Abstract Quantum compilation, critical for bridging high-level quantum programming and physical hardware, faces unique challenges distinct from classical compilation. As quantum computing advances, scalable and efficient quantum compilation methods become necessary. This paper surveys the landscape of quantum compilation, detailing the processes of qubit mapping and circuit optimization, and emphasizing the need for integration with classical computing to harness quantum advantages. Techniques such as Variational Quantum Eigensolver (VQE) exemplify hybrid approaches, highlighting the potential synergy between quantum and classical systems. It is concluded that, while quantum compilation retains many classic methodologies, it introduces novel complexities and opportunities for optimization and verification, essential for the evolving field of quantum computing.
Abstract Quantum compilation, critical for bridging high-level quantum programming and physical hardware, faces unique challenges distinct from classical compilation. As quantum computing advances, scalable and efficient quantum compilation methods become necessary. This paper surveys the landscape of quantum compilation, detailing the processes of qubit mapping and circuit optimization, and emphasizing the need for integration with classical computing to harness quantum advantages. Techniques such as Variational Quantum Eigensolver (VQE) exemplify hybrid approaches, highlighting the potential synergy between quantum and classical systems. It is concluded that, while quantum compilation retains many classic methodologies, it introduces novel complexities and opportunities for optimization and verification, essential for the evolving field of quantum computing.
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.
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.
This paper addresses the critical challenge of quantum algorithm compilation and optimization. The core claim is to design an efficient algorithm for translating high-level algorithms into quantum circuits and subsequently optimizing them to minimize quantum resource consumption. This is achieved through a novel mechanism employing a hybrid approach combining quantum annealing and genetic algorithms to discover optimal quantum circuit implementations. The methodology focuses on reducing the number of qubits and gate operations, key factors in the feasibility of quantum computation. The presented framework offers a potential solution to the complexities inherent in mapping classical algorithms onto quantum hardware, contributing to the advancement of practical quantum computing. The paper outlines the theoretical underpinnings, the proposed optimization strategy, and discusses potential future research directions.
The rapid development of Quantum Computing (QC) as a promising computing paradigm has garnered significant attention for its ability to harness quantum mechanical properties for computation. With classical computing facing limitations outlined by Moore’s Law, QC emerges as a potential alternative for tackling complex computational problems. Yet, to unlock its full potential, robust and optimized compilers are pivotal, especially in addressing challenges posed by circuits with numerous qubits. In this systematic literature review, we analyze 18 articles to identify proposed optimizations for quantum compilers, exploring their applications, performance impacts, and emerging trends. Our primary goal is to offer valuable insights into the recent advancements and challenges in QC compiler optimizations. This will be achieved through the clustering of optimization groups, ultimately facilitating further progress in the development of highly optimized quantum algorithms and circuits.
Quantum computing is a promising technology for high-performance computation, but requires mature toolflows that can map large-scale quantum programs onto targeted hardware. In this paper, we present a scalable compiler for large-scale quantum applications, and show the opportunities for reducing compilation and analysis time, as well as output code size. We discuss the similarities and differences between compiling for a quantum computer as opposed to a classical computer, and present a state-of-the-art approach for compilation of classical circuits into quantum circuits. Our work also highlights the importance of high-level quantum compilation for logical circuit translation, quantitative analysis of algorithms, and optimization of circuit lengths.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.