Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps
원문 초록
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 over classical computing. One of the major challenges in implementing such algorithms is the choice of quantum embeddings and the use of a functionally correct quantum variational circuit. In this paper, we present an application of QSVM (Quantum Support Vector Machines) to predict if a person will require mental health treatment in the tech world in the future using the dataset from OSMI Mental Health Tech Surveys. We achieve this with non-classically simulable feature maps and prove that NISQ HQC Architectures for Quantum Machine Learning can be used alternatively to create good performance models in near-term real-world applications.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
The interplay of robustness and generalization in quantum machine learning
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
QuXAI: Explainers for Hybrid Quantum Machine Learning Models
Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
Fine-Tuning Large Language Models on Quantum Optimization Problems for Circuit Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.