FPGA-Based Prototype of a Quantum Annealing Simulator for Sparse Ising Model

H. M. Waidyasooriya, Yuta Ohma, M. Hariyama
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Abstract

Quantum annealing (QA) is a probabilistic approx-imation method to find the global optimum of a combinatorial optimization problem. QA is done on quantum annealers such as D-wave using quantum properties. Since the number of qubits in quantum annealers is limited, it is difficult to use those to solve large-scale real-world problems. Therefore, quantum annealing simulation on digital computers is necessary. In this paper, we discuss an FPGA based quantum annealing simulator for sparse Ising model. Unlike a fully-connected Ising model, the number of connections among spins in sparse model is limited. Highly sparse Ising models require significantly low amount of computations while allowing more parallel operations. One the other hand, sparsity and the connections among spins are not the same for different Ising models, and it is difficult to propose one specific accelerator architecture for all. We propose a method to automatically generate an application specific accelerator archi-tecture for a given sparse Ising model. The proposed accelerator fully exploits the parallelism to increase the processing speed. We design an FPGA prototype of the proposed accelerator and confirmed the correct behavior. In future, we expect to extend the proposed method to execute large quantum annealing simulations using multiple FPGAs.
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基于fpga的稀疏Ising模型量子退火模拟器原型
量子退火(QA)是一种寻找组合优化问题全局最优解的概率逼近方法。QA是利用量子特性在量子退火器(如D-wave)上完成的。由于量子退火炉中的量子比特数量有限,因此很难使用这些量子比特来解决大规模的现实问题。因此,在数字计算机上进行量子退火模拟是必要的。本文讨论了一种基于FPGA的稀疏Ising模型量子退火模拟器。与全连接的Ising模型不同,稀疏模型中自旋之间的连接数是有限的。高度稀疏的Ising模型需要非常少的计算量,同时允许更多的并行操作。另一方面,不同的伊辛模型的稀疏性和自旋之间的联系是不一样的,而且很难为所有的模型提出一个特定的加速器架构。我们提出了一种针对给定的稀疏Ising模型自动生成特定于应用的加速器架构的方法。该加速器充分利用并行性来提高处理速度。我们设计了所提出的加速器的FPGA原型,并验证了其正确的行为。未来,我们期望将所提出的方法扩展到使用多个fpga执行大型量子退火模拟。
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