Quantum Machine Learning
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Benchmarks
Most implemented
PennyLane: Automatic differentiation of hybrid quantum-classical computations
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Better than classical? The subtle art of benchmarking quantum machine learning models
A divide-and-conquer algorithm for quantum state preparation
Papers
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 Learning