Automatic design of quantum feature maps
We propose a new technique for the automatic generation of optimal ad-hoc ans\"atze for classification by using quantum support vector machine (QSVM). This efficient method is based on NSGA-II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningQuantum Machine LearningSimilar Papers 제목 키워드 기반
Automating quantum feature map design via large language models
Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces. Despite their theoretical promise, des…
Quantum Machine LearningUniversal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces
Encoding classical data into quantum states is considered a quantum feature map to map classical data into a quantum Hilbert space. This feature map provides opportunities to incorporate quantum advantages into machine l…
BIG-bench Machine LearningGeneral ClassificationQuantum Machine LearningNeural auto-designer for enhanced quantum kernels
Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the quantum feature map. However, the challenge of d…
feature selectionQuantum Machine LearningIterative Quantum Feature Maps
Quantum machine learning models that leverage quantum circuits as quantum feature maps (QFMs) are recognized for their enhanced expressive power in learning tasks. Such models have demonstrated rigorous end-to-end quantu…
Contrastive Learningimage-classificationImage ClassificationQuantum Machine LearningDecentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolutional neural network (QCNN) composed of…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Federated LearningKeyword Spotting+2