What Is Fuzzy Logic – And Why It Matters to Us

Giuntini, Roberto; Paoli, Francesco; Freytes, Héctor; Ledda, Antonio; Sergioli, Giuseppe

doi:10.1007/978-3-642-35641-4_32

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the recent work in quantum machine learning has seen the development of multi-class quantum and quantuminspired classifiers that avoid these heuristic strategies. [9][10][11][12][13][14][15][16] Most recently, the quantum-inspired methods [15,16] use techniques from quantum state discrimination for multi-class classification. Other work has seen the development of quantum convolutional neural networks (QCNNs) for multi-class classification.…”

Section: Introductionmentioning

confidence: 99%

A Multi‐Class Quantum Kernel‐Based Classifier

Pillay,

Sinayskiy,

Jembere

et al. 2023

Adv Quantum Tech

View full text Add to dashboard Cite

Multi‐class classification problems are fundamental in many varied domains in research and industry. A popular strategy for solving multi‐class classification problems involves first transforming the problem into many binary classification problems. However, this requires the number of binary classification models that need to be 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 number of classes. In addition, unlike other architectures for multi‐class quantum classifiers, the state reconstruction of a single qubit yields sufficient information for multi‐class classification tasks. Both analytical results and numerical simulations show that this classifier is not only effective when applied to diverse classification problems but also robust to certain conditions of noise.

show abstract