Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Quantum Fuzzy Sets Revisited: Density Matrices, Decoherence, and the Q-Matrix Framework
In 2006 we proposed Quantum Fuzzy Sets, observing that states of a quantum register could serve as characteristic functions of fuzzy subsets, embedding Zadeh's unit interval into the Bloch sphere. That paper was delibera…
Quantum Machine LearningQuantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization
Quantum machine learning offers promising advantages for classification tasks, but noise, decoherence, and connectivity constraints in current devices continue to limit the efficient execution of feature map-based circui…
Quantum Machine LearningIQP Born Machines under Data-dependent and Agnostic Initialization Strategies
Quantum circuit Born machines based on instantaneous quantum polynomial-time (IQP) circuits are natural candidates for quantum generative modeling, both because of their probabilistic structure and because IQP sampling i…
Quantum Machine LearningEmbedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors
Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQ…
Quantum Machine LearningBeyond Barren Plateaus: A Scalable Quantum Convolutional Architecture for High-Fidelity Image Classification
While Quantum Convolutional Neural Networks (QCNNs) offer a theoretical paradigm for quantum machine learning, their practical implementation is severely bottlenecked by barren plateaus -- the exponential vanishing of gr…
Quantum Machine LearningImage ClassificationMitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning
Quantum machine learning models based on parameterized circuits can be viewed as Fourier series approximators. However, they often struggle to learn functions with multiple frequency components, particularly high-frequen…
Quantum Machine LearningNoise Models Impacts and Mitigation Strategies in Photonic Quantum Machine Learning
Photonic Quantum Machine Learning (PQML) is an emerging method to implement scalable, energy-efficient quantum information processing by combining photonic quantum computing technologies with machine learning techniques.…
Quantum Machine LearningCharacterization and upgrade of a quantum graph neural network for charged particle tracking
In the forthcoming years the LHC experiments are going to be upgraded to benefit from the substantial increase of the LHC instantaneous luminosity, which will lead to larger, denser events, and, consequently, greater com…
Quantum Machine LearningGraph Neural NetworkToward Generative Quantum Utility via Correlation-Complexity Map
We study a practical question in generative quantum machine learning: given a classical dataset, can we determine, before training, whether it is well suited to a quantum generative model? We focus on a class of quantum …
Quantum Machine LearningScaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation
Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning. Contemporary quantum machine learning is generally limited to toy examples or heavily restricted…
Dimensionality ReductionQuantum Machine LearningImage GenerationHybrid Quantum Temporal Convolutional Networks
Quantum machine learning models for sequential data face scalability challenges with complex multivariate signals. We introduce the Hybrid Quantum Temporal Convolutional Network (HQTCN), which combines classical temporal…
Quantum Machine LearningComparing Classical and Quantum Variational Classifiers on the XOR Problem
Quantum machine learning applies principles such as superposition and entanglement to data processing and optimization. Variational quantum models operate on qubits in high-dimensional Hilbert spaces and provide an alter…
Quantum Machine LearningQuantum Deep Learning: A Comprehensive Review
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalization, and scalability, can be enhanced base…
Quantum Machine LearningImage ClassificationSpectral Phase Encoding for Quantum Kernel Methods
Quantum kernel methods are promising for near-term quantum ma- chine learning, yet their behavior under data corruption remains insuf- ficiently understood. We analyze how quantum feature constructions degrade under cont…
Quantum Machine LearningTheory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach
Quantum reservoir computers (QRCs) have emerged as a promising approach to quantum machine learning, since they utilize the natural dynamics of quantum systems for data processing and are simple to train. Here, we consid…
Quantum Machine LearningAuto Quantum Machine Learning for Multisource Classification
With fault-tolerant quantum computing on the horizon, there is growing interest in applying quantum computational methods to data-intensive scientific fields like remote sensing. Quantum machine learning (QML) has alread…
Quantum Machine LearningChange DetectionStructured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding
Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and substantial quantum resources, which limits…
Quantum Machine LearningIllustration of Barren Plateaus in Quantum Computing
Variational Quantum Circuits (VQCs) have emerged as a promising paradigm for quantum machine learning in the NISQ era. While parameter sharing in VQCs can reduce the parameter space dimensionality and potentially mitigat…
Quantum Machine LearningKernel-based optimization of measurement operators for quantum reservoir computers
Finding optimal measurement operators is crucial for the performance of quantum reservoir computers (QRCs), since they employ a fixed quantum feature map. We formulate the training of both stateless (quantum extreme lear…
Quantum Machine LearningTime Series PredictionImage ClassificationReverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption
Quantum machine learning in cloud environments requires protecting sensitive data while enabling remote computation. Here we demonstrate the first realistic implementations of a perfectly-secure quantum homomorphic encry…
Quantum Machine Learning