Retargetable Optimizing Compilers for Quantum Accelerators via a Multilevel Intermediate Representation

We present a multilevel quantum–classical intermediate representation (IR) that enables an optimizing, retargetable compiler for available quantum languages. Our work builds upon the multilevel intermediate representation (MLIR) framework and leverages its unique progressive lowering capabilities to...

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Bibliographic Details
Published in:IEEE MICRO 2022-09, Vol.42 (5), p.17-33
Main Authors: Nguyen, Thien, McCaskey, Alexander
Format: Article
Language:English
Subjects:
Accelerators
Codes
Compilers
Computational modeling
Languages
Multilevel
Multilevel systems
Optimization
Program processors
programming languages
Prototyping
quantum computing
quantum programming
Qubit
Representations
Runtime
Runtime library
Subsystems
Virtual environments
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Summary:We present a multilevel quantum–classical intermediate representation (IR) that enables an optimizing, retargetable compiler for available quantum languages. Our work builds upon the multilevel intermediate representation (MLIR) framework and leverages its unique progressive lowering capabilities to map quantum languages to the low-level virtual machine (LLVM) machine-level IR. We provide both quantum and classical optimizations via the MLIR pattern rewriting subsystem 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 1,000× faster than standard Pythonic approaches, and 5–10× faster than comparative standalone quantum language compilers. Our compiler provides quantum resource optimizations via standard programming patterns that result in a 10× reduction in entangling operations, a common source of program noise. We see this work as a vehicle for rapid quantum compiler prototyping.
ISSN:0272-1732
1937-4143
DOI:10.1109/MM.2022.3179654