Abstract
Variational quantum algorithms hold the promise to address meaningful quantum problems already on noisy intermediate-scale quantum hardware, but they face the challenge of designing quantum circuits that both solve the target problem and comply with device limitations. Quantum architecture search (QAS) automates this design process, with reinforcement learning (RL) emerging as a promising approach. Yet, RL-based QAS methods encounter significant scalability issues, as computational and training costs grow rapidly with the number of qubits, circuit depth, and noise, severely impacting performance. To address these challenges, we introduce $\textit{TensorRL-QAS}$, a scalable framework that combines tensor network (TN) methods with RL for designing quantum circuits. By warm-starting the architecture search with a matrix product state approximation of the target solution, TensorRL-QAS effectively narrows the search space to physically meaningful circuits, accelerating convergence to the desired solution. Tested on several quantum chemistry problems of up to 12-qubit, TensorRL-QAS achieves up to a 10-fold reduction in CNOT count and circuit depth compared to baseline methods, while maintaining or surpassing chemical accuracy. It reduces function evaluations by up to 100-fold, accelerates training episodes by up to $98\%$, and achieves up to $50\%$ success probability for 10-qubit systems-far exceeding the $<1\%$ rates of baseline approaches. Robustness and versatility are demonstrated both in the noiseless and noisy scenarios, where we report a simulation of up to 8-qubit. These advancements establish TensorRL-QAS as a promising candidate for a scalable and efficient quantum circuit discovery protocol on near-term quantum hardware.
Abstract (translated)
变分量子算法有望在嘈杂的中等规模量子硬件上解决有意义的量子问题，但它们面临着设计既能解决问题又能符合设备限制的量子电路这一挑战。量子架构搜索（QAS）自动化了这个设计过程，强化学习（RL）作为一种有前景的方法正在兴起。然而，基于RL的QAS方法遇到了显著的可扩展性问题，随着量子比特数量、电路深度和噪声水平的增长，计算和训练成本急剧上升，严重影响了性能。为了解决这些挑战，我们引入了**TensorRL-QAS**，这是一个结合张量网络（TN）方法与强化学习来设计量子电路的可扩展框架。 通过使用目标解的矩阵乘积态近似进行架构搜索的热启动，TensorRL-QAS有效地缩小了物理意义明确的电路的搜索空间，加速收敛到所需解决方案。在几个多达12个量子比特的量子化学问题上测试后，与基准方法相比，TensorRL-QAS实现了高达十倍的CNOT门计数和电路深度减少，同时保持或超越了化学精度。它减少了功能评估次数高达一百倍，训练轮次加速至多98%，并为10个量子比特系统达到了50%的成功概率——远超基准方法不到1%的比率。 在无噪声与有噪声的情景中均证明了其鲁棒性和灵活性，在这些情景下，我们报告了多达8个量子比特的模拟。这些进展使TensorRL-QAS成为未来近期内用于大规模和高效发现量子电路协议的一个很有前景的选择。
URL
https://arxiv.org/abs/2505.09371
