Learning Simon's quantum algorithm

2018/06/27 by Kwok Ho Wan, Feiyang Liu, Wan, Kwok Ho +6 · 4 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Computer science #FOS: Physical sciences #Mathematics #Neural Networks and Applications #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Fourier transform #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Quantum algorithm #Quantum circuit #Quantum computer #Quantum error correction #Quantum gate #Quantum machine learning #Quantum mechanics #Quantum phase estimation algorithm #Unitary state #quant-ph

paper · pdf · doi:10.48550/arxiv.1806.10448

published in arXiv (Cornell University) (Cornell University)

arxiv created 2018/06/27 · openalex publication_date 2018/06/27 · arxiv updated 2018/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider whether trainable quantum unitaries can be used to discover quantum speed-ups for classical problems. Using methods recently developed for training quantum neural nets, we consider Simon's problem, for which there is a known quantum algorithm which performs exponentially faster in the number of bits, relative to the best known classical algorithm. We give the problem to a randomly chosen but trainable unitary circuit, and find that the training recovers Simon's algorithm as hoped.

Citations

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