QuSquare: Scalable Quality-Oriented Benchmark Suite for Pre-Fault-Tolerant Quantum Devices

TL;DR

QuSquare is a comprehensive benchmark suite designed to evaluate and compare the performance of diverse pre-fault-tolerant quantum devices through scalable, fair, and reproducible tests at both system and application levels.

Contribution

It introduces a novel, hardware-agnostic benchmarking framework with four specific tests to assess quantum hardware performance across different architectures.

Findings

Provides a scalable, fair benchmarking methodology

Enables cross-platform performance comparison

Supports development of quantum performance standards

Abstract

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.