A Multi-Class SWAP-Test Classifier
S M Pillay, I Sinayskiy, E Jembere, F Petruccione

TL;DR
This paper introduces the first multi-class SWAP-Test quantum classifier that reduces model complexity and resource requirements, demonstrating effectiveness and noise robustness through analytical and simulation results.
Contribution
It presents a novel multi-class quantum classifier based on the SWAP-Test that requires fewer qubits and maintains invariance to the number of classes, unlike previous models.
Findings
Effective across diverse classification problems
Invariant resource requirements regardless of class number
Robust performance under certain noise conditions
Abstract
Multi-class classification problems are fundamental in many varied domains in research and industry. To solve multi-class classification problems, heuristic strategies such as One-vs-One or One-vs-All can be employed. However, these strategies require the number of binary classification models developed to grow with the number of classes. Recent work in quantum machine learning has seen the development of multi-class quantum classifiers that circumvent this growth by learning a mapping between the data and a set of label states. This work presents the first multi-class SWAP-Test classifier inspired by its binary predecessor and the use of label states in recent work. With this classifier, the cost of developing multiple models is avoided. In contrast to previous work, the number of qubits required, the measurement strategy, and the topology of the circuits used is invariant to the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Neural Networks and Reservoir Computing
