Hybrid quantum learning with data reuploading on a small-scale superconducting quantum simulator
Aleksei Tolstobrov, Gleb Fedorov, Shtefan Sanduleanu, Shamil Kadyrmetov, Andrei Vasenin, Aleksey Bolgar, Daria Kalacheva, Viktor Lubsanov, Aleksandr Dorogov, Julia Zotova, Peter Shlykov, Aleksei Dmitriev, Konstantin Tikhonov, Oleg V. Astafiev
DOI 10.1103/PhysRevA.109.012411 · Physical Review A
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Abstract
Supervised quantum learning is an emergent multidisciplinary domain bridging between variational quantum algorithms and classical machine learning. Here, we study experimentally a hybrid classifier model using quantum hardware simulator (a linear array of four superconducting transmon artificial atoms) trained to solve multilabel classification and image recognition problems. We train a quantum circuit on simple binary and multilabel tasks, achieving classification accuracy around 95%, and a hybrid quantum model with data reuploading with accuracy around 90% when recognizing handwritten decimal digits. Finally, we analyze the inference time in experimental conditions and compare the performance of the studied quantum model with known classical solutions.
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