`pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning`

Module Contents

`pauliX`(qubit_list, coef, t)
`pauliZjZk`(qubit_list, coef, t)
`initial_state`(qubit_list, x)	input x
`ising_model_simulation`(qubit_list, hamiltonian_coef2d, ...)	simulation of Ising model Hamiltonian: H=aiXi+JjkZjZk
`unitary`(qubit, theta)
`one_layer`(qubit_list, theta2d, hamiltonian_coef2d[, ...])	theta2d:[n][3],n is qubit number
`learning_circuit`(qubit_list, layer, theta3d, ...[, t])	qubit_list: qubit list
`get_expectation`(program, qubit_list)
`parity`(number)
`generate_train_data`(type, range)	generate train data

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.pauliX(qubit_list, coef, t)[源代码]

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.pauliZjZk(qubit_list, coef, t)[源代码]

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.initial_state(qubit_list, x)[源代码]: input x

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.ising_model_simulation(qubit_list, hamiltonian_coef2d, step, t)[源代码]: simulation of Ising model Hamiltonian: H=aiXi+JjkZjZk qubit_list: qubit list single_coef: coefficients of Xi,ai[i] double_coef:coefficients of ZjZk, Jjk[j][k]

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.unitary(qubit, theta)[源代码]

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.one_layer(qubit_list, theta2d, hamiltonian_coef2d, step=100, t=10)[源代码]: theta2d:[n][3],n is qubit number hamiltonian_coef2d:[n,n],n is qubit number

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.learning_circuit(qubit_list, layer, theta3d, hamiltonian_coef3d, x, t=10)[源代码]: qubit_list: qubit list theta: parameters to be optimized, [layer,qubit_number,3] hamiltonian_coef:coefficients of fully connected transverse Ising model hamiltonian,[layer,qubit_num,qubit_num], C[i,i]is coefficients of pauli Xi; C[i,j]is coefficients of ZiZj when i>j,C[i,j]=0 when i<j

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.get_expectation(program, qubit_list)[源代码]

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.parity(number)[源代码]

class pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.qcl(qubit_number, layer, coef=1, t=10)[源代码]

optimize(qubit_list, m_qulist, train_data, velocity=0.01)[源代码]: parameter optimization:optimize theta3d and coef

function_learning(qubit_list, train_data, step=1000, velocity=0.01)[源代码]: train quantum circuit

pyqpanda.Algorithm.QuantumCircuitLearning.quantum_circuit_learning.generate_train_data(type, range)[源代码]: generate train data type:function type,include 4 types,1 means x^2,2 means exp(x),3 means sin(x),4 means |x| range:range of variety x