2017/03/31 by Maria Schuld, M. Schuld, Mark Fingerhuth +3 · 6 citations
Computer Science · Physics and Astronomy · #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum many-body systems #quant-ph
paper · pdf · doi:10.1209/0295-5075/119/60002
6 pages, 4 figures
arxiv created 2017/08/28 · openalex publication_date 2017/09/01 · openalex created_date 2017/09/15 · arxiv updated 2018/01/17 · openalex updated_date 2026/08/05
Lately, much attention has been given to quantum algorithms that solve pattern recognition tasks in machine learning. Many of these quantum machine learning algorithms try to implement classical models on large-scale universal quantum computers that have access to non-trivial subroutines such as Hamiltonian simulation, amplitude amplification and phase estimation. We approach the problem from the opposite direction and analyse a distance-based classifier that is realised by a simple quantum interference circuit. After state preparation, the circuit only consists of a Hadamard gate as well as two single-qubit measurements, and computes the distance between data points in quantum parallel. We demonstrate the proof-of-principle using the IBM Quantum Experience and analyse the performance of the classifier with numerical simulations, showing that it classifies surprisingly well for simple benchmark tasks.