Superstaq: Deep Optimization of Quantum Programs
Abstract
We describe Superstaq, a quantum software platform that optimizes the execution of quantum programs by tailoring to underlying hardware primitives. For benchmarks such as the Bernstein-Vazirani algorithm and the Qubit Coupled Cluster chemistry method, we find that deep optimization can improve program execution performance by at least 10x compared to prevailing state-of-the-art compilers. To highlight the versatility of our approach, we present results from several hardware platforms: superconducting qubits (AQT @ LBNL, IBM Quantum, Rigetti), trapped ions (QSCOUT), and neutral atoms (Infleqtion). Across all platforms, we demonstrate new levels of performance and new capabilities that are enabled by deeper integration between quantum programs and the device physics of hardware.
Cite
@article{arxiv.2309.05157,
title = {Superstaq: Deep Optimization of Quantum Programs},
author = {Colin Campbell and Frederic T. Chong and Denny Dahl and Paige Frederick and Palash Goiporia and Pranav Gokhale and Benjamin Hall and Salahedeen Issa and Eric Jones and Stephanie Lee and Andrew Litteken and Victory Omole and David Owusu-Antwi and Michael A. Perlin and Rich Rines and Kaitlin N. Smith and Noah Goss and Akel Hashim and Ravi Naik and Ed Younis and Daniel Lobser and Christopher G. Yale and Benchen Huang and Ji Liu},
journal= {arXiv preprint arXiv:2309.05157},
year = {2023}
}
Comments
Appearing in IEEE QCE 2023 (Quantum Week) conference