Software-Hardware Co-Optimization for Computational Chemistry on Superconducting Quantum Processors
| Autor: | Ali Javadi-Abhari, Yunong Shi, Gushu Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer science Overhead (engineering) Computer Science - Emerging Technologies FOS: Physical sciences 02 engineering and technology computer.software_genre Computational resource 01 natural sciences Abstraction layer Software Computational chemistry 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Quantum computer 010302 applied physics Hardware architecture Quantum Physics business.industry 020202 computer hardware & architecture Software framework Emerging Technologies (cs.ET) Compiler Quantum Physics (quant-ph) business computer Computer hardware |
| Zdroj: | ISCA |
| Popis: | Computational chemistry is the leading application to demonstrate the advantage of quantum computing in the near term. However, large-scale simulation of chemical systems on quantum computers is currently hindered due to a mismatch between the computational resource needs of the program and those available in today's technology. In this paper we argue that significant new optimizations can be discovered by co-designing the application, compiler, and hardware. We show that multiple optimization objectives can be coordinated through the key abstraction layer of Pauli strings, which are the basic building blocks of computational chemistry programs. In particular, we leverage Pauli strings to identify critical program components that can be used to compress program size with minimal loss of accuracy. We also leverage the structure of Pauli string simulation circuits to tailor a novel hardware architecture and compiler, leading to significant execution overhead reduction by up to 99%. While exploiting the high-level domain knowledge reveals significant optimization opportunities, our hardware/software framework is not tied to a particular program instance and can accommodate the full family of computational chemistry problems with such structure. We believe the co-design lessons of this study can be extended to other domains and hardware technologies to hasten the onset of quantum advantage. Comment: 12 pages, 11 figures, to appear in ISCA 2021 |
| Databáze: | OpenAIRE |
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