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Quantum Computing Applications in Real-World Optimization

Ava L. Rodriguez, Julian Patel

Published Jun 19, 2019• 0 citations
Publisher

Abstract

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DOI
10.5281/zenodo.19451726

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With quantum computing technologies nearing the era of quantum supremacy, and of commercialization, near-term devices need to be tested with application-driven benchmarks. Here the authors contribute a comprehensive assessment of the readiness of quantum optimization for a real industrial problem: fault diagnosis in digital circuits. From the perspective of physics and application, they investigate the impact of next-generation quantum annealers in direct comparison to state-of-the-art classical heuristics. Although more challenging in nature, these real-world problems provide insight on the real-world performance of quantum optimization machines.
The application of AI-based forecasting methods has led to great progress in the prediction of complex systems. Among the AI-methods being used reservoir computing (RC) turns out to be so far the most promising approach as it combines superior prediction results with little CPU-needs for training. In Quantum Reservoir Computing (QRC) the reservoir that is commonly a random network, is replaced by a system of entangled qubits. Using a spin-network as ad hoc quantum reservoir it was demonstrated that a NARMA process can be well predicted using as few as 10 qubits [1]. Our recent first results suggest that even a 4-6 qubit system may be sufficient to predict the Lorenz-system as well as with “conventional” RC [2]. In the forthcoming research project “Neuromorphic Quantum Computing (NeMoQC)” within the framework of the DLR Quantum Computing Initiative (QCI) we will now investigate systematically which quantum reservoirs are best suited for prediction and optimization tasks. In a software/hardware codesign we will seek for hardware realizations of QRC. Further, these newly developed QC-based forecasting and optimization methods are to be utilized in real world applications. Industrial partners for both the hardware design and the applications in real world use cases are welcome to join our project. [1] R. Martinez-Pena et al., PRL, 127, 100502 (2021) [2] J. Steinegger, Masterthesis LMU (ongoing)
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