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arXiv 2407.09161physics.opticscond-mat.dis-nncs.ETquant-ph
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在空间光子伊辛机上编码任意伊辛哈密顿量

Encoding arbitrary Ising Hamiltonians on Spatial Photonic Ising Machines

Jason Sakellariou, Alexis Askitopoulos, Georgios Pastras, Symeon I. Tsintzos

AI总结:

本文提出并实验验证了一种可直接控制完整相互作用矩阵的空间光子伊辛机,能够编码任意耦合的伊辛哈密顿量,并在无权与加权图划分问题上展示了通过模拟退火收敛到最优解的能力,显著扩展了 SPIM 在 NP 问题上的应用范围。

摘要:

光子伊辛机是一种新兴的计算范式,旨在解决可归约为寻找伊辛模型基态问题的组合优化问题。空间光子伊辛机已被证明在模拟全连接大规模自旋系统方面具有优势。然而,迄今为止,对一般相互作用矩阵 $J$ 的精细控制只能通过特征值分解方法实现,这要么限制了可扩展性,要么增加了优化过程的执行时间。我们提出并通过实验验证了一种 SPIM 实例,该实例能够直接控制完整的相互作用矩阵,从而可以编码具有任意耦合和连接性的伊辛哈密顿量。我们证明了实验测得的伊辛能量与理论预期值的一致性,然后继续求解无权图和加权图划分问题,展示了通过模拟退火系统性地收敛到最优解。我们的方法在不牺牲系统任何固有优势的情况下,极大地扩展了 SPIM 在实际应用中的适用性,并为在 SPIM 设备上编码已知等价于伊辛模型的全范围 NP 问题铺平了道路。

英文摘要:

Photonic Ising Machines constitute an emergent new paradigm of computation, geared towards tackling combinatorial optimization problems that can be reduced to the problem of finding the ground state of an Ising model. Spatial Photonic Ising Machines have proven to be advantageous for simulating fully connected large-scale spin systems. However, fine control of a general interaction matrix $J$ has so far only been accomplished through eigenvalue decomposition methods that either limit the scalability or increase the execution time of the optimization process. We introduce and experimentally validate a SPIM instance that enables direct control over the full interaction matrix, enabling the encoding of Ising Hamiltonians with arbitrary couplings and connectivity. We demonstrate the conformity of the experimentally measured Ising energy with the theoretically expected values and then proceed to solve both the unweighted and weighted graph partitioning problems, showcasing a systematic convergence to an optimal solution via simulated annealing. Our approach greatly expands the applicability of SPIMs for real-world applications without sacrificing any of the inherent advantages of the system, and paves the way to encoding the full range of NP problems that are known to be equivalent to Ising models, on SPIM devices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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