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Merge pull request #240 from nonhermitian/move-solvers
Move solvers to own modules
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,81 @@ | ||
| # This code is part of Mthree. | ||
| # | ||
| # (C) Copyright IBM 2024. | ||
| # | ||
| # This code is licensed under the Apache License, Version 2.0. You may | ||
| # obtain a copy of this license in the LICENSE.txt file in the root directory | ||
| # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. | ||
| # | ||
| # Any modifications or derivative works of this code must retain this | ||
| # copyright notice, and modified files need to carry a notice indicating | ||
| # that they have been altered from the originals. | ||
| # pylint: disable=no-name-in-module, invalid-name | ||
| """Direct solver routines""" | ||
| import scipy.linalg as la | ||
|
|
||
| from mthree.matrix import _reduced_cal_matrix | ||
| from mthree.utils import counts_to_vector, vector_to_quasiprobs | ||
| from mthree.norms import ainv_onenorm_est_lu | ||
| from mthree.exceptions import M3Error | ||
|
|
||
|
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||
| def reduced_cal_matrix(mitigator, counts, qubits, distance=None): | ||
| """Return the reduced calibration matrix used in the solution. | ||
| Parameters: | ||
| counts (dict): Input counts dict. | ||
| qubits (array_like): Qubits on which measurements applied. | ||
| distance (int): Distance to correct for. Default=num_bits | ||
| Returns: | ||
| ndarray: 2D array of reduced calibrations. | ||
| dict: Counts in order they are displayed in matrix. | ||
| Raises: | ||
| M3Error: If bit-string length does not match passed number | ||
| of qubits. | ||
| """ | ||
| counts = dict(counts) | ||
| # If distance is None, then assume max distance. | ||
| num_bits = len(qubits) | ||
| if distance is None: | ||
| distance = num_bits | ||
|
|
||
| # check if len of bitstrings does not equal number of qubits passed. | ||
| bitstring_len = len(next(iter(counts))) | ||
| if bitstring_len != num_bits: | ||
| raise M3Error( | ||
| "Bitstring length ({}) does not match".format(bitstring_len) | ||
| + " number of qubits ({})".format(num_bits) | ||
| ) | ||
|
|
||
| cals = mitigator._form_cals(qubits) | ||
| A, counts, _ = _reduced_cal_matrix(counts, cals, num_bits, distance) | ||
| return A, counts | ||
|
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||
|
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||
| def direct_solver( | ||
| mitigator, counts, qubits, distance=None, return_mitigation_overhead=False | ||
| ): | ||
| """Apply the mitigation using direct LU factorization. | ||
| Parameters: | ||
| counts (dict): Input counts dict. | ||
| qubits (int): Qubits over which to calibrate. | ||
| distance (int): Distance to correct for. Default=num_bits | ||
| return_mitigation_overhead (bool): Returns the mitigation overhead, default=False. | ||
| Returns: | ||
| QuasiDistribution: dict of Quasiprobabilites | ||
| """ | ||
| cals = mitigator._form_cals(qubits) | ||
| num_bits = len(qubits) | ||
| A, sorted_counts, col_norms = _reduced_cal_matrix(counts, cals, num_bits, distance) | ||
| vec = counts_to_vector(sorted_counts) | ||
| LU = la.lu_factor(A, check_finite=False) | ||
| x = la.lu_solve(LU, vec, check_finite=False) | ||
| gamma = None | ||
| if return_mitigation_overhead: | ||
| gamma = ainv_onenorm_est_lu(A, LU) | ||
| out = vector_to_quasiprobs(x, sorted_counts) | ||
| return out, col_norms, gamma |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| # This code is part of Mthree. | ||
| # | ||
| # (C) Copyright IBM 2024. | ||
| # | ||
| # This code is licensed under the Apache License, Version 2.0. You may | ||
| # obtain a copy of this license in the LICENSE.txt file in the root directory | ||
| # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. | ||
| # | ||
| # Any modifications or derivative works of this code must retain this | ||
| # copyright notice, and modified files need to carry a notice indicating | ||
| # that they have been altered from the originals. | ||
| # pylint: disable=no-name-in-module, invalid-name | ||
| """Iterative solver routines""" | ||
| import numpy as np | ||
| import scipy.sparse.linalg as spla | ||
|
|
||
| from mthree.norms import ainv_onenorm_est_iter | ||
| from mthree.matvec import M3MatVec | ||
| from mthree.utils import counts_to_vector, vector_to_quasiprobs | ||
| from mthree.exceptions import M3Error | ||
|
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||
|
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||
| def iterative_solver( | ||
| mitigator, | ||
| counts, | ||
| qubits, | ||
| distance, | ||
| tol=1e-5, | ||
| max_iter=25, | ||
| details=0, | ||
| callback=None, | ||
| return_mitigation_overhead=False, | ||
| ): | ||
| """Compute solution using GMRES and Jacobi preconditioning. | ||
| Parameters: | ||
| counts (dict): Input counts dict. | ||
| qubits (int): Qubits over which to calibrate. | ||
| tol (float): Tolerance to use. | ||
| max_iter (int): Maximum number of iterations to perform. | ||
| distance (int): Distance to correct for. Default=num_bits | ||
| details (bool): Return col norms. | ||
| callback (callable): Callback function to record iteration count. | ||
| return_mitigation_overhead (bool): Returns the mitigation overhead, default=False. | ||
| Returns: | ||
| QuasiDistribution: dict of Quasiprobabilites | ||
| Raises: | ||
| M3Error: Solver did not converge. | ||
| """ | ||
| cals = mitigator._form_cals(qubits) | ||
| M = M3MatVec(dict(counts), cals, distance) | ||
| L = spla.LinearOperator( | ||
| (M.num_elems, M.num_elems), | ||
| matvec=M.matvec, | ||
| rmatvec=M.rmatvec, | ||
| dtype=np.float32, | ||
| ) | ||
| diags = M.get_diagonal() | ||
|
|
||
| def precond_matvec(x): | ||
| out = x / diags | ||
| return out | ||
|
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| P = spla.LinearOperator( | ||
| (M.num_elems, M.num_elems), precond_matvec, dtype=np.float32 | ||
| ) | ||
| vec = counts_to_vector(M.sorted_counts) | ||
|
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||
| out, error = spla.gmres( | ||
| L, | ||
| vec, | ||
| rtol=tol, | ||
| atol=tol, | ||
| maxiter=max_iter, | ||
| M=P, | ||
| callback=callback, | ||
| callback_type="legacy", | ||
| ) | ||
| if error: | ||
| raise M3Error("GMRES did not converge: {}".format(error)) | ||
|
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| gamma = None | ||
| if return_mitigation_overhead: | ||
| gamma = ainv_onenorm_est_iter(M, tol=tol, max_iter=max_iter) | ||
|
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| quasi = vector_to_quasiprobs(out, M.sorted_counts) | ||
| if details: | ||
| return quasi, M.get_col_norms(), gamma | ||
| return quasi, gamma |
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