BACQ Project Aims to Establish Industry-Relevant Quantum Computing Benchmarks

The BACQ project, part of France’s national quantum strategy, aims to establish industry-relevant performance evaluation criteria for quantum computing. The consortium will focus on application-oriented benchmarks, including THALES, EVIDEN, CEA, CNRS, TERATEC, and LNE.

Unlike previous benchmarking initiatives, BACQ will use a suite of problem-solving benchmarks relevant to industries such as chemistry, aeronautics, electronics, and energy. The project will use the MYRIAD Q tool to aggregate low-level technical metrics and provide operational performance indicators for different quantum computing solutions. The project will also allow comparisons between different quantum machines and classical computers.

What is the BACQ Project and its Objectives?

The BACQ project, supported by the national program on measurement standards and evaluation of quantum technologies MetriQs France, is a part of the French national quantum strategy. The project is dedicated to application-oriented benchmarks for quantum computing. The consortium, which includes THALES, EVIDEN, an Atos business, CEA, CNRS, TERATEC, and LNE, aims to establish performance evaluation criteria of reference that are meaningful for industry users.

Quantum computing promises to revolutionize multiple technical fields and activity sectors, from optimization in logistics to simulation for research in physics or chemistry, engineering, or industry, passing through cryptography. Measuring the progress toward the quantum advantage and realizing such promises with objectivity and reliability is of high interest to potential end users and crucial for the future development of the domain. The challenges, especially to achieve comparable measurements, come from the diversity of the hardware platforms, their specificities in terms of physical characteristics and applications, their maturity that can still be low, and the potential rapid evolution of the technologies.

How Does BACQ Plan to Benchmark Quantum Computers?

A number of initiatives exist to benchmark the performance of quantum computers. Examples include Quantum VOLUME from IBM, SupermarQ from Super Tech, or Quantum LIN PACK from Berkeley Lab. The metrics used in these previous approaches are very technical and require familiarity with the technology. They, therefore, do not make it possible to derive operational indicators of the performance of the different families of algorithms executed on the different existing quantum computers.

BACQ is complementary to the benchmarking initiatives that only focus on low-level hardware physical criteria. The envisioned benchmark suite will be based on resolving several classes of problems covering important application domains of quantum computing, which are meaningful for industrial users. These problems are generic and could be relevant for different branches of industries and services like chemistry, aeronautics, electronics, and energy. Criteria will be defined for the resolution of each problem, some being hardware agnostic and others hardware dependent.

What is the Proposed Methodology of BACQ?

The proposed methodology of BACQ consists in the aggregation of low-level technical metrics and a multi-criteria analysis via the tool MYRIAD Q in order to provide operational performance indicators of the different quantum computing solutions and point out the service qualities of interest to the end users. The aggregation of the criteria and multi-criteria analysis allows fully explainable and transparent notations, comparisons between different quantum machines and with classical computers, as well as identification of the practical advantages of each quantum machine with respect to specific applications.

The project will address both analog machines (quantum simulators and annealers) and gate-based machines (Noisy Intermediate Scale Quantum (NISQ) and Fault Tolerant Quantum Computing (FTQC)). The followed practical approach is to have a suite of benchmarks adaptive to some extent, appropriate to the capabilities of the available quantum machines.

What are the Key Application Domains of Quantum Computing?

The key application domains of quantum computing that BACQ will focus on include simulation of Quantum Physics models, Optimization, Linear System Solving, and Prime Factorization. Machine Learning could be included in the Optimization application domain. These domains are chosen because they are generic and could be relevant for different branches of industries and services like chemistry, aeronautics, electronics, and energy.

What are the Expected Outcomes of the BACQ Project?

The expected outcomes of the BACQ project include the establishment of performance evaluation criteria of reference that are meaningful for industry users. The project aims to provide operational performance indicators of the different quantum computing solutions and point out the service qualities of interest to the end users. The project also aims to allow fully explainable and transparent notations, comparisons between different quantum machines and with classical computers, as well as identification of the practical advantages of each quantum machine with respect to specific applications.