Compiler Optimization for Quantum Computing Using Reinforcement Learning

• URL: http://arxiv.org/abs/2212.04508v2
• Date: Tue, 4 Apr 2023 09:28:14 GMT
• Title: Compiler Optimization for Quantum Computing Using Reinforcement Learning
• Authors: Nils Quetschlich, Lukas Burgholzer, Robert Wille
• Abstract summary: We propose a reinforcement learning framework for developing optimized quantum circuit compilation flows. The proposed framework is set up with a selection of compilation passes from IBM's Qiskit and Quantinuum's TKET. It significantly outperforms both individual compilers in 73% of cases regarding the expected fidelity.
• Score: 3.610459670994051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with many compilation steps and numerous possible optimization passes. Despite the similarities, the development of compilers for quantum computing is still in its infancy -- lacking mutual consolidation on the best sequence of passes, compatibility, adaptability, and flexibility. In this work, we take advantage of decades of classical compiler optimization and propose a reinforcement learning framework for developing optimized quantum circuit compilation flows. Through distinct constraints and a unifying interface, the framework supports the combination of techniques from different compilers and optimization tools in a single compilation flow. Experimental evaluations show that the proposed framework -- set up with a selection of compilation passes from IBM's Qiskit and Quantinuum's TKET -- significantly outperforms both individual compilers in 73% of cases regarding the expected fidelity. The framework is available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).

Related papers

• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
  We design, implement, and evaluate three hybrid quantum k-Means algorithms. We exploit quantum phenomena to speed up the computation of distances. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
  arXiv  Detail & Related papers  (2022-12-13T16:04:16Z)
• Predicting Good Quantum Circuit Compilation Options [3.610459670994051]
  We propose a framework that predicts the best combination of compilation options for quantum circuits. For more than 95% of the circuits, a combination of compilation options within the top-three is determined. The resulting methodology lays the foundation for further applications of machine learning in this domain.
  arXiv  Detail & Related papers  (2022-10-14T18:00:03Z)
• Quantum simulation with just-in-time compilation [0.0]
  We present a first attempt to perform circuit-based quantum simulation using the just-in-time (JIT) compilation technique. Qibojit is a new module for the Qibo quantum computing framework, which uses a just-in-time compilation approach through Python.
  arXiv  Detail & Related papers  (2022-03-16T18:00:00Z)
• Arline Benchmarks: Automated Benchmarking Platform for Quantum Compilers [0.0]
  Open-source software package, Arline Benchmarks, is designed to perform automated benchmarking of quantum compilers. We compare several quantum compilation frameworks based on a set of important metrics. We propose a concept of composite compilation pipeline that combines compiler-specific circuit optimizations in a single compilation stack.
  arXiv  Detail & Related papers  (2022-02-28T18:48:01Z)
• An LLVM-based C++ Compiler Toolchain for Variational Hybrid Quantum-Classical Algorithms and Quantum Accelerators [0.8323133408188051]
  This paper presents an LLVM-based C++ compiler toolchain to efficiently execute variational hybrid quantum-classical algorithms. We introduce a set of extensions to the C++ language for programming these algorithms. We evaluate the framework's performance by running quantum circuits that prepare Thermofield Double (TFD) states.
  arXiv  Detail & Related papers  (2022-02-22T19:32:50Z)
• Enabling Retargetable Optimizing Compilers for Quantum Accelerators via a Multi-Level Intermediate Representation [78.8942067357231]
  We present a multi-level quantum-classical intermediate representation (IR) that enables an optimizing, retargetable, ahead-of-time compiler. We support the entire gate-based OpenQASM 3 language and provide custom extensions for common quantum programming patterns and improved syntax. Our work results in compile times that are 1000x faster than standard Pythonic approaches, and 5-10x faster than comparative standalone quantum language compilers.
  arXiv  Detail & Related papers  (2021-09-01T17:29:47Z)
• Extending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation [78.8942067357231]
  Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. We present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time compilation.
  arXiv  Detail & Related papers  (2021-05-10T21:11:21Z)
• A MLIR Dialect for Quantum Assembly Languages [78.8942067357231]
  We demonstrate the utility of the Multi-Level Intermediate Representation (MLIR) for quantum computing. We extend MLIR with a new quantum dialect that enables the expression and compilation of common quantum assembly languages. We leverage a qcor-enabled implementation of the QIR quantum runtime API to enable a retargetable (quantum hardware agnostic) compiler workflow.
  arXiv  Detail & Related papers  (2021-01-27T13:00:39Z)
• Extending C++ for Heterogeneous Quantum-Classical Computing [56.782064931823015]
  qcor is a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language manner.
  arXiv  Detail & Related papers  (2020-10-08T12:49:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.