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 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.
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 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.
As quantum technologies continue to advance, the proliferation of hardware architectures with diverse capabilities and limitations has underscored the importance of benchmarking as a tool to compare performance across platforms. Achieving fair, scalable and consistent evaluations is a key open problem in quantum computing, particularly in the pre-fault-tolerant era. To address this challenge, we introduce QuSquare, a quality-oriented benchmark suite designed to provide a scalable, fair, reproducible, and well-defined framework for assessing the performance of quantum devices across hardware architectures. QuSquare consists of four benchmark tests that evaluate quantum hardware performance at both the system and application levels: Partial Clifford Randomized, Multipartite Entanglement, Transverse Field Ising Model (TFIM) Hamiltonian Simulation, and Data Re-Uploading Quantum Neural Network (QNN). Together, these benchmarks offer an integral, hardware-agnostic, and impartial methodology to quantify the quality and capabilities of current quantum computers, supporting fair cross-platform comparisons and fostering the development of future performance standards.
Quantum computing hardware is advancing rapidly toward utility-scale machines that will enable scientific breakthroughs. Many teams are pursuing distinct and difficult-to-compare routes to this goal, using different qubit technologies and logical architectures. Tracking progress toward quantum utility therefore requires rigorous benchmarks that measure computational capability relative to utility-scale challenge problems and enable fair comparison across disparate platforms. Here we demonstrate direct, cross-platform measurement of quantum computational capability using a new benchmark that quantifies the size of the largest computationally relevant quantum circuits that a machine can execute successfully and the speed at which it can execute them. We apply this quantum universal operation performance system (QUOPS) experimentally to leading processors from Quantinuum, Google, and IBM, computing directly on physical qubits. Translating state-of-the-art resource requirements for recognized challenge problems that represent useful quantum computation into effective QUOPS circuit sizes shows that computational capability must grow by 5 orders of magnitude, motivating fault-tolerant approaches. We use the same benchmark to assess the performance of a simple fault-tolerant logical-qubit processor implemented on up to eight [[7,1,3]]-encoded logical qubits using Quantinuum Helios-1, and project the growth of capability across successive generations of fault-tolerant quantum computers to show how QUOPS can track progress toward quantum scientific utility.
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.
The superiority of Quantum algorithms over classical algorithms, wherein the quantum algorithms can provide an exponential speedup, is a well-proven phenomenon. Quantum computing simulations on classical computers are of utmost importance in preparing the research community for the quantum computing revolution, designing new algorithms, and validating the results obtained from the quantum hardware. This paper demonstrates the performance benchmarking results of the indigenously developed density-matrix-based quantum computing simulator QSim (qctoolkit.in). We have adopted a framework called “Application-Oriented Performance Benchmarks for Quantum Computing” for the benchmarking procedure and modified it for our cause. We evaluated the performance of the simulator on CPU, GPU, and the Vector Accelerator by running the quantum algorithms with varying numbers of qubits in each cycle, with and without the insertion of noise parameters, to evaluate various metrics. The results obtained will provide the performance aspects of the simulator on the described platforms.