Multiple Embeddings for Quantum Machine Learning
This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies, allowing the model to fully exploit the diversity of quantum computing when processing various datasets. Experimental results validate the effectiveness of the proposed framework, demonstrating significant improvements over existing state-of-the-art methods and achieving superior performance in practical applications.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityQuantum Machine LearningSimilar Papers 제목 키워드 기반
Reverse Map Projections as Equivariant Quantum Embeddings
We introduce the novel class $(E_\alpha)_{\alpha \in [-\infty,1)}$ of reverse map projection embeddings, each one defining a unique new method of encoding classical data into quantum states. Inspired by well-known map pr…
Quantum Machine LearningVariational Quantum Kernels with Task-Specific Quantum Metric Learning
Quantum kernel methods, i.e., kernel methods with quantum kernels, offer distinct advantages as a hybrid quantum-classical approach to quantum machine learning (QML), including applicability to Noisy Intermediate-Scale Q…
feature selectionMetric LearningQuantum Machine LearningTransfer LearningTailor Made Embeddings for Quantum Machine Learning
Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations. In this work, we extend this paradig…
Quantum Machine LearningEmbedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
Quantum Support Vector Machines face scalability challenges due to high-dimensional quantum states and hardware limitations. We propose an embedding-aware quantum-classical pipeline combining class-balanced k-means disti…
Quantum Machine LearningQuantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps
Hybrid Quantum-Classical (HQC) Architectures are used in near-term NISQ Quantum Computers for solving Quantum Machine Learning problems. The quantum advantage comes into picture due to the exponential speedup offered ove…
BIG-bench Machine LearningQuantum Machine Learning