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| dictionary | 16_qubit_trained_circuit_VQE.config |
| |
| | 16_qubit_trained_circuit_VQE.eigval = np.real(eigvals[0]) |
| |
| | 16_qubit_trained_circuit_VQE.eigvec = eigvecs[:,0] |
| |
| | 16_qubit_trained_circuit_VQE.entropy = VQE_Heisenberg.get_Second_Renyi_Entropy( parameters=parameters, qubit_list=qubit_list ) |
| |
| | 16_qubit_trained_circuit_VQE.entropy_exact_gs = VQE_Heisenberg.get_Second_Renyi_Entropy( parameters=np.array([]), qubit_list=[0,1], input_state=eigvec ) |
| |
| | 16_qubit_trained_circuit_VQE.flush |
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| def | 16_qubit_trained_circuit_VQE.Hamiltonian = generate_hamiltonian_tmp( qbit_num ) |
| |
| | 16_qubit_trained_circuit_VQE.initial_state = np.zeros( (1 << qbit_num), dtype=np.complex128 ) |
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| int | 16_qubit_trained_circuit_VQE.inner_blocks = 1 |
| |
| | 16_qubit_trained_circuit_VQE.k |
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| int | 16_qubit_trained_circuit_VQE.layers = 1000 |
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| | 16_qubit_trained_circuit_VQE.linewidth |
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| int | 16_qubit_trained_circuit_VQE.normalized_entropy = entropy/page_entropy |
| |
| int | 16_qubit_trained_circuit_VQE.normalized_entropy_exact_gs = entropy_exact_gs/page_entropy |
| |
| | 16_qubit_trained_circuit_VQE.overlap = state_to_transform.transpose().conjugate() @ eigvecs |
| |
| | 16_qubit_trained_circuit_VQE.overlap_norm = np.real(overlap * overlap.conjugate()) |
| |
| int | 16_qubit_trained_circuit_VQE.page_entropy = 2 * np.log(2.0) - 1.0/( pow(2, qbit_num-2*2+1) ) |
| |
| | 16_qubit_trained_circuit_VQE.param_num = VQE_Heisenberg.get_Parameter_Num() |
| |
| | 16_qubit_trained_circuit_VQE.parameters = np.load( 'COSINE_1000x1_layers_Heisenberg_16_3point_zero_init_3overlap0.8602661822824491.npy' ) |
| |
| int | 16_qubit_trained_circuit_VQE.qbit_num = 16 |
| |
| list | 16_qubit_trained_circuit_VQE.qubit_list = [0,1] |
| |
| def | 16_qubit_trained_circuit_VQE.squander_circuit = generate_circuit_ansatz( layers, inner_blocks, qbit_num) |
| |
| | 16_qubit_trained_circuit_VQE.state_to_transform = initial_state.copy() |
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| list | 16_qubit_trained_circuit_VQE.topology = [] |
| |
| | 16_qubit_trained_circuit_VQE.VQE_energy = VQE_Heisenberg.Optimization_Problem( parameters ) |
| |
| | 16_qubit_trained_circuit_VQE.VQE_Heisenberg = Variational_Quantum_Eigensolver(Hamiltonian, qbit_num, config) |
| |
| | 16_qubit_trained_circuit_VQE.which |
| |
