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Catherine McGeoch

Publications and source records attributed to Catherine McGeoch.

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How NOT to Fool the Masses When Giving Performance Results for Quantum Computers

In 1991, David Bailey wrote an article describing techniques for overstating the performance of massively parallel computers. Intended as a lighthearted protest against the practice of inflating benchmark results in order to ``fool the masses" and boost sales, the paper sparked development of procedural standards that help benchmarkers avoid methodological errors leading to unjustified and misleading conclusions. Now that quantum computers are starting to realize their potential as viable alternatives to classical computers, we can see the mistakes of three decades ago being repeated by a new batch of researchers who are unfamiliar with this history and these standards. Inspired by Bailey's model, this paper presents four suggestions for newcomers to quantum performance benchmarking, about how not to do it. They are: (1) Don't claim superior performance without mentioning runtimes; (2) Don't report optimized results without mentioning the tuning time needed to optimize those results; (3) Don't claim faster runtimes for (or in comparison to) solvers running on imaginary platforms; and (4) No cherry-picking (without justification and qualification). Suggestions for improving current practice appear in the last section.

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Optimization Applications as Quantum Performance Benchmarks

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-Cut problems using gate-model quantum devices and a quantum annealing device. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through compelling visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. As an illustration, we explore the factors that influence quantum computing system throughput, using results obtained through execution on various quantum simulators and quantum hardware systems.

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Hybrid quantum annealing for larger-than-QPU lattice-structured problems

Quantum processing units (QPUs) executing annealing algorithms have shown promise in optimization and simulation applications. Hybrid algorithms are a natural bridge to additional applications of larger scale. We present a straightforward and effective method for solving larger-than-QPU lattice-structured Ising optimization problems. Performance is compared against simulated annealing with promising results, and improvement is shown as a function of the generation of D-Wave QPU used.

quant-ph

A cross-disciplinary introduction to quantum annealing-based algorithms

A central goal in quantum computing is the development of quantum hardware and quantum algorithms in order to analyse challenging scientific and engineering problems. Research in quantum computation involves contributions from both physics and computer science, hence this article presents a concise introduction to basic concepts from both fields that are used in annealing-based quantum computation, an alternative to the more familiar quantum gate model. We introduce some concepts from computer science required to define difficult computational problems and to realise the potential relevance of quantum algorithms to find novel solutions to those problems. We introduce the structure of quantum annealing-based algorithms as well as two examples of this kind of algorithms for solving instances of the max-SAT and Minimum Multicut problems. An overview of the quantum annealing systems manufactured by D-Wave Systems is also presented.

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