Application-Oriented Performance Benchmarks for Quantum Computing

Abstract

In this work we introduce an open source suite of quantum application-oriented performance benchmarks that is designed to measure the effectiveness of quantum computing hardware at executing quantum applications. These benchmarks probe a quantum computer's performance on various algorithms and small applications as the problem size is varied, by mapping out the fidelity of the results as a function of circuit width and depth using the framework of volumetric benchmarking. In addition to estimating the fidelity of results generated by quantum execution, the suite is designed to benchmark certain aspects of the execution pipeline in order to provide end-users with a practical measure of both the quality of and the time to solution. Our methodology is constructed to anticipate advances in quantum computing hardware that are likely to emerge in the next five years. This benchmarking suite is designed to be readily accessible to a broad audience of users and provides benchmarks that correspond to many well-known quantum computing algorithms.

33 pages, 36 figures; Added to Section VI about Impact of Compiler Optimization Techniques; Updated 20230105 for clarity after review feedback

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Cited by in corpus (45)

• MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing
• Challenges and Opportunities in Quantum Optimization
• Application-Oriented Performance Benchmarks for Quantum Computing
• Benchmarking a trapped-ion quantum computer with 30 qubits
• Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers
• Benchmarking quantum computers
• Simultaneous execution of quantum circuits on current and near-future NISQ systems
• Benchmarking of Different Optimizers in the Variational Quantum Algorithms for Applications in Quantum Chemistry
• Quantum Fourier Transform using Dynamic Circuits
• Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
• Applying Grover's Algorithm to Hash Functions: A Software Perspective
• Comprehensive characterization of three-qubit Grover search algorithm on IBM's 127-qubit superconducting quantum computers
• Benchmarking digital quantum simulations above hundreds of qubits using quantum critical dynamics
• Open Hardware Solutions in Quantum Technology
• A Practical Introduction to Benchmarking and Characterization of Quantum Computers
• Quantum algorithms for scientific computing
• Volumetric Benchmarking of Error Mitigation with Qermit
• A Framework for Curriculum Transformation in Quantum Information Science and Technology Education
• Improving Performance in Combinatorial Optimization Problems with Inequality Constraints: An Evaluation of the Unbalanced Penalization Method on D-Wave Advantage
• Quantum Fourier Iterative Amplitude Estimation
• Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK
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• A Comprehensive Cross-Model Framework for Benchmarking the Performance of Quantum Hamiltonian Simulations
• Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem
• Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
• Learning to rank quantum circuits for hardware-optimized performance enhancement
• Empirical learning of dynamical decoupling on quantum processors
• Channel Attention for Quantum Convolutional Neural Networks
• Quantum integration of decay rates at second order in perturbation theory
• Survey on Computational Applications of Tensor Network Simulations
• Scalable Full-Stack Benchmarks for Quantum Computers
• Q-gen: A Parameterized Quantum Circuit Generator
• Learning a quantum computer's capability
• Counting collisions in random circuit sampling for benchmarking quantum computers
• Improving Qubit Routing by Using Entanglement Mediated Remote Gates
• Unconditionally separating noisy ${\mathsf{QNC}}^0$ from bounded polynomial threshold circuits of constant depth
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