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Quantum Machine Learning Quantum Simulation

Design Automation and Design Space Exploration for Quantum Computers

arXiv

Authors: Mathias Soeken, Martin Roetteler, Nathan Wiebe, Giovanni De Micheli

Year

2016

Paper ID

42070

Status

Preprint

Abstract Read

~2 min

Abstract Words

103

Citations

N/A

Abstract

A major hurdle to the deployment of quantum linear systems algorithms and recent quantum simulation algorithms lies in the difficulty to find inexpensive reversible circuits for arithmetic using existing hand coded methods. Motivated by recent advances in reversible logic synthesis, we synthesize arithmetic circuits using classical design automation flows and tools. The combination of classical and reversible logic synthesis enables the automatic design of large components in reversible logic starting from well-known hardware description languages such as Verilog. As a prototype example for our approach we automatically generate high quality networks for the reciprocal 1/x, which is necessary for quantum linear systems algorithms.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2016 reference point for readers tracking recent quantum research.
A major hurdle to the deployment of quantum linear systems algorithms and recent quantum simulation algorithms lies in the difficulty to find inexpensive reversible circuits...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1612.00631 [2] arXiv https://arxiv.org/abs/1612.00631 [3] Publisher https://arxiv.org/abs/1612.00631

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