混合数字-模拟量子计算，让现有量子设备高效运行机器学习任务。
Digital-Analog Quantum Machine Learning
- 结合连续模拟演化与离散量子门操作，提升计算效率。
- 无需容错量子计算机，可在当前硬件上实现有效机器学习。
- 适合研究量子算法加速与现有量子设备应用的学者。
机器学习算法广泛应用于工业和社会各领域，使设备能从经验中学习并提升性能。然而，海量数据对经典计算设备构成挑战。量子系统可能提供解决方案，但在短期内难以规模化，受限于退相干和量子叠加态的脆弱性。近期研究表明，融合大块模拟演化与离散量子门的数字-模拟量子范式，在无需容错量子计算机的前提下，可有效探索经典与量子系统的知识。本文综述了若干近期工作，展示该范式如何在现有量子设备上实现高效的机器学习计算。
原文摘要 · Abstract (English)
Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been made accessible. However, dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts. On the other hand, quantum systems themselves are also hard to scale up, due to decoherence and the fragility of quantum superpositions. In the short and mid term, it has been evidenced that a quantum paradigm that combines evolution under large analog blocks with discrete quantum gates, may be fruitful to achieve new knowledge of classical and quantum systems with no need of having a fault-tolerant quantum computer. In this Perspective, we review some recent works that employ this digital-analog quantum paradigm to carry out efficient machine learning calculations with current quantum devices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。