MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing

Abstract

Quantum software tools for a wide variety of design tasks on and across different levels of abstraction are crucial in order to eventually realize useful quantum applications. This requires practical and relevant benchmarks for new software tools to be empirically evaluated and compared to the current state of the art. Although benchmarks for specific design tasks are commonly available, the demand for an overarching cross-level benchmark suite has not yet been fully met and there is no mutual consolidation in how quantum software tools are evaluated thus far. In this work, we propose the MQT Bench benchmark suite (as part of the Munich Quantum Toolkit, MQT) based on four core traits: (1) cross-level support for different abstraction levels, (2) accessibility via an easy-to-use web interface (https://www.cda.cit.tum.de/mqtbench) and a Python package, (3) provision of a broad selection of benchmarks to facilitate generalizability, as well as (4) extendability to future algorithms, gate-sets, and hardware architectures. By comprising more than 70,000 benchmark circuits ranging from 2 to 130 qubits on four abstraction levels, MQT Bench presents a first step towards benchmarking different abstraction levels with a single benchmark suite to increase comparability, reproducibility, and transparency.

arXiv comment14 pages, 4 figures, MQT Bench is hosted at https://www.cda.cit.tum.de/mqtbench/, revised layout and updated tool

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Cited by in corpus (49)

• Benchmarking quantum computers
• The MQT Handbook: A Summary of Design Automation Tools and Software for Quantum Computing
• QFaaS: A Serverless Function-as-a-Service Framework for Quantum Computing
• Equivalence Checking of Quantum Circuits with the ZX-Calculus
• Compiler Optimization for Quantum Computing Using Reinforcement Learning
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• Computational Capabilities and Compiler Development for Neutral Atom Quantum Processors: Connecting Tool Developers and Hardware Experts
• Predicting Good Quantum Circuit Compilation Options
• MQT Predictor: Automatic Device Selection with Device-Specific Circuit Compilation for Quantum Computing
• Benchmarking the performance of quantum computing software
• Simulation Paths for Quantum Circuit Simulation with Decision Diagrams
• Quantum algorithms for scientific computing
• An Abstract Model and Efficient Routing for Logical Entangling Gates on Zoned Neutral Atom Architectures
• An Accurate and Efficient Analytic Model of Fidelity Under Depolarizing Noise Oriented to Large Scale Quantum System Design
• SCIM MILQ: An HPC Quantum Scheduler
• FragQC: An Efficient Quantum Error Reduction Technique using Quantum Circuit Fragmentation
• Near-Term Spin-Qubit Architecture Design via Multipartite Maximally-Entangled States
• Quantum Circuit Mutants: Empirical Analysis and Recommendations
• beSnake: A routing algorithm for scalable spin-qubit architectures
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• Towards Supporting QIR: Steps for Adopting the Quantum Intermediate Representation
• Mantra: Rewriting Quantum Programs to Minimize Trap-Movements for Zoned Rydberg Atom Arrays
• MIRAGE: Quantum Circuit Decomposition and Routing Collaborative Design using Mirror Gates
• Synchronization for Fault-Tolerant Quantum Computers
• The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
• AEQUAM: Accelerating Quantum Algorithm Validation through FPGA-Based Emulation
• Improving Figures of Merit for Quantum Circuit Compilation
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• Quantum Circuit Simulation with Fast Tensor Decision Diagram
• Multithreaded parallelism for heterogeneous clusters of QPUs
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• Qonductor: A Cloud Orchestrator for Quantum Computing
• Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures
• QSteed: A Resource-Virtualized and Hardware-Aware Quantum Compilation Framework for Real Quantum Computing Processors
• Equivalence Checking of Quantum Circuits via Path-Sum and Weighted Model Counting
• Graph Neural Network-Based Predictor for Optimal Quantum Hardware Selection
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• Distributing Quantum Circuits Using Pre-Distributed Entanglement Pairs over Quantum Networks
• PyQBench: a Python library for benchmarking gate-based quantum computers