QEDC Suite Enhances Quantum Computing Performance, Broadens Application Relevance

The Quantum Economic Development Consortium (QEDC) suite of Application-Oriented Benchmarks is a tool for measuring quantum computer performance. Led by Thomas Lubinski, researchers have expanded the suite’s relevance to more complex applications. They’ve introduced a method for improving landscape coverage, exemplified in a new scalable HHL linear equation solver benchmark. They’ve also added a Variational Quantum Eigensolver (VQE) implementation of a Hydrogen Lattice simulation and explored the suite’s use for machine learning applications. Additionally, they’ve included optimization and error mitigation in the benchmarking workflow, which could facilitate the exploration of algorithmic options and their performance impact.

What is the QEDC Suite of Application-Oriented Benchmarks?

The Quantum Economic Development Consortium (QEDC) suite of Application-Oriented Benchmarks is a tool designed to measure the performance characteristics of quantum computers in relation to real-world applications. The benchmark programs cover a range of problem sizes and inputs, capturing key performance metrics such as the quality of results, total execution time, and quantum gate resources consumed. The work described in this manuscript, led by Thomas Lubinski and a team of researchers from various institutions, investigates the challenges in broadening the relevance of this benchmarking methodology to applications of greater complexity.

The QEDC suite is similar to the SPEC benchmarks for classical computers, providing algorithms and simple applications structured as benchmarks that sweep over a range. These benchmarks are valuable for users as they provide measures that may be closer to their actual experience and in the context of recognizable application scenarios.

How Can the QEDC Suite Improve Landscape Coverage?

The researchers introduced a method for improving landscape coverage by systematically varying algorithm parameters. This functionality was exemplified in a new scalable HHL linear equation solver benchmark. The HHL algorithm is a quantum algorithm for solving systems of linear equations. The researchers implemented this algorithm as a benchmark and executed it to obtain results.

The results from executing the HHL benchmark showed that the method for improving landscape coverage was successful. This is a significant step in broadening the relevance of the QEDC suite to applications of greater complexity.

What is the Impact of the QEDC Suite on Hydrogen Lattice Simulation?

The researchers added a Variational Quantum Eigensolver (VQE) implementation of a Hydrogen Lattice simulation to the QEDC suite. They introduced a methodology for analyzing the result quality and runtime cost trade-off. The researchers observed a decrease in accuracy with an increased number of qubits, but only a mild increase in the execution time.

This addition to the QEDC suite and the methodology for analyzing the result quality and runtime cost trade-off is another significant step in broadening the relevance of the QEDC suite to applications of greater complexity.

How Can the QEDC Suite be Used for Machine Learning Applications?

The researchers explored the unique characteristics of a supervised machine-learning classification application as a benchmark to gauge the extensibility of the framework to new classes of application. Applying this to a binary classification problem revealed the increase in training time required for larger anzatz circuits and the significant classical overhead.

This exploration of the use of the QEDC suite for machine learning applications is a significant step in broadening the relevance of the QEDC suite to applications of greater complexity.

How Can the QEDC Suite Include Optimization and Error Mitigation?

The researchers added methods to include optimization and error mitigation in the benchmarking workflow. This allows them to identify a favorable trade-off between approximate gate synthesis and gate noise, observe the benefits of measurement error mitigation and a form of deterministic error mitigation algorithm, and to contrast the improvement with the resulting time overhead.

The inclusion of optimization and error mitigation in the benchmarking workflow is a significant step in broadening the relevance of the QEDC suite to applications of greater complexity. The researchers believe that the benchmark framework can be instrumental in facilitating the exploration of algorithmic options and their impact on performance.