[3Win5] Poster session 3

[3Win5-71] Possibilities of Stochastic Simulations Using Sampling by Quantum Circuit Born Machines

〇Yoshihiro Osakabe¹, Kiichiro Toyoizumi¹, Akinori Asahara¹, Taiki Morinaga¹, Koichiro Yawata¹ (1.Hitachi, Ltd.)

Keywords:Quantum Machine Learning, Quantum Circuit Learning, Surrogate Model, Stochastic Sampling

In this study, we discuss the applicability of Quantum Circuit Born Machine (QCBM) as surrogate models for simulations involving complex stochastic variations. It is well-known that QCBM can learn complex probability distributions, such as Gaussian mixture distributions, through the probability amplitudes of quantum circuits. However, research on applying trained QCBM as sampling method for probabilistic physical simulations has not been sufficiently investigated. Therefore, this paper focuses on the sampling accuracy and speed of QCBM and evaluates their feasibility through a comparison with rejection sampling.