用强化学习自动合成量子电路，效率远超传统方法。
Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis
- 将电路合成转为基于对称矩阵的强化学习问题，设计可迁移的神经网络。
- 六量子比特下99.2%实例秒级找到最优或近优解，毫秒级生成结果。
- 模型可跨量子比特数泛化，支持达三十量子比特的复杂电路合成。
我们研究全连接量子设备上Clifford量子电路的合成问题。将此任务建模为强化学习问题，智能体通过学习一系列基本Clifford门序列，将给定的对称矩阵表示的Clifford电路简化为单位矩阵。该框架支持基于从单位矩阵出发的随机游走的简单学习课程。我们提出一种新型神经网络架构，具备对量子比特重标号的等变性，并且大小无关，使单一训练策略无需电路拼接或网络重参数即可适用于不同量子比特数量。在六量子比特电路（目前唯一有最优参考的规模）上，该智能体在毫秒内完成每例合成，99.2%的实例可在秒级内找到最优或仅差一个双量子比特门的解。经过十量子比特实例的持续训练后，模型成功扩展至从未见过的最多三十量子比特的Clifford表征，包括由上千个Clifford门生成的目标，其平均双量子比特门数低于Qiskit的Aaronson-Gottesman与贪心合成器。
原文摘要 · Abstract (English)
We consider the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. We approach this task as a reinforcement learning problem in which an agent learns to discover a sequence of elementary Clifford gates that reduces a given symplectic matrix representation of a Clifford circuit to the identity. This formulation permits a simple learning curriculum based on random walks from the identity. We introduce a novel neural network architecture that is equivariant to qubit relabelings of the symplectic matrix representation, and which is size-agnostic, allowing a single learned policy to be applied across different qubit counts without circuit splicing or network reparameterization. On six-qubit Clifford circuits, the largest regime for which optimal references are available, our agent finds circuits within one two-qubit gate of optimality in milliseconds per instance, and finds optimal circuits in 99.2% of instances within seconds per instance. After continued training on ten-qubit instances, the agent scales to unseen Clifford tableaus with up to thirty qubits, including targets generated from circuits with over a thousand Clifford gates, where it achieves lower average two-qubit gate counts than Qiskit's Aaronson-Gottesman and greedy Clifford synthesizers.
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