Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Hybrid Quantum-Classical NLP Classification with Compact Semantic Representations: An Experimental Analysis of Representation Compression
Large language and sentence-embedding models provide rich semantic representations, but their high dimensionality poses a challenge for near-term quantum machine learning (QML), where quantum circuits can process only a …
Quantum Machine LearningDimensionality ReductionA Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability
We present a unified reinforcement-learning (RL) framework that discovers compact parametrized quantum circuits (PQCs) for data-scarce device modeling. A graph neural network (GNN) policy optimized by proximal policy opt…
Quantum Machine LearningGraph Neural NetworkCharacterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access
Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accompanying QML is also starting to be studied, which inherit privacy leaka…
Quantum Machine LearningQuantum-Grassmann-Plucker Token Mixing for Deep Learning-Based Post-Disaster Damage Assessment
Timely post-disaster building damage assessment from satellite imagery is a critical engineering decision support task, yet it remains constrained by class imbalance, ambiguous intermediate damage states, and limited cro…
Building Damage AssessmentQuantum Machine LearningImage ClassificationComparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data
The classification and regression of particle collision events constitute a persistent computational challenge in experimental high energy physics, where large volumes of simulated data must be processed with both speed …
Quantum Machine LearningBernstein-Vazirani Networks: Quantum Machine Learning by Interference
We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference for supervised learning, demonstrated on vision and representation learning tasks.…
Quantum Machine LearningRepresentation LearningQuantum-Logic Tsetlin Machines: Interpretable Quantum Machine Learning with Commuting Projector Clauses
Tsetlin Machines (TMs) learn interpretable Boolean clauses using finite-state automata. We introduce the Quantum-Logic Tsetlin Machine (QL-TM), which replaces Boolean literals with quantum propositions represented by pro…
Quantum Machine LearningHow Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits
Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of c…
Quantum Machine LearningCautious optimism for deep parameterized quantum circuits
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performance on unseen data changes as the number…
Quantum Machine LearningClassical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data, potentially with fewer parameters and f…
Quantum Machine LearningAnomaly DetectionInterpreting Quantum Learning Models via Stochastic Processes
Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement. While such models can exhibit computational advantages, their internal functioning and decision …
Quantum Machine LearningDecision MakingFrom hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models
We characterize and compare the inherent interpretability offerings of a standard linear model with a single qubit mixed state model for the task of supervised binary classification. A side by side comparison reveals tha…
Quantum Machine LearningBinary ClassificationFeature ImportanceInput-Aware Dynamic Backdoor Attack Against Quantum Neural Networks
Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable t…
Quantum Machine LearningContrastive LearningProvable learning separation for predicting time-evolution of quantum many-body systems
Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantum machine learning (QML) tasks that exhi…
Quantum Machine LearningCanonical quantization of neurons
Canonical quantization provides a systematic procedure for constructing quantum models from classical Hamiltonians. Here, we apply this principle to a fundamental computational primitive of machine learning: the neuron. …
Quantum Machine LearningHybrid quantum-classical neural network for sentiment analysis
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid qu…
Quantum Machine LearningSentiment AnalysisTransfer LearningQuantum vs. Classical Machine Learning: A Unified Empirical Comparison
Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches. At this stage, the evidence supporting the pe…
Quantum Machine LearningReinforcement LearningBeyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning
As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory. In …
Quantum Machine LearningBinary ClassificationExploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding
Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Intermediate-Scale Quantum (NISQ) devices remains an empirical question, es…
Quantum Machine LearningParameter-Efficient Continuous-Variable Photonic Quantum Neural Networks for Edge Quantum AI: Demonstration in Oral Cancer Detection
Early detection of oral cancer markedly improves clinical outcomes, yet specialized diagnostic tools remain scarce in low-resource settings. Smartphone-based screening is a scalable alternative but needs lightweight mode…
Medical Image ClassificationQuantum Machine Learning