An Efficient Quantum Classifier Based on Hamiltonian Representations
Tiblias, F Schroeder, A Zhang, Y Gachechiladze, M Gurevych, I
Tiblias, F
Schroeder, A
Zhang, Y
Gachechiladze, M
Gurevych, I
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Department
Natural Language Processing
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Conference proceeding
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Abstract
Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks. However, many studies rely on toy datasets or heavy feature reduction, raising concerns about their scalability. Progress is further hindered by hardware limitations and the significant costs of encoding dense vector representations on quantum devices. To address these challenges, we propose an efficient approach called Hamiltonian classifier that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings and computing predictions as their expectation values. In addition, we introduce two classifier variants with different scaling in terms of parameters and sample complexity. We evaluate our approach on text and image classification tasks, against well-established classical and quantum models. The Hamiltonian classifier delivers performance comparable to or better than these methods. Notably, our method achieves logarithmic complexity in both qubits and quantum gates, making it well-suited for large-scale, real-world applications.
Citation
F. Tiblias, A. Schroeder, Y. Zhang, M. Gachechiladze, I. Gurevych, "An Efficient Quantum Classifier Based on Hamiltonian Representations," 2026, pp. 67-89.
Source
Communications in Computer and Information Science
Conference
International Conference on Quantum Artifical Intelligence and Natural Language Processing
Keywords
46 Information and Computing Sciences, 40 Engineering, 4009 Electronics, Sensors and Digital Hardware, 4611 Machine Learning, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD)
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Source
International Conference on Quantum Artifical Intelligence and Natural Language Processing
Publisher
Springer Nature