Learning to rank quantum circuits for hardware-optimized performance enhancement

no code implementations • 9 Apr 2024 • Gavin S. Hartnett, Aaron Barbosa, Pranav S. Mundada, Michael Hush, Michael J. Biercuk, Yuval Baum

We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware.

Learning-To-Rank

Data Needs and Challenges of Quantum Dot Devices Automation: Workshop Report

no code implementations • 21 Dec 2023 • Justyna P. Zwolak, Jacob M. Taylor, Reed Andrews, Jared Benson, Garnett Bryant, Donovan Buterakos, Anasua Chatterjee, Sankar Das Sarma, Mark A. Eriksson, Eliška Greplová, Michael J. Gullans, Fabian Hader, Tyler J. Kovach, Pranav S. Mundada, Mick Ramsey, Torbjoern Rasmussen, Brandon Severin, Anthony Sigillito, Brennan Undseth, Brian Weber

Gate-defined quantum dots are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers.

Benchmarking