Modeling Feature Maps for Quantum Machine Learning
Quantum Machine Learning (QML) offers significant potential for complex tasks like genome sequence classification, but quantum noise on Noisy Intermediate-Scale Quantum (NISQ) devices poses practical challenges. This study systematically evaluates how various quantum noise models including dephasing, amplitude damping, depolarizing, thermal noise, bit-flip, and phase-flip affect key QML algorithms (QSVC, Peg-QSVC, QNN, VQC) and feature mapping techniques (ZFeatureMap, ZZFeatureMap, and PauliFeatureMap). Results indicate that QSVC is notably robust under noise, whereas Peg-QSVC and QNN are more sensitive, particularly to depolarizing and amplitude-damping noise. The PauliFeatureMap is especially vulnerable, highlighting difficulties in maintaining accurate classification under noisy conditions. These findings underscore the critical importance of feature map selection and noise mitigation strategies in optimizing QML for genomic classification, with promising implications for personalized medicine.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationQuantum Machine LearningSimilar Papers 제목 키워드 기반
Investigating Quantum Feature Maps in Quantum Support Vector Machines for Lung Cancer Classification
In recent years, quantum machine learning has emerged as a promising intersection between quantum physics and artificial intelligence, particularly in domains requiring advanced pattern recognition such as healthcare. Th…
Cancer ClassificationDiagnosticLung Cancer DiagnosisQuantum Machine Learning+1Universal 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 LearningAutomating 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 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 LearningQuantum Machine Learning for Image Classification: A Hybrid Model of Residual Network with Quantum Support Vector Machine
Recently, there has been growing attention on combining quantum machine learning (QML) with classical deep learning approaches, as computational techniques are key to improving the performance of image classification tas…
Dimensionality ReductionQuantum Machine LearningImage Classification