دسته بندی:

محاسبات کوانتومی - Quantum-Computing

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سال انتشار:

2022

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عنوان انگلیسی مقاله:

Retargetable Optimizing Compilers for Quantum Accelerators via a Multilevel Intermediate Representation

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ترجمه فارسی عنوان مقاله:

کامپایلرهای بهینه سازی مجدد قابل هدف گیری برای شتاب دهنده های کوانتومی از طریق یک نمایش میانی چند سطحی

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منبع:

ieee - ieee Micro;2022;42;5;10:1109/MM:2022:3179654

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نویسنده:

Thien Nguyen; Alexander McCaskey

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چکیده انگلیسی:

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.

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قیمت: رایگان

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توضیحات اضافی:

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