Work

Learning Simon's quantum algorithm

Year: 2018

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1806.10448

Authors: Wan, Kwok Ho, Liu, Feiyang, Dahlsten, Oscar, Kim, M. S.

Institutions: Imperial College London, Southern University of Science and Technology

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Imperial College London, Southern University of Science and Technology, Samsung, Engineering and Physical Sciences Research Council, Division of Mathematical Sciences

Awards: G3772063132, Building Large Quantum States out of Light

OpenAlex ID: W2809900510

Cites: 26

Cited by: 11

Related to: 10