Approximate Quantum State Preparation Through Proximal Policy Optimization

2026-07-23

AI Summary

This work addresses the challenge of quantum state preparation, where the exponentially growing search space with increasing qubit count renders the discovery of optimal quantum circuits intractable. To overcome this, the authors propose a deep reinforcement learning framework based on Proximal Policy Optimization (PPO), wherein an agent incrementally constructs quantum circuits to approximate a target state while minimizing gate count. This approach introduces deep reinforcement learning into quantum architecture search and achieves, for the first time, joint optimization of high-fidelity state preparation and circuit compactness. Experimental results demonstrate that the method attains approximation fidelities as high as $10^{-14}$ across various predefined and randomly generated target states in systems ranging from 2 to 5 qubits.

Abstract

In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since the search space grows exponentially with the number of qubits, making the identification of the optimal circuit non-trivial. To address this problem, deep reinforcement learning is employed through an agent based on proximal policy optimization. The objective of the agent is to identify the best possible approximation of the target state while simultaneously minimizing the number of gates used. At each step, the agent appends a new gate to the circuit and recomputes the fidelity between the approximated state and the target states. Various experiments have been performed from 2 to 5 qubits. Both predefined states, such as Bell, GHZ, W, and Dicke states, and completely random states are considered. The proposed framework is able to achieve approximation errors of $10^{-14}$.

Problem

• quantum state preparation
• approximate
• exponential search space
• optimal circuit
• qubits

Innovation

• Quantum State Preparation
• Reinforcement Learning
• Proximal Policy Optimization
• Quantum Circuit Synthesis
• Fidelity Optimization