コード例 #1
0
    def test_hhl_non_hermitian(self):
        self.log.debug('Testing HHL with simple non-hermitian matrix')

        nonherm_params = self.params
        nonherm_params['eigs']['num_ancillae'] = 6
        nonherm_params['eigs']['num_time_slices'] = 80
        nonherm_params['eigs']['negative_evals'] = True
        nonherm_params['reciprocal']['negative_evals'] = True

        matrix = [[1, 1], [2, 1]]
        vector = [1, 0]

        algo_input = LinearSystemInput()
        algo_input.matrix = matrix
        algo_input.vector = vector

        # run ExactLSsolver
        ref_result = run_algorithm(self.els_params, algo_input)
        ref_solution = ref_result['solution']
        ref_normed = ref_solution/np.linalg.norm(ref_solution)
        # run hhl
        hhl_result = run_algorithm(nonherm_params, algo_input)
        hhl_solution = hhl_result['solution']
        hhl_normed = hhl_solution/np.linalg.norm(hhl_solution)
        # compare result
        fidelity = state_fidelity(ref_normed, hhl_normed)
        self.assertGreater(fidelity, 0.8)

        self.log.debug('HHL solution vector:       {}'.format(hhl_solution))
        self.log.debug('algebraic solution vector: {}'.format(ref_solution))
        self.log.debug('fidelity HHL to algebraic: {}'.format(fidelity))
        self.log.debug('probability of result:     {}'.format(hhl_result["probability_result"]))
コード例 #2
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    def test_hhl_random_hermitian(self):
        self.log.debug('Testing HHL with random hermitian matrix')

        hermitian_params = self.params
        hermitian_params['eigs']['num_ancillae'] = 4

        n = 2
        np.random.seed(0)
        matrix = rmg.random_hermitian(n, eigrange=[0, 1])
        vector = random(n)

        algo_input = LinearSystemInput()
        algo_input.matrix = matrix
        algo_input.vector = vector

        # run ExactLSsolver
        ref_result = run_algorithm(self.els_params, algo_input)
        ref_solution = ref_result['solution']
        ref_normed = ref_solution/np.linalg.norm(ref_solution)
        # run hhl
        hhl_result = run_algorithm(hermitian_params, algo_input)
        hhl_solution = hhl_result['solution']
        hhl_normed = hhl_solution/np.linalg.norm(hhl_solution)

        # compare result
        fidelity = state_fidelity(ref_normed, hhl_normed)
        np.testing.assert_approx_equal(fidelity, 1, significant=2)

        self.log.debug('HHL solution vector:       {}'.format(hhl_solution))
        self.log.debug('algebraic solution vector: {}'.format(ref_normed))
        self.log.debug('fidelity HHL to algebraic: {}'.format(fidelity))
        self.log.debug('probability of result:     {}'.format(hhl_result["probability_result"]))
コード例 #3
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    def test_hhl_negative_eigs(self):
        self.log.debug('Testing HHL with matrix with negative eigenvalues')

        neg_params = self.params
        neg_params['eigs']['num_ancillae'] = 4
        neg_params['eigs']['negative_evals'] = True
        neg_params['reciprocal']['negative_evals'] = True

        n = 2
        np.random.seed(0)
        matrix = rmg.random_diag(n, eigrange=[-1, 1])
        vector = random(n)

        algo_input = LinearSystemInput()
        algo_input.matrix = matrix
        algo_input.vector = vector

        # run ExactLSsolver
        ref_result = run_algorithm(self.els_params, algo_input)
        ref_solution = ref_result['solution']
        ref_normed = ref_solution/np.linalg.norm(ref_solution)
        # run hhl
        hhl_result = run_algorithm(neg_params, algo_input)
        hhl_solution = hhl_result["solution"]
        hhl_normed = hhl_solution/np.linalg.norm(hhl_solution)

        # compare results
        fidelity = state_fidelity(ref_normed, hhl_normed)
        np.testing.assert_approx_equal(fidelity, 1, significant=3)

        self.log.debug('HHL solution vector:       {}'.format(hhl_solution))
        self.log.debug('algebraic solution vector: {}'.format(ref_normed))
        self.log.debug('fidelity HHL to algebraic: {}'.format(fidelity))
        self.log.debug('probability of result:     {}'.format(hhl_result["probability_result"]))
コード例 #4
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    def test_hhl_diagonal_other_dim(self, n, num_ancillary):
        self.log.debug('Testing HHL with matrix dimension other than 2**n')

        dim_params = self.params
        dim_params['eigs']['num_ancillae'] = num_ancillary
        dim_params['eigs']['negative_evals'] = True
        dim_params['reciprocal']['negative_evals'] = True

        np.random.seed(0)
        matrix = rmg.random_diag(n, eigrange=[0, 1])
        vector = random(n)

        algo_input = LinearSystemInput()
        algo_input.matrix = matrix
        algo_input.vector = vector

        # run ExactLSsolver
        ref_result = run_algorithm(self.els_params, algo_input)
        ref_solution = ref_result['solution']
        ref_normed = ref_solution/np.linalg.norm(ref_solution)
        # run hhl
        hhl_result = run_algorithm(dim_params, algo_input)
        hhl_solution = hhl_result['solution']
        hhl_normed = hhl_solution/np.linalg.norm(hhl_solution)

        # compare result
        fidelity = state_fidelity(ref_normed, hhl_normed)
        np.testing.assert_approx_equal(fidelity, 1, significant=1)

        self.log.debug('HHL solution vector:       {}'.format(hhl_solution))
        self.log.debug('algebraic solution vector: {}'.format(ref_solution))
        self.log.debug('fidelity HHL to algebraic: {}'.format(fidelity))
        self.log.debug('probability of result:     {}'.format(hhl_result["probability_result"]))
コード例 #5
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ファイル: test_hhl.py プロジェクト: pistoia/qiskit-aqua
    def test_hhl_random_hermitian_sv(self):
        self.log.debug('Testing HHL with random hermitian matrix')

        hermitian_params = self.params
        hermitian_params['eigs']['num_ancillae'] = 4

        n = 2
        matrix = rmg.random_hermitian(n, eigrange=[0, 1])
        vector = random(2)

        algo_input = LinearSystemInput()
        algo_input.matrix = matrix
        algo_input.vector = vector

        # run hhl
        result = run_algorithm(hermitian_params, algo_input)
        hhl_solution = result["solution_hhl"]
        hhl_normed = hhl_solution / np.linalg.norm(hhl_solution)
        # linear algebra solution
        linalg_solution = np.linalg.solve(matrix, vector)
        linalg_normed = linalg_solution / np.linalg.norm(linalg_solution)

        # compare result
        fidelity = abs(linalg_normed.dot(hhl_normed.conj()))**2
        np.testing.assert_approx_equal(fidelity, 1, significant=2)

        self.log.debug('HHL solution vector:       {}'.format(hhl_solution))
        self.log.debug('algebraic solution vector: {}'.format(linalg_solution))
        self.log.debug('fidelity HHL to algebraic: {}'.format(fidelity))
        self.log.debug('probability of result:     {}'.format(
            result["probability_result"]))
コード例 #6
0
ファイル: test_hhl.py プロジェクト: pistoia/qiskit-aqua
    def test_hhl_negative_eigs_sv(self):
        self.log.debug('Testing HHL with matrix with negative eigenvalues')

        neg_params = self.params
        neg_params['eigs']['num_ancillae'] = 4
        neg_params['eigs']['negative_evals'] = True
        neg_params['reciprocal']['negative_evals'] = True

        n = 2
        matrix = rmg.random_diag(n, eigrange=[-1, 1])
        vector = random(2)

        algo_input = LinearSystemInput()
        algo_input.matrix = matrix
        algo_input.vector = vector

        # run hhl
        result = run_algorithm(neg_params, algo_input)
        hhl_solution = result["solution_hhl"]
        hhl_normed = hhl_solution / np.linalg.norm(hhl_solution)
        # linear algebra solution
        linalg_solution = np.linalg.solve(matrix, vector)
        linalg_normed = linalg_solution / np.linalg.norm(linalg_solution)

        # compare result
        fidelity = abs(linalg_normed.dot(hhl_normed.conj()))**2
        np.testing.assert_approx_equal(fidelity, 1, significant=3)

        self.log.debug('HHL solution vector:       {}'.format(hhl_solution))
        self.log.debug('algebraic solution vector: {}'.format(linalg_solution))
        self.log.debug('fidelity HHL to algebraic: {}'.format(fidelity))
        self.log.debug('probability of result:     {}'.format(
            result["probability_result"]))