2024/03/04 by Kevin Z. Shen, B. Jobst, Shen, Kevin +5 · 7 citations
Computer Science · Physics and Astronomy · #Artificial intelligence #Computer science #Deep learning #MNIST database #Pattern recognition (psychology) #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum mechanics #Theoretical and Computational Physics
paper · pdf · doi:10.48550/arxiv.2403.02405
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
The potential impact of quantum machine learning algorithms on industrial applications remains an exciting open question. Conventional methods for encoding classical data into quantum computers are not only too costly for a potential quantum advantage in the algorithms but also severely limit the scale of feasible experiments on current hardware. Therefore, recent works, despite claiming the near-term suitability of their algorithms, do not provide experimental benchmarking on standard machine learning datasets. We attempt to solve the data encoding problem by improving a recently proposed variational algorithm [1] that approximately prepares the encoded data, using asymptotically shallow circuits that fit the native gate set and topology of currently available quantum computers. We apply the improved algorithm to encode the Fashion-MNIST dataset [2], which can be directly used in future empirical studies of quantum machine learning algorithms. We deploy simple quantum variational classifiers trained on the encoded dataset on a current quantum computer ibmq-kolkata [3] and achieve moderate accuracies, providing a proof of concept for the near-term usability of our data encoding method.