MCTS discovers superior data encoding circuits for QCCNNs that outperform standard encodings on medical datasets, with effective rank of feature maps serving as a performance predictor.
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Better than classical? the subtle art of benchmark- ing quantum machine learning models
15 Pith papers cite this work. Polarity classification is still indexing.
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QCNN layers equivariant under pixel cyclic shifts are exactly characterized as Fourier-mode multiplexers after QFT, enabling a deep network with constant expected gradient norm at initialization.
A Pauli-transfer-matrix analysis of QELMs reveals the full set of nonlinear Pauli features generated by encoding and transformed by quantum channels, producing an interpretable classical nonlinear vector autoregression model that approximates flow maps in dynamical systems.
MerLin is a new open-source discovery engine for photonic and hybrid quantum machine learning that integrates circuit simulations into standard ML frameworks and reproduces 18 prior works as reusable benchmarks.
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
SBQE encodes data via learnable shot distributions over initial states to form mixed quantum representations, achieving 89.1% accuracy on Semeion and 80.95% on Fashion MNIST without encoding gates.
A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.
Quantum kernels in QSVM deliver higher minority-class F1 scores than classical linear or RBF kernels on medical foundation model embeddings for binary insurance classification, avoiding classical collapse in noiseless simulation.
Classical machine learning models outperform variational quantum models on population-level prediction of heat-related physiological events, while quantum models still exhibit non-trivial learning on the harmonized datasets.
Quantum-inspired 1024-D document embeddings exhibit weak, unstable ranking performance and structural geometric limitations, performing better as auxiliary components in hybrid lexical-embedding retrieval systems.
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
IQPopt is a JAX-based software tool enabling classical optimization of IQP circuits with thousands of qubits via efficient simulation of Pauli-Z expectation values, plus a module for quantum generative model training.
This review structures current evidence for quantum-in-biology, quantum-for-biology, and biology-for-quantum, identifying mature cases like enzymatic tunneling and radical-pair magnetoreception while flagging unresolved topics and needed benchmarks.
QCNN, QRNN, and QViT perform well on low-feature data but degrade on high-feature datasets, with QViT most robust to quantum noise and classical-style models better against adversarial noise.
A correlation-guided hybrid quantum-classical model using QAOA achieves 84.6% accuracy on crime pattern classification with reduced trainable parameters compared to classical machine learning baselines.
citing papers explorer
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Discovering Data Encoding Strategies for Quantum-Classical Neural Networks Using Monte Carlo Tree Search
MCTS discovers superior data encoding circuits for QCCNNs that outperform standard encodings on medical datasets, with effective rank of feature maps serving as a performance predictor.
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Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexers
QCNN layers equivariant under pixel cyclic shifts are exactly characterized as Fourier-mode multiplexers after QFT, enabling a deep network with constant expected gradient norm at initialization.
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Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach
A Pauli-transfer-matrix analysis of QELMs reveals the full set of nonlinear Pauli features generated by encoding and transformed by quantum channels, producing an interpretable classical nonlinear vector autoregression model that approximates flow maps in dynamical systems.
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MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning
MerLin is a new open-source discovery engine for photonic and hybrid quantum machine learning that integrates circuit simulations into standard ML frameworks and reproduces 18 prior works as reusable benchmarks.
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Soft-Quantum Algorithms
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
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Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks
SBQE encodes data via learnable shot distributions over initial states to form mixed quantum representations, achieving 89.1% accuracy on Semeion and 80.95% on Fashion MNIST without encoding gates.
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Quantum Machine Learning for State Tomography Using Classical Data
A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.
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Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings
Quantum kernels in QSVM deliver higher minority-class F1 scores than classical linear or RBF kernels on medical foundation model embeddings for binary insurance classification, avoiding classical collapse in noiseless simulation.
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Classical and Quantum Machine Learning for Population-Level Prediction of Heat-Related Physiological Events
Classical machine learning models outperform variational quantum models on population-level prediction of heat-related physiological events, while quantum models still exhibit non-trivial learning on the harmonized datasets.
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On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework
Quantum-inspired 1024-D document embeddings exhibit weak, unstable ranking performance and structural geometric limitations, performing better as auxiliary components in hybrid lexical-embedding retrieval systems.
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Spectral methods: crucial for machine learning, natural for quantum computers?
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
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IQPopt: Fast optimization of instantaneous quantum polynomial circuits in JAX
IQPopt is a JAX-based software tool enabling classical optimization of IQP circuits with thousands of qubits via efficient simulation of Pauli-Z expectation values, plus a module for quantum generative model training.
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Quantum in Biology, Quantum for Biology, and Biology for Quantum: Mapping the Evidence and the Road Ahead
This review structures current evidence for quantum-in-biology, quantum-for-biology, and biology-for-quantum, identifying mature cases like enzymatic tunneling and radical-pair magnetoreception while flagging unresolved topics and needed benchmarks.
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A Comprehensive Analysis of Accuracy and Robustness in Quantum Neural Networks
QCNN, QRNN, and QViT perform well on low-feature data but degrade on high-feature datasets, with QViT most robust to quantum noise and classical-style models better against adversarial noise.
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Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics
A correlation-guided hybrid quantum-classical model using QAOA achieves 84.6% accuracy on crime pattern classification with reduced trainable parameters compared to classical machine learning baselines.