Retargetable Optimizing Compilers for Quantum Accelerators via a Multi-Level Intermediate Representation

Journal Article · 14 June 2022 · IEEE Micro

DOI: https://doi.org/10.1109/mm.2022.3179654 · OSTI ID:1883985

Nguyen, Thien  ^[1]; McCaskey, Alex  ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

In this work, we present a multi-level quantum-classical intermediate representation (IR) that enables an optimizing, retargetable compiler for available quantum languages. Our work builds upon the Multi-level Intermediate Representation (MLIR) framework and leverages its unique progressive lowering capabilities to map quantum languages to the LLVM machine-level IR. We provide both quantum and classical optimizations via the MLIR pattern rewriting sub-system and standard LLVM optimization passes, and demonstrate the programmability, compilation, and execution of our approach via standard benchmarks and test cases. In comparison to other standalone language and compiler efforts available today, our work results in compile times that are 1000x faster than standard Pythonic approaches, and 5-10x faster than comparative standalone quantum language compilers. Our compiler provides quantum resource optimizations via standard programming patterns that result in a 10x reduction in entangling operations, a common source of program noise. We see this work as a vehicle for rapid quantum compiler prototyping.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1883985
• Journal Information: IEEE Micro, Journal Name: IEEE Micro Journal Issue: 5 Vol. 42; ISSN 0272-1732
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English