Abstract
Quantum computing has great potential for advancing machine learning algorithms beyond classical reach. Even though full-fledged universal quantum computers do not exist yet, its expected benefits for machine learning can already be shown using simulators and already available quantum hardware. In this work, we consider a distance-based classification algorithm and modify it to be run on actual early stage quantum hardware. We extend upon earlier work and present a non-trivial reduction using only two qubits. The algorithm is consequently run on a two-qubit silicon spin quantum computer. We show that the results obtained using the two-qubit silicon spin quantum computer are similar to the theoretically expected results.
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This work is the result of the quantum technology project of TNO. There are no conflict of interests for the authors.
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Neumann, N.M.P. (2021). Classification Using a Two-Qubit Quantum Chip. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_6
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