A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.
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Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.
PAPUS is a pair-adaptive quantum classification method in Pauli space that reaches over 90% accuracy on 9 datasets with lower measurement and gate costs and only 1.67% accuracy drop under noise compared to 9.44% for baselines.
Angle encoding in hybrid quantum logistic regression yields the strongest performance among quantum variants, matching classical baselines in discrimination and achieving the lowest calibration error on pulsar candidate data.
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.
citing papers explorer
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Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.
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Divide et impera: hybrid multinomial classifiers from quantum binary models
Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.
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PAPUS: Pauli-Space-Based Multiclass Quantum Classification
PAPUS is a pair-adaptive quantum classification method in Pauli space that reaches over 90% accuracy on 9 datasets with lower measurement and gate costs and only 1.67% accuracy drop under noise compared to 9.44% for baselines.
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Hybrid Quantum-Classical Logistic Regression for Calibrated Classification of Pulsar Candidates
Angle encoding in hybrid quantum logistic regression yields the strongest performance among quantum variants, matching classical baselines in discrimination and achieving the lowest calibration error on pulsar candidate data.
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Compton Form Factor Extraction using Quantum Deep Neural Networks
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.