RGD optimization method for quantum state tomography by HuberyMing · Pull Request #1485 · qiboteam/qibo

HuberyMing · 2024-10-11T15:14:19Z

Checklist:

Reviewers confirm new code works as expected.
Tests are passing.
Coverage does not decrease.
Documentation is updated.

To include the optimization method of Riemannian Gradient Descent (RGD) algorithm to do the tomography problem. The implementation follows from the arXiv:2210.04717, which was published in Phys. Rev. Lett. 132, 240804

renatomello · 2024-10-14T05:31:41Z

Not entering the other merits of the PR, this would have to be inside the tomography module instead of being a separate tomography_RGB module.
Moreover, tests should follow how tests in qibo are done. Right now they're in a folder that is being ignored by gitignore.

alecandido · 2024-10-16T11:21:19Z

@renatomello to follow up what I told you about remotes during the Qibo meeting, you could do the following:

git remote add hubery-ming git@github.com:HuberyMing/qibo.git
git pull hubery-ming
git switch -c QST -t hubery-ming/QST

in this way you would set up a remote named hubery-ming, tracking @HuberyMing's repo, and then create a local branch in your local repo named QST, tracking the https://github.com/HuberyMing/qibo/tree/QST branch.

Of course, you won't have pushing rights (unless @HuberyMing will grant you), but that's in case you want to test it locally.

Maybe you were already familiar with this, but this may also be useful for everyone else who'd like to have a look (and it could be a reference to deal for PRs from forks in general).

renatomello · 2024-10-17T06:54:41Z

+                        2 * np.pi * random.random(),
+                        2 * np.pi * random.random(),
+                        2 * np.pi * random.random(),


A tip for the future: This choice of phases does not sample random states uniformly. Please see

qibo/src/qibo/quantum_info/random_ensembles.py

Line 48 in 958b11b

def uniform_sampling_U3(ngates: int, seed=None, backend=None):

and https://pennylane.ai/qml/demos/tutorial_haar_measure

renatomello · 2024-10-17T06:58:01Z

+from qibo import gates, quantum_info
+
+
+class State:


Correct me if I'm wrong, but it seems like this base class is unnecessary since every method is dependent on a qibo.models.Circuit method. I'd say that if these state classes are needed at all (which I'm not convinced of), you could use Circuit as the base class directly.

renatomello · 2024-10-17T07:03:28Z

+class HadamardState(State):
+    """
+    Constructor for HadamardState class
+    """
+
+    def __init__(self, n):
+        State.__init__(self, n)
+        self.circuit_name = "Hadamard"
+
+    def create_circuit(self):
+        circuit = qibo.Circuit(self.n)
+
+        for i in range(self.n):
+            circuit.add(gates.H(i))
+
+        self.circuit = circuit


Since this is just a layer of Hadamards, this is a Hadamard transform of the initial state. A more concise way to implement this would be to modify the following function:

qibo/src/qibo/quantum_info/utils.py

Line 118 in 958b11b

def hadamard_transform(array, implementation: str = "fast", backend=None):

For instance, if the input is a circuit, then a layer of Hadamard is added. That would perform the Hadamard transform of whatever state one wants, including the zero state in your case.

renatomello · 2024-10-17T07:18:03Z

+def build_projector_naive(label, label_format="big_endian"):
+    """to directly generate a Pauli matrix from tensor products
+
+    Args:
+        label (str): label of the projection, e.g.  'XXZYZ'
+        label_format (str, optional): the ordering of the label. Defaults to 'big_endian'.
+
+    Raises:
+        Exception: when the matrix size is too big (i.e. for qubit number > 6)
+
+    Returns:
+        ndarray: a matrix representing the Pauli operator
+    """
+    if label_format == "little_endian":
+        label = label[::-1]
+    if len(label) > 6:
+        raise Exception("Too big matrix to generate!")
+    projector = reduce(
+        lambda acc, item: np.kron(acc, item),
+        [matrix_dict[letter] for letter in label],
+        [1],
+    )
+    return projector
+
+
+# Generate a projector by computing non-zero coordinates and their values in the matrix, aka the "fast" implementation
+def build_projector_fast(label, label_format="big_endian"):
+    """to fastly generate a Pauli projection matrix in sparse matrix format
+
+    Args:
+        label (str): label of the projection, e.g.  'XXZYZ'
+        label_format (str, optional): the ordering of the label. Defaults to 'big_endian'.
+
+    Returns:
+        sparse matrix: sparse matrix of the Pauli operator representing label
+    """
+    if label_format == "little_endian":
+        label = label[::-1]
+
+    n = len(label)
+    d = 2**n
+
+    # map's result NOT subscriptable in py3, just tried map() -> list(map()) for py2 to py3
+    ij = [
+        list(map(binarize, y))
+        for y in [zip(*x) for x in product(*[ij_dict[letter] for letter in label])]
+    ]
+    values = [
+        reduce(lambda z, w: z * w, y)
+        for y in [x for x in product(*[values_dict[letter] for letter in label])]
+    ]
+    ijv = list(map(lambda x: (x[0][0], x[0][1], x[1]), zip(ij, values)))
+
+    i_coords, j_coords, entries = zip(*ijv)
+
+    projector = sparse.coo_matrix(
+        (entries, (i_coords, j_coords)), shape=(d, d), dtype=complex
+    )
+    return projector


@BrunoLiegiBastonLiegi this is a case where it would be convenient to have the quantum_info.basis.pauli_basis function return a generator because then one could use that to directly access a specific element of the basis.

mho291 · 2024-10-17T07:35:36Z

Just curious, would it be helpful to have a call to understand how the algorithm works? It might make it easier to review the code.

mho291 · 2024-10-17T07:57:27Z

@HuberyMing If you're doing the changes locally, you could do pip install pre-commit followed by pre-commit install inside your environment. Each time you do git commit -m "message", the pre-commit will run. If anything fails, you have to do git commit again before git push. Then we we will avoid having extra commits in this PR.

alecandido · 2024-10-17T08:03:14Z

Each time you do git commit -m "message", the pre-commit will run. If anything fails, you have to do git commit again before git push.

Specifically, if it fails the involved hook is "declaring" a failure, that may imply that you have to fix something manually. However, most of the hooks we have registered are also auto-fixing the issues they find (e.g. the formatter fails if the result differs from the original - but if it fails, it also reformats the fails).
The auto-fixes are not staged, so you also need to git add the fixed files.

However, in these cases, I'm sure that @HuberyMing just applied the suggestions from the review from the PR web interface. In which case there is no chance of having pre-commit running anywhere else...

(as you also prepended

@HuberyMing If you're doing the changes locally,

)

renatomello · 2024-10-17T08:05:32Z

Just curious, would it be helpful to have a call to understand how the algorithm works? It might make it easier to review the code.

It might me needed, yes. Let me read the paper first though, so I have a better understanding.

HuberyMing · 2024-10-17T08:15:41Z

Just curious, would it be helpful to have a call to understand how the algorithm works? It might make it easier to review the code.

It might me needed, yes. Let me read the paper first though, so I have a better understanding.

Hi @renatomello Thank you for the review. No problem. How do we discuss?

renatomello · 2024-10-17T08:30:00Z

Just curious, would it be helpful to have a call to understand how the algorithm works? It might make it easier to review the code.

It might me needed, yes. Let me read the paper first though, so I have a better understanding.

Hi @renatomello Thank you for the review. No problem. How do we discuss?

We can try to arrange a chat via Zoom next week. Right now, I'd like to see a separate PR adding the GHZ state to qibo.models.encodings. I think it will be a good lesson on a small scale on how this PR should move forward in terms of code implementation.
You can follow the style of the functions inside qibo.models.encodings as well as the test style inside tests/test_models_encodings.py.

It's a good first issue.

HuberyMing · 2024-10-17T08:40:26Z

Just curious, would it be helpful to have a call to understand how the algorithm works? It might make it easier to review the code.

It might me needed, yes. Let me read the paper first though, so I have a better understanding.

Hi @renatomello Thank you for the review. No problem. How do we discuss?

We can try to arrange a chat via Zoom next week. Right now, I'd like to see a separate PR adding the GHZ state to qibo.models.encodings. I think it will be a good lesson on a small scale on how this PR should move forward in terms of code implementation. You can follow the style of the functions inside qibo.models.encodings as well as the test style inside tests/test_models_encodings.py.

It's a good first issue.

Thanks. I will do these first.

renatomello · 2024-10-17T09:57:32Z

+
+    elif circuit_Choice == 2:  # directly generate density matrix via qutip
+        Nr = 1
+        rho = qu.rand_dm_ginibre(2**n, dims=[[2] * n, [2] * n], rank=Nr)


Suggested change

rho = qu.rand_dm_ginibre(2**n, dims=[[2] * n, [2] * n], rank=Nr)

rho = random_density_matrix(2**n, rank=Nr, metric="ginibre")

MatteoRobbiati · 2025-01-08T22:30:52Z

Do you plan to continue with this PR @HuberyMing? :)

HuberyMing · 2025-01-09T05:56:16Z

Do you plan to continue with this PR @HuberyMing? :)

Sorry for not pushing forward much. I will continue the PR. Hope to finish it soon.

MatteoRobbiati · 2025-01-09T08:21:19Z

Sorry for not pushing forward much. I will continue the PR. Hope to finish it soon.

No problem! Take your time :) I just wanted to check the status!

for more information, see https://pre-commit.ci

HuberyMing · 2026-03-18T11:12:40Z

Since 'NumpyBackend' object has no attribute 'np', do we use 'np' (numpy) directly instead?

alecandido · 2026-03-18T12:22:19Z

Hi @HuberyMing: in #1711 the backends' mechanism was quite modified, which triggered on its own the release of Qibo v0.3.0.

To a first approximation, the self.np attribute has been replaced by the self.engine.

qibo/src/qibo/backends/abstract.py

Line 25 in e8bca30

self.engine = None

Though that is meant to be used mainly internally to the backend. If you are a backend user, then you should directly use the functions exposed by the backend

qibo/src/qibo/backends/abstract.py

Lines 332 to 335 in e8bca30

    
           def einsum( 
        
               self, subscripts: str, *operands: List[ArrayLike], **kwargs 
        
           ) -> ArrayLike: 
        
               return self.engine.einsum(subscripts, *operands, **kwargs)

In this case it will be backend.einsum(...).

HuberyMing · 2026-04-15T00:01:39Z

@scarrazza @mho291 I am ready for the PR. Now files are merged to one file src/qibo/tomography_RGD/RGD_Optimized.py. This is in a temporary working directory where I also put two test files. To finish the PR, in which place do you suggest to put RGD_Optimized.py? It may be renamed as state_tomography_RGD.py if needed.

Implement PauliMap and RGDOptimizer classes in src/qibo/tomography/state_tomography.py. Add comprehensive tests in tests/test_tomography_pauli_map.py and tests/test_tomography_rgd_optimizer.py. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

HuberyMing · 2026-04-20T07:15:31Z

The quantum state tomography via RGD algorithm is now implemented as one file at src/qibo/tomography/state_tomography.py with two classes PauliMap and RGDOptimizer.
The test files are tests/test_tomography_pauli_map.py and tests/test_tomography_rgd_optimizer.py.
Ready for PR. Thanks.

renatomello mentioned this pull request Oct 14, 2024

Quantum natural gradient qiboteam/qiboml#35

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Add GHZ state to models.encodings #1496

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scarrazza added this to the Qibo 0.2.16 milestone Jan 20, 2025

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	rho = qu.rand_dm_ginibre(2*n, dims=[[2] n, [2] * n], rank=Nr)
	rho = random_density_matrix(2**n, rank=Nr, metric="ginibre")

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