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Data compression for quantum machine learning

Dilip, Rohit and Liu, Yu-Jie and Smith, Adam and Pollmann, Frank (2022) Data compression for quantum machine learning. Physical Review Research, 4 (4). Art. No. 043007. ISSN 2643-1564. doi:10.1103/physrevresearch.4.043007.

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The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of achieving quantum speedups in machine learning tasks. These devices, however, are composed of a small number of qubits and can faithfully run only short circuits. This puts many proposed approaches for quantum machine learning beyond currently available devices. We address the problem of compressing classical data into efficient representations on quantum devices. Our proposed methods allow both the required number of qubits and depth of the quantum circuit to be tuned. We achieve this by using a correspondence between matrix-product states and quantum circuits and further propose a hardware-efficient quantum circuit approach, which we benchmark on the Fashion-MNIST dataset. Finally, we demonstrate that a quantum circuit-based classifier can achieve competitive accuracy with current tensor learning methods using only 11 qubits.

Item Type:Article
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URLURL TypeDescription
Dilip, Rohit0000-0002-1820-0321
Liu, Yu-Jie0000-0002-7657-9464
Additional Information:R.D. acknowledges Sheng-Hsuan Lin for helpful discussions and technical assistance. Y.-J.L. was supported by the Max Planck Gesellschaft (MPG) through the International Max Planck Research School for Quantum Science and Technology (IMPRS-QST). A.S. was partly supported by a Research Fellowship from the Royal Commission for the Exhibition of 1851. F.P. acknowledges support of the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation program (Grant Agreement No. 771537). F.P. also acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy Grant No. EXC-2111-390814868. F.P.'s research is part of the Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus.
Funding AgencyGrant Number
International Max Planck Research School for Quantum Science and TechnologyUNSPECIFIED
Royal Commission for the Exhibition of 1851UNSPECIFIED
European Research Council (ERC)771537
Deutsche Forschungsgemeinschaft (DFG)EXC-2111-390814868
Hightech Agenda Bayern PlusUNSPECIFIED
Issue or Number:4
Record Number:CaltechAUTHORS:20221114-805047300.10
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117857
Deposited By: Research Services Depository
Deposited On:29 Nov 2022 17:59
Last Modified:29 Nov 2022 17:59

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