Optimization of neural networks via finite-value quantum fluctuations

Ohzeki, Masayuki; Okada, Shinsuke; Terabe, Masayoshi; Taguchi, Shinichiro

doi:10.1038/s41598-018-28212-4

Cited by 29 publications

(18 citation statements)

References 33 publications

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“…Generalization performance, which is the measure of potential power in machine learning but not directly related to the cost function to be optimized, can be enhanced via stochastic methods to optimize cost functions. In particular, QA actually leads to better generalization performance, as shown in literature 43 .…”

Section: Other Solvers and Comparison Datamentioning

confidence: 61%

Control of Automated Guided Vehicles Without Collision by Quantum Annealer and Digital Devices

Ohzeki

Miki

Miyama

et al. 2019

Front. Comput. Sci.

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We formulate an optimization problem to control a large number of automated guided vehicles in a plant without collision. The formulation consists of binary variables. A quadratic cost function over these variables enables us to utilize certain solvers on digital computers and recently developed purpose-specific hardware such as D-Wave 2000Q and the Fujitsu digital annealer. In the present study, we consider an actual plant in Japan, in which vehicles run, and assess efficiency of our formulation for optimizing the vehicles via several solvers. We confirm that our formulation can be a powerful approach for performing smooth control while avoiding collisions between vehicles, as compared to a conventional method. In addition, comparative experiments performed using several solvers reveal that D-Wave 2000Q can be useful as a rapid solver for generating a plan for controlling the vehicles in a short time although it deals only with a small number of vehicles, while a digital computer can rapidly solve the corresponding optimization problem even with a large number of binary variables. 2/123/12 10/12

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Section: Other Solvers and Comparison Datamentioning

confidence: 61%

Control of Automated Guided Vehicles Without Collision by Quantum Annealer and Digital Devices

Ohzeki

Miki

Miyama

et al. 2019

Front. Comput. Sci.

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“…Actually in this parameter range, the quantum annealing gives better optimization results than the simulated annealing. On the other hand, regardless of problem to be solved, the quantum annealing generally contains stochastic fluctuations in the solutions 28,32 . When the parameter α is in the intermediate range where the difficulty inherent in the optimization problem is moderate, both the simulated annealing and the quantum annealing give high quality solutions, but the simulated annealing gives slightly better solutions than the quantum annealing because the relative strength of stochastic fluctuations is large.…”

Section: Resultsmentioning

confidence: 99%

Traffic signal optimization on a square lattice with quantum annealing

Inoue

Okada

Matsumori

et al. 2021

Sci Rep

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The spread of intelligent transportation systems in urban cities has caused heavy computational loads, requiring a novel architecture for managing large-scale traffic. In this study, we develop a method for globally controlling traffic signals arranged on a square lattice by means of a quantum annealing machine, namely the D-Wave quantum annealer. We first formulate a signal optimization problem that minimizes the imbalance of traffic flows in two orthogonal directions. Then we reformulate this problem as an Ising Hamiltonian, which is compatible with quantum annealers. The new control method is compared with a conventional local control method for a large 50-by-50 city, and the results exhibit the superiority of our global control method in suppressing traffic imbalance over wide parameter ranges. Furthermore, the solutions to the global control method obtained with the quantum annealing machine are better than those obtained with conventional simulated annealing. In addition, we prove analytically that the local and the global control methods converge at the limit where cars have equal probabilities for turning and going straight. These results are verified with numerical experiments.

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“…In the previous studies, the previous studies on the classical Boltzmann machine learning by use of the quantum annealer reported better performance than the standard methods in classical computers 43 . In theoretical aspect, the small quantum fluctuation improves the performance on prediction as shown in several studies 44 , 45 .…”

Section: Introductionmentioning

confidence: 82%

Assessment of image generation by quantum annealer

Sato

Ohzeki

Tanaka

2021

Sci Rep

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Quantum annealing was originally proposed as an approach for solving combinatorial optimization problems using quantum effects. D-Wave Systems has released a production model of quantum annealing hardware. However, the inherent noise and various environmental factors in the hardware hamper the determination of optimal solutions. In addition, the freezing effect in regions with weak quantum fluctuations generates outputs approximately following a Gibbs–Boltzmann distribution at an extremely low temperature. Thus, a quantum annealer may also serve as a fast sampler for the Ising spin-glass problem, and several studies have investigated Boltzmann machine learning using a quantum annealer. Previous developments have focused on comparing the performance in the standard distance of the resulting distributions between conventional methods in classical computers and sampling by a quantum annealer. In this study, we focused on the performance of a quantum annealer as a generative model from a different aspect. To evaluate its performance, we prepared a discriminator given by a neural network trained on an a priori dataset. The evaluation results show a higher performance of quantum annealer compared with the classical approach for Boltzmann machine learning in training of the generative model. However the generation of the data suffers from the remanent quantum fluctuation in the quantum annealer. The quality of the generated images from the quantum annealer gets worse than the ideal case of the quantum annealing and the classical Monte-Carlo sampling.

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Optimization of neural networks via finite-value quantum fluctuations

Cited by 29 publications

References 33 publications

Control of Automated Guided Vehicles Without Collision by Quantum Annealer and Digital Devices

Control of Automated Guided Vehicles Without Collision by Quantum Annealer and Digital Devices

Traffic signal optimization on a square lattice with quantum annealing

Assessment of image generation by quantum annealer

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