发表机构
K.K. Wagh Institute of Engineering Education and Research（K.K. Wagh工程教育与研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出TATVA强化学习框架，将量子电路综合视为序列决策问题，结合DQN和PPO并行架构，实现5量子比特、保真度0.999999的紧凑电路合成，并支持泛化。
AI 中文摘要
量子计算中最重要的初始步骤之一是高保真量子态制备，因为其中的错误会影响最终的计算结果。因此，主要挑战之一是如何设计一个能够以较少的门数准确实现所需目标态的量子电路。传统上，电路是通过预定义规则和数学分解来设计的，但这使得为不同且复杂的目标态设计电路变得困难。本文提出了TATVA，一个用于综合量子电路的强化学习（RL）系统。TATVA将电路综合视为一个序列决策问题，其中智能体每次选择一个门，并在每个门应用后计算保真度，选择产生最高保真度的门用于电路。该系统引入了一种并行架构，使用两个强化学习智能体，即深度Q网络（DQN）和近端策略优化（PPO），以及Qiskit的态向量模拟器。该系统可以综合多达5个量子比特，同时实现0.999999的保真度并生成紧凑的电路。在达到所需保真度后，它执行一个事后优化步骤，在保持所达到的保真度的同时减少电路深度。电路最终通过保真度、成功率、门数和电路深度来评估。训练中未使用的目标态用于测试系统是否有效学习并能够泛化到训练态之外。该方法旨在利用强化学习实现高保真度的自动量子电路综合。
英文摘要
One of the most important initial steps in quantum computing is high-fidelity quantum state preparation, because errors in it can affect the final computational result. Therefore, one of the major challenges is to design a quantum circuit that can achieve the desired target state accurately with a smaller number of gates. Traditionally, circuits are designed using predefined rules and mathematical decompositions, but this makes it difficult to design circuits for different and complex target states. This paper presents TATVA, a reinforcement learning (RL) system for synthesizing quantum circuits. TATVA views circuit synthesis as a sequential decision problem in which the agents select one gate at a time and calculate the fidelity after each applied gate, with the gate producing the highest fidelity selected for the circuit. The system introduces a parallel architecture that uses two RL agents, Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), along with Qiskit's statevector simulator. The system can synthesize up to 5 qubits while achieving a fidelity of 0.999999 and producing compact circuits. After achieving the desired fidelity, it performs a post-hoc optimization step that reduces the circuit depth while preserving the achieved fidelity. The circuit is finalized using fidelity, success rate, gate count, and circuit depth. Target states that are not used during training are used to test whether the system has learned effectively and can generalize beyond the training states. This approach aims to enable automatic quantum circuit synthesis with high fidelity using reinforcement learning.
Comments9 pages, 10 figures
