噪声量子计算机与可扩展经典深度学习之间的协同作用,实现量子错误缓解

IF 5.8 2区 物理与天体物理 Q1 OPTICS
Simone Cantori, Andrea Mari, David Vitali, Sebastiano Pilati
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引用次数: 0

摘要

我们研究了将噪声量子计算机的计算能力与经典可扩展卷积神经网络(CNN)的计算能力相结合的潜力。我们的目标是准确预测代表量子伊辛模型特罗特分解动力学的参数化量子电路的精确期望值。通过将(模拟的)噪声期望值与电路结构信息结合起来,我们的 CNNs 有效地捕捉到了电路结构与输出行为之间的潜在关系,通过迁移学习,还能预测比训练集所包含的量子比特更多的电路。值得注意的是,得益于量子信息,即使仅基于经典描述符的监督学习失败,我们的 CNN 也能取得成功。此外,它们的表现优于一种流行的误差缓解方案,即零噪声外推法,这表明量子计算工具与经典计算工具之间的协同作用能带来比纯量子或纯经典方法更高的准确性。通过调整噪声强度,我们探索了从由量子噪声数据辅助的计算能力强大的经典 CNN 到相当精确的量子计算,再通过经典深度学习进行误差缓解的交叉过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synergy between noisy quantum computers and scalable classical deep learning for quantum error mitigation

We investigate the potential of combining the computational power of noisy quantum computers and of classical scalable convolutional neural networks (CNNs). The goal is to accurately predict exact expectation values of parameterized quantum circuits representing the Trotter-decomposed dynamics of quantum Ising models. By incorporating (simulated) noisy expectation values alongside circuit structure information, our CNNs effectively capture the underlying relationships between circuit architecture and output behaviour, enabling, via transfer learning, also predictions for circuits with more qubits than those included in the training set. Notably, thanks to the quantum information, our CNNs succeed even when supervised learning based only on classical descriptors fails. Furthermore, they outperform a popular error mitigation scheme, namely, zero-noise extrapolation, demonstrating that the synergy between quantum and classical computational tools leads to higher accuracy compared with quantum-only or classical-only approaches. By tuning the noise strength, we explore the crossover from a computationally powerful classical CNN assisted by quantum noisy data, towards rather precise quantum computations, further error-mitigated via classical deep learning.

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来源期刊
EPJ Quantum Technology
EPJ Quantum Technology Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
7.70
自引率
7.50%
发文量
28
审稿时长
71 days
期刊介绍: Driven by advances in technology and experimental capability, the last decade has seen the emergence of quantum technology: a new praxis for controlling the quantum world. It is now possible to engineer complex, multi-component systems that merge the once distinct fields of quantum optics and condensed matter physics. EPJ Quantum Technology covers theoretical and experimental advances in subjects including but not limited to the following: Quantum measurement, metrology and lithography Quantum complex systems, networks and cellular automata Quantum electromechanical systems Quantum optomechanical systems Quantum machines, engineering and nanorobotics Quantum control theory Quantum information, communication and computation Quantum thermodynamics Quantum metamaterials The effect of Casimir forces on micro- and nano-electromechanical systems Quantum biology Quantum sensing Hybrid quantum systems Quantum simulations.
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