通过近端策略优化进行近似量子态制备
Approximate Quantum State Preparation Through Proximal Policy Optimization
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中文总结 AI 辅助
该研究针对近似量子态制备这一难题，利用基于近端策略优化的深度强化学习，让智能体识别目标状态的最佳近似并减少门的使用，经2至5个量子比特实验，框架能实现10⁻¹⁴的近似误差。
中文摘要 AI 辅助
本文提出了一种用于近似量子态制备（QSP）的量子架构搜索框架。QSP是一项具有挑战性的任务，因为搜索空间随量子比特数量呈指数增长，确定最优电路并非易事。为解决此问题，通过基于近端策略优化的智能体采用深度强化学习。智能体的目标是识别目标状态的最佳近似，同时最小化门的使用数量。在每一步，智能体向电路添加新门并重新计算近似状态与目标状态之间的保真度。对2至5个量子比特进行了各种实验，考虑了预定义状态（如贝尔、GHZ、W和迪克态）以及完全随机状态。所提出的框架能够实现10⁻¹⁴的近似误差。
英文摘要
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 $10^{-14}$.
发表机构
- University of Parma（帕尔马大学）
机构由 AI 辅助整理，请以论文原文为准。
