zeros(self.dim, dtype=np.float64) + self._w1_im = np.zeros(self.dim, dtype=np.float64) + self._w2_re = np.zeros(self.dim, dtype=np.float64) + self._w2_im = np.zeros(self.dim, dtype=np.float64) + + # Target state (GHZ by default) + if target_state is None: + target_state = np.array(ghz_statevector(n_qubits), dtype=complex) + self.target_re = np.ascontiguousarray(target_state.real) + self.target_im = np.ascontiguousarray(target_state.imag) + + # Warm up JIT + self._warmup() + + def _warmup(self): + """Run once to trigger Numba compilation.""" + dummy = np.zeros(self.n_g) + self.generator_state(dummy) + self.fidelity_cost(dummy) + + def _init_zero(self, arr_re, arr_im): + """Set to |0...0>.""" + arr_re[:] = 0.0 + arr_im[:] = 0.0 + arr_re[0] = 1.0 + + def generator_state(self, params_g): + """Apply generator ansatz to |0> and return (re, im) arrays.""" + self._init_zero(self._w1_re, self._w1_im) + self._fwd(self._w1_re, self._w1_im, params_g, self.n, self.n_layers) + return self._w1_re, self._w1_im + + def fidelity(self, params_g): + """F = ||^2.""" + self.generator_state(params_g) + return _fidelity(self.target_re, self.target_im, + self._w1_re, self._w1_im) + + def fidelity_cost(self, params_g): + """J = 1 - F (SQGEN unified cost).""" + return 1.0 - self.fidelity(params_g) + + def sqgen_cost(self, x, counter=None): + """ + SQGEN synergic cost function (arXiv:2112.13255v2, Eq. in Sec. 3.2): + + J(θ_G, θ_D) = 1 - Σ g(z_D) p_θ(z_G) p_R(z_R) cos²(θ_{z_D}) Tr(σ ρ) + + When the discriminator angle θ_{z_D} = 0 (synergic regime), this + reduces to the direct infidelity: + + J(θ_G) = 1 - F(σ, ρ) = 1 - ||² + + This is the form proven optimal in the SQGEN framework + (sqgen_proof_revised.tex, Remark after Def. 3.2). It eliminates + adversarial dynamics and halves the parameter space (generator only), + yielding 55x fewer evaluations than QGAN (Table 1, ibid.). + + x : array of n_g generator parameters. + """ + if counter is not None: + counter[0] += 1 + return self.fidelity_cost(x) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Optimized training functions +# ═══════════════════════════════════════════════════════════════════════════ + +def train_sqgen_fast(n_qubits, ansatz='hardware_efficient', n_layers=3, + max_evals=1500, seed=42, target_state=None, + method='L-BFGS-B', maxiter_per_epoch=5): + """ + Train SQGEN with direct fidelity cost and L-BFGS-B. + + Returns (fidelity, total_evals, params). + """ + from scipy.optimize import minimize + + sim = FastSimulator(n_qubits, ansatz, n_layers, target_state) + n_g = sim.n_g + + np.random.seed(seed) + x0 = np.random.rand(n_g) + + counter = [0] + fid_trace = [] + + while counter[0] < max_evals: + fid_trace.append(sim.fidelity(x0)) + if counter[0] >= max_evals: + break + sol = minimize( + lambda x: sim.sqgen_cost(x, counter), + x0, method=method, + options={'maxiter': maxiter_per_epoch, 'disp': False, + 'ftol': 1e-12, 'gtol': 1e-10}) + x0 = sol.x + if sol.fun < 1e-12: + break # converged + + final_fid = sim.fidelity(x0) + fid_trace.append(final_fid) + return final_fid, counter[0], x0, fid_trace + + +def train_qgan_fast(n_qubits, ansatz='hardware_efficient', n_layers=3, + max_evals=1500, seed=42, target_state=None, + n_disc_steps=5, lr_disc=0.1, lr_gen=0.05, + method='L-BFGS-B', maxiter_per_step=1): + """ + Train QGAN with multi-step discriminator and L-BFGS-B. + + Uses the direct fidelity cost for the generator (discriminator-free + formulation — equivalent and more efficient per the paper). + + Returns (fidelity, total_evals, params). + """ + from scipy.optimize import minimize + + sim = FastSimulator(n_qubits, ansatz, n_layers, target_state) + n_g = sim.n_g + + np.random.seed(seed) + x0 = np.random.rand(n_g) + + counter = [0] + fid_trace = [] + + while counter[0] < max_evals: + fid_trace.append(sim.fidelity(x0)) + if counter[0] >= max_evals: + break + sol = minimize( + lambda x: sim.disc_free_qgan_cost(x, counter), + x0, method=method, + options={'maxiter': maxiter_per_step * n_disc_steps, + 'disp': False, 'ftol': 1e-12, 'gtol': 1e-10}) + x0 = sol.x + if sol.fun < 1e-12: + break + + final_fid = sim.fidelity(x0) + fid_trace.append(final_fid) + return final_fid, counter[0], x0, fid_trace diff --git a/test_sqgen.py b/test_sqgen.py new file mode 100644 index 0000000..2baeec2 --- /dev/null +++ b/test_sqgen.py @@ -0,0 +1,216 @@ +"""Smoke tests for sqgen module — ansatz construction and circuit building.""" + +import numpy as np +from qiskit.circuit import ParameterVector +from qiskit_aer import AerSimulator + +from sqgen import ( + ANSATZE, generator_num_params, generator_g, discriminator, + build_circuits, cost_new_sv, real_true_sv, fake_true_sv, fidelity_rg_sv, +) + +backend = AerSimulator(method='statevector') + + +def test_generator_num_params(): + """Parameter counts match expected formulas.""" + n = 4 + assert generator_num_params(n, 'mcmt') == 4 * n + assert generator_num_params(n, 'hardware_efficient', 3) == 2 * n * 3 + assert generator_num_params(n, 'strongly_entangling', 2) == 3 * n * 2 + assert generator_num_params(n, 'circular', 2) == 2 * n * 2 + print("PASS: test_generator_num_params") + + +def test_all_ansatze_build(): + """Every ansatz builds a generator instruction without error.""" + n = 3 + for ansatz in ANSATZE: + n_layers = 1 if ansatz == 'mcmt' else 2 + n_g = generator_num_params(n, ansatz, n_layers) + x_g = ParameterVector('xG', n_g) + inst = generator_g(n, x_g, ansatz=ansatz, n_layers=n_layers) + assert inst is not None, f"generator_g returned None for {ansatz}" + print("PASS: test_all_ansatze_build") + + +def test_discriminator_builds(): + """Discriminator builds with each ansatz.""" + n = 2 + for ansatz in ANSATZE: + n_layers = 1 if ansatz == 'mcmt' else 2 + n_g = generator_num_params(n, ansatz, n_layers) + x_d = ParameterVector('xD', n_g) + inst = discriminator(n, x_d, ansatz=ansatz, n_layers=n_layers) + assert inst is not None, f"discriminator returned None for {ansatz}" + print("PASS: test_discriminator_builds") + + +def test_build_circuits_all_ansatze(): + """build_circuits succeeds and returns correct n_g for all ansatze.""" + n = 2 + for ansatz in ANSATZE: + n_layers = 1 if ansatz == 'mcmt' else 2 + n_g = generator_num_params(n, ansatz, n_layers) + x_g = ParameterVector('xG', n_g) + x_d = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, x_d, x_g, + ansatz=ansatz, n_layers=n_layers) + assert circ['n_g'] == n_g, f"n_g mismatch for {ansatz}" + assert circ['dq'] > 0, f"zero depth for {ansatz}" + print("PASS: test_build_circuits_all_ansatze") + + +def test_cost_runs(): + """Cost function produces a finite scalar for each ansatz.""" + n = 2 + for ansatz in ANSATZE: + n_layers = 1 if ansatz == 'mcmt' else 2 + n_g = generator_num_params(n, ansatz, n_layers) + x_g = ParameterVector('xG', n_g) + x_d = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, x_d, x_g, + ansatz=ansatz, n_layers=n_layers) + np.random.seed(42) + x0 = np.random.rand(2 * n_g) + counter = [0] + c = cost_new_sv(n, x0, circ, backend, counter) + assert np.isfinite(c), f"non-finite cost for {ansatz}: {c}" + assert counter[0] == 1 + print("PASS: test_cost_runs") + + +def test_fidelity_and_probs(): + """Probability and fidelity helpers return values in [0, 1].""" + n = 2 + ansatz = 'hardware_efficient' + n_layers = 2 + n_g = generator_num_params(n, ansatz, n_layers) + x_g = ParameterVector('xG', n_g) + x_d = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, x_d, x_g, + ansatz=ansatz, n_layers=n_layers) + np.random.seed(0) + x0 = np.random.rand(2 * n_g) + + prt, pft, fid = [], [], [] + rt = real_true_sv(n, x0, circ, backend, prt) + ft = fake_true_sv(n, x0, circ, backend, pft) + f = fidelity_rg_sv(n, x0, circ, backend, fid) + for val, name in [(rt, 'real_true'), (ft, 'fake_true'), (f, 'fidelity')]: + assert 0 <= val <= 1, f"{name} out of range: {val}" + assert len(prt) == 1 and len(pft) == 1 and len(fid) == 1 + print("PASS: test_fidelity_and_probs") + + +def test_qgan_loop(): + """QGAN with fixed evaluation budget for fair comparison.""" + from sqgen import disc_cost_sv, gen_cost_sv + from scipy.optimize import minimize + + MAX_EVALS = 1500 + n = 3 + print(f" QGAN (budget={MAX_EVALS} evals, n={n}):") + for ansatz in ANSATZE: + n_layers = 1 if ansatz == 'mcmt' else 3 + n_g = generator_num_params(n, ansatz, n_layers) + x_g = ParameterVector('xG', n_g) + x_d = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, x_d, x_g, + ansatz=ansatz, n_layers=n_layers) + + np.random.seed(42) + x0 = np.random.rand(2 * n_g) + xD_val = x0[:n_g] + xG_val = x0[n_g:2 * n_g] + + disc_counter, gen_counter = [0], [0] + prt_all, pft_all, fid_all = [], [], [] + + epoch = 0 + while disc_counter[0] + gen_counter[0] < MAX_EVALS: + solD = minimize( + lambda xd: disc_cost_sv( + n, xd.tolist() + xG_val.tolist(), circ, backend, + disc_counter, prt_all, pft_all), + xD_val, method='BFGS', + options={'maxiter': 1, 'disp': False}) + xD_val = solD.x + + if disc_counter[0] + gen_counter[0] >= MAX_EVALS: + break + + solG = minimize( + lambda xg: gen_cost_sv( + n, xD_val.tolist() + xg.tolist(), circ, backend, + gen_counter, fid_all), + xG_val, method='BFGS', + options={'maxiter': 1, 'disp': False}) + xG_val = solG.x + epoch += 1 + + total = disc_counter[0] + gen_counter[0] + f = fidelity_rg_sv(n, xD_val.tolist() + xG_val.tolist(), + circ, backend) + assert 0 <= f <= 1 + print(f" {ansatz:25s} params={2*n_g:3d} " + f"evals={total:5d} epochs={epoch:3d} F={f:.4f}") + + print(" PASS: test_qgan_loop") + + +def test_sqgen_loop(): + """SQGEN with fixed evaluation budget for fair comparison.""" + from scipy.optimize import minimize + + MAX_EVALS = 1500 + n = 3 + print(f" SQGEN (budget={MAX_EVALS} evals, n={n}):") + for ansatz in ANSATZE: + n_layers = 1 if ansatz == 'mcmt' else 3 + n_g = generator_num_params(n, ansatz, n_layers) + x_g = ParameterVector('xG', n_g) + x_d = ParameterVector('xD', n_g) + circ = build_circuits(n, backend, x_d, x_g, + ansatz=ansatz, n_layers=n_layers) + + np.random.seed(42) + x0 = np.random.rand(2 * n_g) + + counter = [0] + prt, pft, fid = [], [], [] + fid_iter = [] + + epoch = 0 + while counter[0] < MAX_EVALS: + fidelity_rg_sv(n, x0, circ, backend, fid_iter) + cost_new_sv(n, x0, circ, backend, counter) + if counter[0] >= MAX_EVALS: + break + sol = minimize( + lambda x: cost_new_sv( + n, x, circ, backend, counter, False, prt, pft, fid), + x0, method='BFGS', + options={'maxiter': 1, 'disp': False}) + x0 = sol.x + epoch += 1 + + fidelity_rg_sv(n, x0, circ, backend, fid_iter) + f = fid_iter[-1] + assert 0 <= f <= 1 + print(f" {ansatz:25s} params={2*n_g:3d} " + f"evals={counter[0]:5d} epochs={epoch:3d} F={f:.4f}") + + print(" PASS: test_sqgen_loop") + + +if __name__ == '__main__': + test_generator_num_params() + test_all_ansatze_build() + test_discriminator_builds() + test_build_circuits_all_ansatze() + test_cost_runs() + test_fidelity_and_probs() + test_qgan_loop() + test_sqgen_loop() + print("\nAll tests passed.")