Work

Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm

Year: 2024

Type: article

Abstract: There is an ongoing effort to find quantum speedups for learning problems. Recently [Y. Liu et al., Nat. Phys. 17, 1013 (2021)] proved an exponential speedup for quantum support vector machines by leveraging the speedup of Shor's algorithm. We expand upon this result and identify a speedup utilizing Grover's algorithm in the kernel of a support vector machine. To show the practicality of the kernel structure we apply it to a problem related to pattern matching, providing a practical yet provable advantage. Moreover, we show that combining quantum computation in a preprocessing step with classical methods for classification further improves classifier performance.

Source: Physical Review A

DOI: 10.1103/physreva.110.032434

Authors: T. Muser, E. Zapusek, V. Belis, F. Reiter

Institution: ETH Zurich

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Eidgenössische Technische Hochschule Zürich

Awards: Dissipation Engineering of Fault-Tolerant Quantum Computation and Phases for Quantum Metrology, G4430097687, G7253316779, G7861265189, G8216528182