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Ben McMurtry

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Proceedings Article | 15 November 2024 Presentation + Paper

Quantum-inspired annealing for optimisation of swarming behaviours

Rowan Sugden, Rowan Curtis, Ben McMurtry, Paul Martin, Samuel Bennett, Robert Lamb, Richard Claridge

Proceedings Volume 13202, 132020A (2024) https://doi.org/10.1117/12.3030888

KEYWORDS: Annealing, Quantum systems, Quantum annealing, Mathematical optimization, Quantum simulation, Quantum approximate optimization

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Decision making in a complex and fast moving environment can be challenging for both humans and autonomous systems. Many operational decisions can be expressed as combinatorial optimisation problems, which allows them to be expressed mathematically but can incur high computational overheads to solve. Quantum information processing promises the capability of solving large combinatorial optimisation problems such as positioning of tens to hundreds of sensor carrying drones within a set of complex objectives and constraints to create a connected ISR swarm. Whilst the mathematical approach for this optimisation is well characterised, extant quantum computers can also address the problem but do not currently have the scale to address medium to large complexity. Digital annealers are GPU-based devices that bridge the gap between quantum and classical computing in that they run quantum annealing-like algorithms with the accompanying problem complexity and speed benefits, but using the decades of scale advantage provided by a conventional silicon platform. This provides the opportunity to apply quantum information processing to existing ISR platforms reaping benefits in planning, resilience and rapid changing of objectives. This presentation will describe the application of digital annealing to control an ISR swarm and its implications on the requirements for future quantum information processing approaches and the potential route to near-term testing.

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