Neural Network Post-Processing for Quantum Energy Estimation
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Solution Overview
Problem
Current methods for estimating ground state energy of quantum systems, such as VQE with Jastrow factor post-processing, face limitations in accuracy due to weak expression capability, which affects the precision of ground state energy estimation.
Innovation Solution
A method and system utilizing a parameterized quantum circuit and neural network for post-processing, where the neural network acts as a general function approximator to enhance the accuracy of ground state energy estimation by adjusting parameters based on convergence conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Jastrow factor is used for post-processing in VQE, then quantum entanglement and correlation relationships are described more accurately, but the expression capability remains relatively weak which affects ground state energy estimation accuracy
Solution Approach 1:
The patent replaces the traditional Jastrow factor post-processing approach with a neural network-based post-processing system. The neural network acts as a universal function approximator that can capture complex quantum correlations and entanglement relationships more effectively than the Jastrow factor, thereby improving both expression capability and ground state energy estimation accuracy simultaneously
Solution Approach 2:
The patent introduces parameterized quantum circuits with adjustable parameters that can be optimized through convergence conditions. By changing the parameters of both the quantum circuit and neural network, the system achieves better expression capability and more accurate ground state energy estimation, resolving the contradiction between these two aspects
Data Source
AI summary
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.