发表机构
Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC; Theoretical Division, Los Alamos National Laboratory; USRA Research Institute for Advanced Computer Science (RIACS); NEST, Scuola Normale Superiore; D-Wave Quantum; New Mexico Consortium（跨学科物理与复杂系统研究所（IFISC） UIB-CSIC; 理论部，洛斯阿拉莫斯国家实验室; 美国大学空间研究协会先进计算机科学研究院（RIACS）; NEST，比萨高等师范学院; D-Wave量子; 新墨西哥联盟）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究在4,500量子比特的量子退火器上实现大规模量子机器学习模型，利用反向退火产生的多体动力学处理时间数据，证明相互作用不可或缺，并成功预测混沌时间序列。
AI 中文摘要
量子机器学习可能揭示超出经典模型范围的统计结构，但这要求量子模型足够大且具有足够的表达能力以发挥作用，并且读取成本足够低。大多数方法优化大量量子参数，因此受到昂贵训练循环的限制。在此，我们报告了一个在可编程超导量子退火器上实现的量子机器学习模型，该模型使用多达4,500个量子比特大规模处理时间数据——这是迄今为止进行的最大规模量子机器学习实验。遵循量子储层计算范式，由反向退火产生的未训练的本征多体动力学被直接用于处理时间数据。我们证明了退火过程中产生的相互作用是不可或缺的——非相互作用的储层对其输入没有记忆。我们在标准记忆基准上实验评估了我们的模型，并证明它可以成功预测混沌时间序列。这些结果确立了量子退火器作为大规模量子机器学习的可扩展平台。
英文摘要
Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and cheap enough to read out. Most approaches optimize many quantum parameters and are thus limited by expensive training loops. Here we report a quantum machine-learning model implemented on a programmable superconducting quantum annealer that processes temporal data at large scale using up to 4,500 qubits-the largest quantum machine-learning experiment performed to date. Following the quantum reservoir computing paradigm, the untrained native many-body dynamics generated by reverse annealing is directly used to process temporal data. We prove that the interactions produced during annealing are indispensable-a non-interacting reservoir retains no memory of its input. We evaluate our model experimentally on standard memory benchmarks and demonstrate that it can successfully forecast chaotic time series. These results establish quantum annealers as a scalable platform for large-scale quantum machine learning.
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