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arXiv:1604.01401 · Quantum Science and Technology

A software methodology for compiling quantum programs

Thomas Häner, Damian S. Steiger, Krysta M. Svore, Matthias Troyer

Published Jan 8, 2018• 172 citations
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Abstract

Abstract Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.

DOI
10.1088/2058-9565/aaa5cc

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Abstract Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.
We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together with constraints and objectives on the final program. Advanced synthesis algorithms transform the model into a low-level quantum program that meets the user's specification and is directed at a stipulated hardware. This separation of description from implementation is essential for scale. The technology adapts electronic design automation methods to quantum computing, finding feasible implementations in a virtually unlimited functional space. The results show clear superiority over the compilation and transpilation methods used today. We expect that this technological approach will take over and prevail as quantum software become more demanding, complex, and essential.
Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.
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.
Building a quantum computer is not only about having qubits or quantum processors, analogously to classical computers are not only based on transistors or integrated circuits. Accordingly, in the very recent times, a few unique research groups in academia and companies have been exploring and proposing high-level and low-level programming languages, compilers, microarchitecture and instruction set architectures for quantum computation as well as different computer architecture approaches. In other words, developing a quantum computer requires bridging quantum applications and quantum devices. This talk will address the main challenges of building a scalable full-stack quantum computing system followed by a discussion on its architecture focusing on fault tolerance and compilation of quantum algorithms on NISQ devices. I will also provide my vision on how the research community could accelerate the process towards building such a scalable quantum machine, potentially through vertical cross-layer co-design structured methodologies.
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 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.