Abstract: A method of a quantum circuit simulation includes receiving a primitive function for the quantum circuit simulation, and determining at least a first input parameter of the primitive function. The quantum circuit simulation includes a plurality of first tensors respectively for the first input parameter. The method includes converting the primitive function to a target function according to the primitive function and the at least the first input parameter. The target function includes a converted first input parameter corresponding to the first input parameter, the plurality of first tensors are spliced into a second tensor for the converted first input parameter in the quantum circuit simulation. The method further includes obtaining an execution result of the target function according to at least the second tensor for the converted first input parameter, and performing the quantum circuit simulation based on the execution result of the target function.

Abstract: An expectation value estimation method for a quantum system includes: acquiring an output quantum state of n qubits obtained from an input quantum state of the n qubits through a noisy parameterized quantum circuit (PQC); post-processing the output quantum state of the n qubits by using a neural network to obtain expectation values of a plurality of Pauli strings which are obtained by decomposing a target function; calculating an expectation value of the target function under the output quantum state of the n qubits according to the expectation values of the plurality of Pauli strings; adjusting at least a parameter of the neural network until the expectation value of the target function converges; and acquiring, when the expectation value of the target function satisfies a convergence condition, a convergent expectation value of the target function, the convergent expectation value being an expectation value that satisfies the convergence condition.

Abstract: A method includes: performing transformation processing on input quantum states of n qubits through a parameterized quantum circuit, to output quantum states of the n qubits, an expected energy value of Hamiltonian of a target quantum system in the output quantum states of the n qubits being a summation result of expected energy values of k Pauli strings obtained by decomposing the Hamiltonian, n and k being positive integers; performing post-processing on the output quantum states of the n qubits by using a neural network, and obtaining the expected energy value of the Hamiltonian by calculating a post-processing result of the neural network; adjusting parameters of the parameterized quantum circuit and parameters of the neural network while aiming to converge the expected energy value of the Hamiltonian; and determining the expected energy value of the Hamiltonian satisfying the convergence condition as ground state energy of the target quantum system.