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A Physics-Based Quantum Computing Compiler

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

This paper proposes and details the design of a novel quantum computing compiler, termed a Physics-Based Quantum Computing Compiler (PBQCC). The core innovation lies in automating the process of quantum algorithm generation by directly translating physical laws – specifically, quantum mechanical equations – into executable quantum circuits. Traditional quantum algorithm design relies heavily on manual coding, a process prone to human error and limited by the designer's expertise. The PBQCC utilizes a formal language and symbolic deduction to achieve this automation. The compiler takes a high-level description of a physical system and its desired manipulation (expressed within a physics-based framework) as input, and then generates an optimized quantum circuit that embodies the solution. This approach fundamentally shifts the paradigm from algorithm design to physics-driven computation, offering the potential for significantly increased efficiency, scalability, and accessibility within the quantum computing domain. The compiler incorporates optimization techniques and validation steps to ensure the fidelity and correctness of the generated quantum circuits. This document outlines the architecture, key components, and operational principles of the PBQCC, emphasizing its potential to revolutionize quantum algorithm development. ---

DOI
10.5281/zenodo.22447881

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Traditional compiler optimization focuses on maximizing performance within the constraints of classical architectures. However, quantum computing presents a fundamentally different paradigm, demanding a new approach to software optimization. This paper proposes a hardware-aware compiler optimization technique, inspired by quantum computing principles, to bridge the gap between high-level programming languages and the specific characteristics of quantum hardware. The core idea is to translate high-level code into quantum circuits while explicitly considering factors like qubit connectivity, gate fidelity, and decoherence. This approach allows for optimization strategies tailored to the unique limitations and opportunities of quantum devices, potentially leading to significant performance improvements compared to generic quantum compilation methods. The compiler operates by analyzing the algorithmic complexity, identifying quantum-native operations, and strategically mapping them onto the available quantum hardware, thereby minimizing the impact of hardware constraints. This paper outlines the framework, key components, and potential optimizations within this hardware-aware compiler.
This paper presents a novel theory for quantum computing program compilation and optimization. The core of this theory lies in translating high-level quantum programs into efficient quantum circuit implementations. This is achieved through the utilization of an abstract syntax tree (AST) derived from the quantum program. A compilation algorithm is designed to traverse the AST and generate corresponding quantum circuits, incorporating optimization strategies throughout the process. The work addresses a significant challenge within quantum computing – the complexity of program compilation – and proposes a new theoretical framework to facilitate the development of more practical and performant quantum algorithms. The theoretical framework outlined here provides a foundation for future research and development in quantum compiler design.
Quantum computing holds transformative potential, but its adoption is hindered by the complexity of generating efficient, hardware-specific code. This work presents a modular, extensible compiler framework that bridges high-level quantum languages with diverse hardware architectures. The framework consists of three modules: a front-end for parsing quantum code into a hardware-agnostic intermediate representation (IR), an optimization module for enhancing quantum circuits through gate synthesis, qubit routing, and error mitigation, and a back-end for generating hardware-specific instructions. Major contributions include a hardware-agnostic IR for cross-platform compatibility, optimization techniques to reduce gate complexity and noise, and hardware-specific adaptations to improve execution fidelity. A practical demonstration optimizes quantum circuits, highlighting the impact of hardware constraints. Comparative analysis of IBM Quantum and IonQ platforms underscores the role of qubit connectivity and noise resilience in algorithmic performance. This scalable framework enhances quantum software development and efficient hardware utilization.
Quantum computing is a revolution in computational theory that uses the principles of quantum mechanics to perform computations that are beyond the capacity of classical computers. It works with the special features of the quantum bits or qubits to encode and transform the information in manners conventional bits cannot imagine. A quantum compiler is a software tool designed to translate high-level quantum language code into executable quantum instructions. This paper aims to develop a quantum compiler for translating Quantum Computation Language source codes into quantum bytecodes which can later be executed on a Quantum Virtual Machine. This compiler will have a combinational structure that includes a frontend for interpreting the QCL code, an intermediate representation for optimizing quantum algorithms, and a backend for generating efficient quantum bytecode. Optimization techniques will be applied to improve the functioning of the quantum circuits. The compiler will also include an error handling module to handle the errors. The successful deployment of a quantum compiler for QCL has many implications and unlocks opportunities for advancements in quantum technologies.
We survey recent strides made towards building a software framework that is capable of compiling quantum algorithms from a high-level description down to physical gates that can be implemented on a fault-tolerant quantum computer. We discuss why compilation and design automation tools such as the ones in our framework are key for tackling the grand challenge of building a scalable quantum computer. We then describe specialized libraries that have been developed using the LIQUi|〉 programming language. This includes reversible circuits for arithmetic as well as new, truly quantum approaches that rely on quantum computer architectures that allow the probabilistic execution of gates, a model that can reduce time and space overheads in some cases. We highlight why these libraries are useful for the implementation of many quantum algorithms. Finally, we survey the tool Revs that facilitate resource efficient compilation of higher-level irreversible programs into lower-level reversible circuits while trying to optimize the memory footprint of the resulting reversible networks. This is motivated by the limited availability of qubits for the foreseeable future.
This paper introduces Quantum Computing Optimization via Physical Laws (QCOP), a novel algorithm designed to leverage the inherent parallelism of quantum computing and the fundamental relationships within physical laws for optimization problems. The core mechanism involves translating an optimization challenge into a quantum mechanical system governed by the Schrödinger equation. Crucially, the algorithm incorporates conservation laws derived from physical principles as constraints, aiming to enhance both efficiency and accuracy. QCOP's novelty lies in the direct integration of quantum computing with established physical laws, offering a potentially transformative approach to optimization. The proposed methodology offers a distinct pathway compared to traditional quantum optimization algorithms, capitalizing on the unique properties of quantum systems to solve complex problems. The framework presented here provides a theoretical foundation for future research and development in this burgeoning field. We outline the key elements of the QCOP algorithm and discuss its potential applications.