HybridQ: A Hybrid Simulator for Quantum Circuits. Mandrà, S., Marshall, J., Rieffel, E. G., & Biswas, R. arXiv:2111.06868 [quant-ph], November, 2021. arXiv: 2111.06868![HybridQ: A Hybrid Simulator for Quantum Circuits [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ, a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: "Easy to Use", "Easy to Extend", and "Use the Best Available Technology". The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.
@article{mandra_hybridq_2021,
title = {{HybridQ}: {A} {Hybrid} {Simulator} for {Quantum} {Circuits}},
shorttitle = {{HybridQ}},
url = {http://arxiv.org/abs/2111.06868},
abstract = {Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ, a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: "Easy to Use", "Easy to Extend", and "Use the Best Available Technology". The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.},
urldate = {2021-11-21},
journal = {arXiv:2111.06868 [quant-ph]},
author = {Mandrà, Salvatore and Marshall, Jeffrey and Rieffel, Eleanor G. and Biswas, Rupak},
month = nov,
year = {2021},
note = {arXiv: 2111.06868},
keywords = {numerical analysis, quantum physics, uses sympy},
}
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