The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubits in a quantum computer and consider it as a standard to evaluate the performance of the quantum computer intuitively. However, it is quite misleading in most times, especially for investors or governments. This is because the quantum computer works in a quite different way than classical computers. Thus, quantum benchmarking is of great importance. Currently, many quantum benchmarks are proposed from different aspects. In this paper, we review the existing performance benchmarking protocols, models, and metrics. We classify the benchmarking techniques into three categories: physical benchmarking, aggregative benchmarking, and application-level benchmarking. We also discuss the future trend for quantum computer's benchmarking and propose setting up the QTOP100.
The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubits in a quantum computer and consider it as a standard to evaluate the performance of the quantum computer intuitively. However, it is quite misleading in most times, especially for investors or governments. This is because the quantum computer works in a quite different way than classical computers. Thus, quantum benchmarking is of great importance. Currently, many quantum benchmarks are proposed from different aspects. In this paper, we review the existing performance benchmarking protocols, models, and metrics. We classify the benchmarking techniques into three categories: physical benchmarking, aggregative benchmarking, and application-level benchmarking. We also discuss the future trend for quantum computer's benchmarking and propose setting up the QTOP100.
Quantum computers have the potential to provide an advantage over classical computers in a number of areas. Numerous metrics to benchmark the performance of quantum computers, ranging from their individual hardware components to entire applications, have been proposed over the years. Objective comparisons are further hampered in practice as different variations of the same metric are used, and the data disclosed together with a reported metric value is often not sufficient to reproduce the measurements. In this poster, we present our recent article, where we address these challenges by providing a review of metrics and benchmarks for quantum computers [1], including 1) a comprehensive collection of benchmarks allowing holistic comparisons of quantum computers, 2) a consistent format of the definitions across all metrics including a transparent description of the methodology and of the main assumptions and limitations, and 3) a reproducible approach by linking the metrics to open-source software used to evaluate them . We identify five areas where international standardization working groups could be established, namely: i) the identification and agreement on the categories of metrics that comprehensively benchmark device performance; ii) the identification and agreement on a set of well-established metrics that together comprehensively benchmark performance; iii) the identification of metrics specific to hardware platforms, including non-gate-based quantum computers; iv) inter-laboratory comparison studies to develop best practice guides for measurement methodology; and v) agreement on what data and software should be reported together with a metric value to ensure trust, transparency and reproducibility. We provide potential routes to advancing these areas. We expect that this work will accelerate the progress of quantum computing hardware towards quantum advantage. [1] D. Lall et al., A Review and Collection of Metrics and Benchmarks for Quantum Computers: Definitions, Methodologies and Software, arXiv:2502.06717.
Architectures for quantum computing can only be scaled up when they are accompanied by suitable benchmarking techniques. The document provides a comprehensive overview of the state and recommendations for systematic benchmarking of quantum computers. Benchmarking is crucial for assessing the performance of quantum computers, including the hardware, software, as well as algorithms and applications. The document highlights key aspects such as component-level, system-level, software-level, HPC-level, and application-level benchmarks. Component-level benchmarks focus on the performance of individual qubits and gates, while system-level benchmarks evaluate the entire quantum processor. Software-level benchmarks consider the compiler's efficiency and error mitigation techniques. HPC-level and cloud benchmarks address integration with classical systems and cloud platforms, respectively. Application-level benchmarks measure performance in real-world use cases. The document also discusses the importance of standardization to ensure reproducibility and comparability of benchmarks, and highlights ongoing efforts in the quantum computing community towards establishing these benchmarks. Recommendations for future steps emphasize the need for developing standardized evaluation routines and integrating benchmarks with broader quantum technology activities.
Abstract The technological development of increasingly larger quantum processors on different quantum platforms raises the problem of how to fairly compare their performance, known as quantum benchmarking of quantum processors. This is a challenge that computer scientists have already faced when comparing classical processors, leading to the development of various mathematical tools to address it, but also to the identification of the limits of this problem. In this work, we briefly review the most important aspects of both classical processor benchmarks and the metrics comprising them, providing precise definitions and analyzing the quality attributes that they should exhibit. Subsequently, we analyze the intrinsic properties that characterize the paradigm of quantum computing and hinder the naive transfer of strategies from classical benchmarking. However, we can still leverage some of the lessons learned such as the quality attributes of a good benchmark such as relevance, reproducibility, fairness, verifiability and usability. Additionally, we review some of the most important metrics and benchmarks for quantum processors proposed in the literature, assessing what quality attributes they fulfill. Finally, we propose general guidelines for quantum benchmarking. These guidelines aim to pave the way for establishing a roadmap towards standardizing the performance evaluation of quantum devices, ultimately leading to the creation of an organization akin to the Standard Performance Evaluation Corporation (SPEC).
Quantum computing has emerged as a promising paradigm for addressing computational challenges in physics that remain intractable for classical methods. This study presents a quantitative benchmarking analysis of prominent quantum algorithms applied to representative physics problem classes, with the objective of evaluating their computational performance relative to state-of-the-art classical algorithms. Using standardized performance metrics—including execution time, algorithmic complexity, resource utilization, accuracy, scalability, and quantum advantage ratios—the study systematically compares quantum and classical approaches across multiple experimental configurations. The results demonstrate that quantum algorithms exhibit measurable performance gains for specific problem types, particularly in terms of runtime efficiency and scaling behavior, while also highlighting practical constraints related to qubit requirements and circuit depth. Visual and tabulated analyses further reveal that quantum advantage is highly problem-dependent and influenced by algorithmic structure and system size. Overall, the findings provide empirical evidence that quantum algorithms can offer substantial computational benefits for selected physics applications, while underscoring the importance of quantitative benchmarking in distinguishing theoretical promise from practical performance. This work contributes to ongoing efforts to objectively assess quantum advantage and informs future research directions in quantum algorithm development and physics-based applications.
Quantum computing, taking advantage of its parallel computing, is expected to provide exponential acceleration on some difficult problems. It is one of the important directions for the leapfrog development of computing power in the future. However, due to the limitation of the number of quantum bits, coherence time, fidelity and other factors, the computing power of quantum computers has not been fully utilized at the current stage. In recent years, researchers have proposed a variety of quantum computing performance benchmarks to evaluate and research the performance of quantum computers from the bit, circuit, system, application and other levels. However, the existing performance benchmarks cannot directly evaluate the ability of quantum computers to solve specific problems. Considering the above problems, this paper proposes a VQA-classification-algorithm-oriented performance benchmarks for quantum computing, which takes the classification effect under different data scales as one of the evaluation indicators. This benchmark can comprehensively evaluate the ability of quantum computers to solve data classification problems from the three dimensions of scale, speed, and quality. The benchmark was used to evaluate the superconducting quantum computer and simulator of the IBM and Huawei quantum platform, and its effect was verified experimentally.
Quantum AI is a new way to think about the future of Artificial Intelligence through the merging of two technologies: quantum computing and advanced machine learning. Quantum AI has the potential to provide solutions to computationally difficult problems that traditional systems cannot solve. Quantum AI's continued success will rely on advancements in hybrid quantum-classical solutions, as well as the expansion of quantum hardware; hence, research remains necessary to conduct systematic benchmarks as well as performance analysis in order to determine both how well quantum AI algorithms perform practically, and where their limitations lie. In this chapter, detailed examinations of benchmarks that have been developed specifically for quantum AI are presented alongside evaluative metrics across various key areas of performance including algorithmic efficiency, scalability, noise immunity, and resource use for various types of quantum architectures. The chapter integrates dialogue between theory and practice to demonstrate ways in which performance metrics (i.e., quantum circuit depth, fidelity, convergence behaviour and computational complexity) can effectively compare Quantum AI algorithms to classical algorithms. Particular attention is given to hybrid variational algorithms, quantum-enhanced optimization algorithms, and quantum machine learning. Using benchmark datasets, simulation frameworks and new evaluation methods, there are several challenges in terms of reproducibility, hardware variability and algorithm/hardware co-design that emerge through analysis of the above metrics as well. Aside from focusing on a more broad view of how Performance Evaluation relates to developing more effective Quantum AI Solutions. In addition to viewing Benchmarking as a technical exercise solely, this chapter provides a more holistic view in relation to a human perspective of the situation. This paper outlines how performance evaluation practices can be used as a considerable and well-designed means of bridging the gap between the promise of Quantum Technology at an abstract level, and the ability of Quantum Technology to produce tangible outcomes in the real world by adopting a more comprehensive Governance-oriented approach to Benchmarking Quantum AI Systems. The purpose of this chapter is to assist the growing community of researchers, practitioners, and Strategic Technology Professionals by providing them with a clear and structured framework for Evaluation of Quantum AI Algorithms. There is a significant focus of the chapter on establishing important reference points that organizations can use to determine when an intelligent system has successfully achieved a true and validated quantum advantage. The ultimate goal is to assist in guiding the responsible development and implementation of Quantum AI to enable the Potential Benefits of Quantum AI to be realized, in ways that are meaningful and impactful.