Scalable Measurement-Based Quantum Simulation Patterns for Benchmarking

TL;DR

This paper introduces QPatLib, a library of measurement patterns for quantum simulation, aiming to standardize and facilitate pattern optimization and benchmarking on near-term quantum devices.

Contribution

The paper presents a new dataset of measurement patterns, along with a workflow for generating and benchmarking them for measurement-based quantum simulation.

Findings

Provides a standardized dataset for pattern optimization.

Includes benchmark patterns for quantum unitary evolution.

Defines patterns with different conventions for scalability.

Abstract

Measurement-based quantum computing uses measurement patterns on predefined quantum resource states to execute quantum logic. Quantum simulation offers an important use case on near-term devices. However, pattern optimization depends on the multivariable interplay between hardware and software constraints and is therefore use-dependent and highly non-trivial. Optimization of large-scale patterns under realistic assumptions remains a barrier. We announce the release of the quantum measurement pattern library QPatLib, a dataset that, in v1.0, presents patterns for use in measurement-based quantum simulation. We present the workflow for generating patterns that execute Pauli-string unitaries needed for many quantum algorithms. We provide benchmark patterns for measurement-based quantum unitary evolution. The measurement patterns are defined with different conventions for commuting Pauli-string subsets to allow scaling of pattern size and complexity. The purpose of the library is to (i) serve as a standardized testbed for pattern-optimization protocols for measurement-based quantum simulation routines, (ii) offer a suite of patterns for direct use on hardware, (iii) provide data to empirically justify pattern design principles, and (iv) provide a flexible resource for future storage and use of measurement-based patterns beyond quantum simulation.