Method and system for estimating ground state energy of quantum system

An apparatus for estimating ground state energy of a quantum system, comprising:

• a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement:
• 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 by using a convergence condition of the expected energy value of the Hamiltonian as an objective;
• and
• when the expected energy value of the Hamiltonian satisfies the convergence condition, determining the expected energy value of the Hamiltonian satisfying the convergence condition as ground state energy of the target quantum system.

A non-transitory computer-readable storage medium, storing a computer program, the computer program, when loaded and executed by a processor, causing the processor to implement:

• 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 by using a convergence condition of the expected energy value of the Hamiltonian as an objective;
• and
• when the expected energy value of the Hamiltonian satisfies the convergence condition, determining the expected energy value of the Hamiltonian satisfying the convergence condition as ground state energy of the target quantum system.