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arXiv 2412.04053quant-ph
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通过强化学习增强量子比特读出

Enhanced Qubit Readout via Reinforcement Learning

Aniket Chatterjee, Jonathan Schwinger, Yvonne Y. Gao

AI总结:

本研究利用无模型强化学习优化超导量子比特读出,实现低误差(4.6e-3)且速度提升近三倍,波形鲁棒且易于实现,显著提升量子处理器性能。

摘要:

测量是稳健且实用的量子计算的基本组成部分。对于超导量子比特,测量过程涉及对量子比特-谐振子联合动力学的有效操控,理想情况下应以最短的读出脉冲和谐振子重置时间提供最高质量的量子比特态判别。在此,我们利用无模型强化学习(RL)结合定制的训练环境来实现这一多方面的优化任务。使用IBM量子设备,我们证明了由RL智能体获得的脉冲不仅成功实现了最先进的性能,分配误差为$(4.6 \pm 0.4)\times10^{-3}$,而且其读出及随后的谐振子重置速度比系统默认过程快近三倍。此外,学习到的波形对实际参数漂移具有鲁棒性,并遵循广义解析形式,使其在实际中易于实现且无显著计算开销。我们的结果为提升超导量子处理器的性能提供了有效的读出策略,并展示了RL在为复杂量子信息处理任务提供最优且基于实验信息的解决方案方面的能力。

英文摘要:

Measurement is an essential component of robust and practical quantum computation. For superconducting qubits, the measurement process involves the effective manipulation of the joint qubit-resonator dynamics, and it should ideally provide the highest quality for qubit state discrimination with the shortest readout pulse and resonator reset time. Here, we harness model-free reinforcement learning (RL), together with a tailored training environment, to achieve this multi-pronged optimization task. Using the IBM quantum device, we demonstrate that the pulse obtained by the RL agent not only successfully achieves state-of-the-art performance, with an assignment error of $(4.6 \pm 0.4)\times10^{-3}$, but also executes the readout and the subsequent resonator reset almost three times faster than the system's default process. Furthermore, the learned waveforms are robust against realistic parameter drifts and follow a generalized analytical form, making them readily implementable in practice with no significant computation overhead. Our results provide an effective readout strategy to boost the performance of superconducting quantum processors and demonstrate the prowess of RL in providing optimal and experimentally informed solutions for complex quantum information processing tasks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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