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PhD thesis, Department of Physics, Tokyo Institute of Technology Kadowaki T (1998) Study of optimization problems by quantum annealing. Kadowaki T, Nishimori H (1998) Quantum annealing in the transverse Ising model. Science 220(4598):671–680ĭenchev VS, Boixo S, Isakov SV, Ding N, Babbush R, Smelyanskiy V, Martinis J, Neven H (2016) What is the computational value of finite-range tunneling? Phys Rev X 6(3):031015

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Lawler EL, Lenstra JK, Rinnooy Kan AHG, Shmoys DB (1985) The traveling salesman problem: a guided tour of combinatorial optimization. Schrijver A (2003) Combinatorial optimization: polyhedra and efficiency, vol 24. We also achieved over 9-times large energy efficiency compared to a CPU-based system. We propose an FPGA accelerator for QA simulations designed using “open computing language.” We achieved up to 12.6 times speed-up for single FPGA implementation and 23.8 times speed-up for two-FPGA implementation compared to a CPU. If we can reduce the huge computational time, it is possible to use QA simulations to solve combinatorial optimization problems. Since the access to a quantum annealer is also limited, acceleration of QA simulations using conventional computers is regarded as a very important topic. However, large problems should be partitioned into sub-problems and solved separately, and this reduces the processing speed of the quantum annealer. It has been shown that the processing time of QMC simulation on a CPU scales similarly to that of QA on the D-wave 2X quantum annealer, although the latter is over \(10^8\) times faster than the former. QA can be simulated on a computer using quantum Monte Carlo (QMC) simulation of the Ising model, while sacrificing a huge processing time. Quantum annealing (QA) is a method to find the global optimum for a combinatorial optimization problem by using quantum fluctuations.