Add binary_encoder parameterized in hyperspherical coordinates
#1584
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renatomello
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## master #1584 +/- ##
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renatomello
requested review from
stavros11,
BrunoLiegiBastonLiegi and
MatteoRobbiati
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Run on QPU
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Co-authored-by: Alejandro Sopena <44305203+AlejandroSopena@users.noreply.github.com>
Co-authored-by: Alejandro Sopena <44305203+AlejandroSopena@users.noreply.github.com>
| @@ -668,6 +674,8 @@ def _generate_rbs_angles(data, nqubits: int, architecture: str): | |||
| r_array[j - 1] = math.sqrt(r_array[2 * j] ** 2 + r_array[2 * j - 1] ** 2) | |||
| phases[j - 1] = math.acos(r_array[2 * j - 1] / r_array[j - 1]) | |||
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| phases = np.array([float(phase) for phase in phases]) | |||
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is there any specific reason why the phases are not returned as backend specific arrays? (thus with the usual backend.cast)
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all functions in this module are backend-agnostic as of right now. If we were to introduce the backend as an argument, I'd prefer to do it in all encodings are once on a separate PR.
| for weight in range(1, nqubits): | ||
| n_choose_k = int(binom(nqubits, weight)) | ||
| cummul_n_k += n_choose_k | ||
| placeholder = np.random.rand(n_choose_k) |
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not sure how big n_choose_k can get, but just as a reminder generating samples with numba and cupy should be faster if it's big.
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The size is the regular binomial coefficient, tending to
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| cummul_n_k = 0 | ||
| initial_string = np.array([1] + [0] * (nqubits - 1)) | ||
| for weight in range(1, nqubits): |
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This is a very big loop, it might be clearer to make it as separate function:
for weight in range(1, nqubits):
separate_function(...)Co-authored-by: BrunoLiegiBastonLiegi <45011234+BrunoLiegiBastonLiegi@users.noreply.github.com>
for more information, see https://pre-commit.ci
Co-authored-by: BrunoLiegiBastonLiegi <45011234+BrunoLiegiBastonLiegi@users.noreply.github.com>
This is based on Section V of https://arxiv.org/abs/2405.20408 and it works for real- and complex-valued data.
Checklist: