Spatial Quantum Optical Annealer for Spin Hamiltonians

自旋哈密顿量的空间量子光学退火器

• 批准号: 10086022
• 金额: $ 59.53万
• 依托单位: UNIVERSITY OF CAMBRIDGE
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10086022
• 关键词: Spatial; Quantum; Optical; Annealer; Spin

Optical simulators rank among the most promising candidates to power future technological breakthroughs in terms of speed, scalability, power-consumption and quantum advantage, serving a wide range of useful optimization problems. However, the operation of such simulators remains currently limited by noise, the extent of algorithmic problems they can embed and to the classical regime where they compete with supercomputers. HEISINGBERG aims to bring our state-of-the-art spatial photonic spin simulator (an iterated cycle of all-optical processing through a spatial light modulator that couples 10,000 spins) into the quantum regime by upgrading its coherent drive to squeezed light, making it fully programmable through vector-matrix multiplication schemes, use of holography, ancillary spins & effective magnetic fields, and designing dedicated custom-tailored and purpose-built algorithms. The reduced fluctuations in one quadrature of the fields will allow us to scale up and optimize the performances of the existing machine to bring it beyond the capabilities of both classical supercomputers and competing spin-simulators. HEISINGBERG devices will operate 100,000 spins at room temperature and process new quantum annealing algorithms on an improved XY architecture. Besides, the nonclassical resources of squeezed states when modulated, admixed and phase-controlled through beam splitters, such as entanglement or superpositions of multiphoton states will be prospected to harness a quantum advantage and boost our machine into its quantum simulation regime. This development will stimulate the quantum information processing community by concretely articulating problems of algorithmic complexity and clarify the nature of the quantum advantage available in annealers and simulators. These advances will allow us to demonstrate, on a cloud platform, annealing and adiabatic algorithms that can efficiently solve NP-hard problems.

在速度、可扩展性、功耗和量子优势方面，光学模拟器是最有希望为未来技术突破提供动力的候选者之一，可以解决各种有用的优化问题。然而，这种模拟器的操作目前仍然受到噪声、它们可以嵌入的算法问题的程度以及它们与超级计算机竞争的经典机制的限制。HEISINGBERG的目标是将我们最先进的空间光子自旋模拟器（通过空间光调制器耦合10,000个自旋的全光处理的迭代周期）升级为压缩光，使其通过矢量矩阵乘法方案完全可编程，使用全息，辅助自旋和有效磁场，并设计专门的定制和专用算法，将其带入量子体制。在一个正交场中减少波动将使我们能够扩大和优化现有机器的性能，使其超越经典超级计算机和竞争自旋模拟器的能力。HEISINGBERG设备将在室温下运行100,000个自旋，并在改进的XY架构上处理新的量子退火算法。此外，通过分束器调制、混合和相位控制压缩态的非经典资源，如多光子态的纠缠或叠加，将有望利用量子优势，推动我们的机器进入量子模拟状态。这一发展将通过具体阐明算法复杂性的问题，并阐明在退火器和模拟器中可用的量子优势的性质，从而刺激量子信息处理社区。这些进步将使我们能够在云平台上证明，退火和绝热算法可以有效地解决np困难问题。