As quantum computing systems continue to mature, there is an increasing need for benchmarking methodologies that capture performance in terms of meaningful, application-level metrics. In this work, we present a scalable framework for application-level quantum benchmarking that is designed to support internal system evaluation and cross-platform comparison across technology providers. Our framework is guided by a set of core principles, including measurability, simplicity, scalability, and extensibility. We present 13 benchmark families that reflect realistic workloads across multiple domains. This enables the systematic evaluation of the quality of solutions, the total execution time, total used energy, as well as Time-to-Solution. The benchmarks are designed to be reproducible, interpretable across stakeholder groups, and adaptable to evolving system capabilities. The framework aims to bridge the gap between low-level performance metrics and real-world value, providing a unified approach to assessing quantum systems. The resulting benchmarks support development and validation and contribute to the foundation of industry-wide benchmarking standards.
As quantum computing systems continue to mature, there is an increasing need for benchmarking methodologies that capture performance in terms of meaningful, application-level metrics. In this work, we present a scalable framework for application-level quantum benchmarking that is designed to support internal system evaluation and cross-platform comparison across technology providers. Our framework is guided by a set of core principles, including measurability, simplicity, scalability, and extensibility. We present 13 benchmark families that reflect realistic workloads across multiple domains. This enables the systematic evaluation of the quality of solutions, the total execution time, total used energy, as well as Time-to-Solution. The benchmarks are designed to be reproducible, interpretable across stakeholder groups, and adaptable to evolving system capabilities. The framework aims to bridge the gap between low-level performance metrics and real-world value, providing a unified approach to assessing quantum systems. The resulting benchmarks support development and validation and contribute to the foundation of industry-wide benchmarking standards.
The fragmented landscape of quantum computer benchmarks, characterized by system-specific tools and inconsistent evaluation methodologies, hinders reliable cross-platform performance assessment. We introduce Metriq, an open-source collaborative platform for reproducible cross-platform quantum benchmarking that integrates benchmark definition and execution, data collection, and public presentation into a unified workflow. The Metriq benchmark suite spans both system-level metrics that characterize fundamental device properties such as entanglement quality, gate performance, and circuit speed, as well as application-inspired protocols that assess performance on quantum machine learning, optimization, and quantum simulation tasks. Benchmarks are chosen to scale with processor size, and the framework incorporates cost and resource estimation to support practical evaluation. Using Metriq, we collect and publicly release results from more than ten quantum computers across multiple hardware vendors, enabling systematic cross-platform comparison. The resulting curated dataset also reveals the practical strengths and limitations of individual benchmarks, creating a feedback loop that informs the ongoing refinement of the suite. To summarize performance across the benchmark suite, we introduce the Metriq Score, a composite index aggregating benchmark outcomes. We further present cross-benchmark analyses enabled by the shared dataset and their correlations with hardware calibration metrics. Through open development and data sharing, Metriq provides a practical foundation for reproducible benchmarking of quantum computers as hardware and benchmarking methods continue to evolve.
As quantum processors continue to scale in size and complexity, the need for well-defined, reproducible, and technology-agnostic performance metrics becomes increasingly critical. Here we present a suite of scalable quantum computing benchmarks developed as key performance indicators (KPIs) within the EU Quantum Flagship. These proposed benchmarks are designed to assess holistic system performance rather than isolated components, and to remain applicable across both noisy intermediate-scale quantum (NISQ) devices and future fault-tolerant architectures. We introduce four core benchmarks addressing complementary aspects of quantum computing capability: large multi-qubit circuit execution via a Clifford Volume benchmark, scalable multipartite entanglement generation through GHZ-state preparation, a benchmark based on the application of Shor's period-finding subroutine to simple functions, and a protocol quantifying the benefit of quantum error correction using Bell states. Each benchmark is accompanied by clearly specified protocols, reporting standards, and scalable evaluation methods. Together, these KPIs provide a coherent framework for transparent and fair performance assessment across quantum hardware platforms and for tracking progress late-NISQ toward early fault-tolerant quantum computation.
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.
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.
The emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. This problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.
Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances in quantum computing toward practical applications, the need to analyze and compare different quantum solutions increases. While different low-level benchmarks for QC exist, these benchmarks do not provide sufficient insights into real-world application-level performance. We propose an application-centric benchmark method and the QUantum computing Application benchmaRK (QUARK) framework to foster the investigation and creation of application benchmarks for QC. This paper establishes three significant contributions: (1) it makes a case for application-level benchmarks and provides an in-depth "pen and paper" benchmark formulation of two reference problems: robot path and vehicle option optimization from the industrial domain; (2) it proposes the open-source QUARK framework for designing, implementing, executing, and analyzing benchmarks; (3) it provides multiple reference implementations for these two reference problems based on different known, and where needed, extended, classical and quantum algorithmic approaches and analyzes their performance on different types of infrastructures.