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arXiv 2408.03376quant-ph
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大规模量子过程的纠缠增强学习

Entanglement-enhanced learning of quantum processes at scale

IBM Quantum, IBM T.J. Watson Research Center(IBM量子,IBM托马斯·J·沃森研究中心);Pritzker School of Molecular Engineering, University of Chicago(芝加哥大学普里茨克分子工程学院);Department of Physics, University of California, Berkeley(加州大学伯克利分校物理系)

Alireza Seif, Senrui Chen, Swarnadeep Majumder, Haoran Liao, Derek S. Wang, Moein Malekakhlagh, Ali Javadi-Abhari, Liang Jiang, Zlatko K. Minev

AI总结:

针对量子过程学习测量复杂度高、实际量子存储器含噪声导致优势不明的问题,提出误差缓解的纠缠增强学习方法,经理论与超导量子处理器实验验证,能以更低开销高效学习量子过程。

摘要:

学习影响量子系统的未知过程可揭示其底层物理机制,并实现对不良效应的抑制、缓解与校正。描述一个通用量子过程需要指数级数量的参数。当这些参数编码在不相容可观测量中时,对其的测量受不确定性原理约束,且需要指数级次数的测量。然而,对于Pauli信道(泡利信道),若能使用理想量子存储器和纠缠操作,便可将参数编码在对易可观测量中,从而指数级降低测量复杂度。但在实际应用中,量子存储器和纠缠操作总会存在噪声并引入误差,这使得使用含噪声量子存储器的优势尚不明确。为应对这些挑战,我们提出了误差缓解的纠缠增强学习方法,并从理论和实验两方面证明:即便存在噪声,使用含噪声量子存储器的纠缠学习Pauli信道与不使用纠缠的学习方式在效率上仍存在差异。我们在多个示例中验证了该协议的有效性,包括最高64量子比特的假设检验,以及在超导量子处理器上使用最高16量子比特学习并行门层中的固有噪声过程。我们的协议能提供关于量子过程的准确且实用的信息,每个量子比特的开销因子为$1.33 \u00b1 0.05$,远小于无量子存储器纠缠时的2这一基本下界。本研究表明,辅助含噪声量子存储器的纠缠与误差缓解相结合,可显著提升量子过程的学习效果。

英文摘要:

Learning unknown processes affecting a quantum system reveals underlying physical mechanisms and enables suppression, mitigation, and correction of unwanted effects. Describing a general quantum process requires an exponentially large number of parameters. Measuring these parameters, when they are encoded in incompatible observables, is constrained by the uncertainty principle and requires exponentially many measurements. However, for Pauli channels, having access to an ideal quantum memory and entangling operations allows encoding parameters in commuting observables, thereby exponentially reducing measurement complexity. In practice, though, quantum memory and entangling operations are always noisy and introduce errors, making the advantage of using noisy quantum memory unclear. To address these challenges we introduce error-mitigated entanglement-enhanced learning and show, both theoretically and experimentally, that even with noise, there is a separation in efficiency between learning Pauli channels with and without entanglement with noisy quantum memory. We demonstrate our protocol's efficacy in examples including hypothesis testing with up to 64 qubits and learning inherent noise processes in a layer of parallel gates using up to 16 qubits on a superconducting quantum processor. Our protocol provides accurate and practical information about the process, with an overhead factor of $1.33 \pm 0.05$ per qubit, much smaller than the fundamental lower bound of 2 without entanglement with quantum memory. Our study demonstrates that entanglement with auxiliary noisy quantum memory combined with error mitigation considerably enhances the learning of quantum processes.

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