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MONTE CARLO MARKOV CHAIN BASED QUANTUM PROGRAM OPTIMIZATION
MONTE CARLO MARKOV CHAIN BASED QUANTUM PROGRAM OPTIMIZATION
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机译：基于蒙特卡洛马尔科夫链的量子程序优化
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摘要
From a quantum program a first mutant is generated using a processor and a memory, where the first mutant is a randomly-generated transformation of the quantum program. A quality score, a correctness distance, and a probability of acceptance corresponding to the first mutant are computed. An acceptance corresponding to the first mutant is determined according to the probability of acceptance. Upon determining that an acceptance of the first mutant corresponding to the probability of acceptance exceeds an acceptance threshold, the quantum program is replaced with the first mutant. Upon determining that the quality score exceeds a storage threshold and that the correctness distance is zero, the first mutant is stored. These actions are iterated until reaching an iteration limit.
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