A quantum computing system naturally consists of two components, the software system and the hardware system. Quantum applications are programmed using the quantum software and then executed on the quantum hardware. However, the performance of existing quantum computing system is still limited. Solving a practical problem that is beyond the capability of classical computers on a quantum computer has not yet been demonstrated. In this review, we point out that the quantum software and hardware systems should be designed collaboratively to fully exploit the potential of quantum computing. We first review three related works, including one hardware-aware quantum compiler optimization, one application-aware quantum hardware architecture design flow, and one co-design approach for the emerging quantum computational chemistry. Then we discuss some potential future directions following the co-design principle.
A quantum computing system naturally consists of two components, the software system and the hardware system. Quantum applications are programmed using the quantum software and then executed on the quantum hardware. However, the performance of existing quantum computing system is still limited. Solving a practical problem that is beyond the capability of classical computers on a quantum computer has not yet been demonstrated. In this review, we point out that the quantum software and hardware systems should be designed collaboratively to fully exploit the potential of quantum computing. We first review three related works, including one hardware-aware quantum compiler optimization, one application-aware quantum hardware architecture design flow, and one co-design approach for the emerging quantum computational chemistry. Then we discuss some potential future directions following the co-design principle.
Quantum computing promises revolutionary advancements across various fields, but its practical realization hinges on overcoming significant challenges in algorithm execution. Traditional hardware-software co-design approaches for quantum computing often rely on pre-defined architectures and compilation strategies, which can be suboptimal for diverse quantum algorithms. This paper proposes an adaptive hardware-software co-design framework that dynamically adjusts the quantum computing system's configuration – encompassing both hardware architecture and software compilation – in response to the specific requirements of the algorithm being executed. The core claim is that optimized co-design is pivotal for efficient quantum algorithm execution. This framework leverages a dynamic optimization loop, continuously monitoring algorithm performance and adjusting parameters such as qubit connectivity, gate set selection, and compilation strategies. The system utilizes a mathematical model to represent the algorithm's computational demands and hardware capabilities, enabling real-time adaptation. This approach moves beyond static design methodologies, offering a pathway towards significantly improved performance and resource utilization in quantum computing systems. The framework's ability to adapt in real-time represents a crucial step towards unlocking the full potential of quantum algorithms.
Computational chemistry is the leading application to demonstrate the advantage of quantum computing in the near term. However, large-scale simulation of chemical systems on quantum computers is currently hindered due to a mismatch between the computational resource needs of the program and those available in today’s technology. In this paper we argue that significant new optimizations can be discovered by co-designing the application, compiler, and hardware. We show that multiple optimization objectives can be coordinated through the key abstraction layer of Pauli strings, which are the basic building blocks of computational chemistry programs. In particular, we leverage Pauli strings to identify critical program components that can be used to compress program size with minimal loss of accuracy. We also leverage the structure of Pauli string simulation circuits to tailor a novel hardware architecture and compiler, leading to significant execution overhead reduction by up to 99%. While exploiting the high-level domain knowledge reveals significant optimization opportunities, our hardware/software framework is not tied to a particular program instance and can accommodate the full family of computational chemistry problems with such structure. We believe the co-design lessons of this study can be extended to other domains and hardware technologies to hasten the onset of quantum advantage.
The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, but the quantum devices currently available possess only a very limited number of qubits and suffer from considerable imperfections. One possibility to progress towards practical utility is to use a co-design approach: Problem formulation and algorithm, but also the physical QPU properties are tailored to the specific application. Since QPUs will likely be used as accelerators for classical computers, details of systemic integration into existing architectures are another lever to influence and improve the practical utility of QPUs. In this work, we investigate the influence of different parameters on the runtime of quantum programs on tailored hybrid CPU-QPU-systems. We study the influence of communication times between CPU and QPU, how adapting QPU designs influences quantum and overall execution performance, and how these factors interact. Using a simple model that allows for estimating which design choices should be subjected to optimisation for a given task, we provide an intuition to the HPC community on potentials and limitations of co-design approaches. We also discuss physical limitations for implementing the proposed changes on real quantum hardware devices.
Quantum computing is positioned to be a transformative technology to key areas of scientific interest such as quantum chemistry, material science, and cryptography. At a hardware level, recent years have seen broad progress in a variety of qubit modalities, however, error rates are still orders of magnitude higher than what is necessary for large-scale quantum programs. This gap in error rates motivates the use of quantum error correction (QEC) and the realization of fault-tolerant quantum computing (FTQC), yet today, although the theory of FTQC is promising, effectively connecting it to real devices poses significant challenges. In this dissertation we study opportunities for more efficient and scalable FTQC architectures through the co-design of quantum hardware and software. We first look at how unique hardware capabilities in quantum computers made from 2D arrays of optically trapped atoms can improve QEC performance. For computation, we study how longer-range interactions between atoms can enable a novel embedding of QEC blocks on physical qubits that improves logical-level connectivity. For memory, we study how mid-circuit movement of atoms can effectively implement higher encoding rate QEC codes, creating a qubit-efficient memory system. We next consider the performance impact of real-time classical systems in FTQC architectures, and explore the use of speculation to improve large-scale decoding systems. Finally, we study how software systems can be used to improve QEC designs. We consider the problem of QEC circuit optimization, and study an automated approach based on iterative optimization of performance-reducing subcircuits.
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.
Quantum computing, built on the principles of quantum mechanics, has emerged as a transformative paradigm with the potential to revolutionize computational capabilities, enabling solutions to problems that are currently intractable for classical systems. Quantum computing integrates hardware, architectures, and software for improved applications while utilizing superposition, entanglement, and interference to promote materials science, cryptography, optimization, and artificial intelligence. Sections introduce and describe the model components needed in order to create quantum processors, including qubits, quantum gates and control electronics, as well as a review of new developments in quantum software frameworks and programming tools. Notable examples, such as Shor’s algorithm for cryptographic caveat, Grover’s algorithm for inefficient search processes, and quantum-enhanced optimization, illuminate the position of quantum computing as a means of tackling tough problems that span disciplines, such as pharmaceuticals, predictors of climate, and finance modeling. However, the field continues to face significant challenges, like limited scalability, quantum gate error rates, qubit coherence times, and difficulties integrating with classical systems. Future efforts will focus on developing hybrid quantum-classical systems, scalable architectures, and industry- specific applications to transition quantum computing from exploration to practical deployment. This review provides a comprehensive analysis of quantum computing, covering its potential, trends, and challenges, while exploring hardware-software integration, classicalquantum comparisons, and insights to guide research and industry in unlocking its revolutionary possibility.