2021/04/16 by Daniel K. Park, Carsten Blank, Francesco Petruccione · 9 citations
Computer Science · Physics and Astronomy · #Binary number #Dimension (graph theory) #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum algorithm #Quantum circuit #Quantum computer #Quantum error correction #Quantum gate #Quantum phase estimation algorithm #Quantum-Dot Cellular Automata #quant-ph
paper · pdf · doi:10.1109/ijcnn52387.2021.9533403
published as 2021 International Joint Conference on Neural Networks (IJCNN) · 7 pages, 6 figures
arxiv created 2021/04/16 · openalex created_date 2021/04/26 · openalex publication_date 2021/07/18 · arxiv updated 2021/09/24 · openalex updated_date 2026/08/05
To witness quantum advantages in practical settings, substantial efforts are required not only at the hardware level but also on theoretical research to reduce the computational cost of a given protocol. Quantum computation has the potential to significantly enhance existing classical machine learning methods, and several quantum algorithms for binary classification based on the kernel method have been proposed. These algorithms rely on estimating an expectation value, which in turn requires an expensive quantum data encoding procedure to be repeated many times. In this work, we calculate explicitly the number of repetition necessary for acquiring a fixed success probability and show that the Hadamard-test and the swap-test circuits achieve the optimal variance in terms of the quantum circuit parameters. The variance, and hence the number of repetition, can be further reduced only via optimization over data-related parameters. We also show that the kernel-based binary classification can be performed with a single-qubit measurement regardless of the number and the dimension of the data. Finally, we show that for a number of relevant noise models the classification can be performed reliably without quantum error correction. Our findings are useful for designing quantum classification experiments under limited resources, which is the common challenge in the noisy intermediate-scale quantum era.