A variational autoencoder learns quantum embeddings compressing ImageNet into 13 qubits and achieving 98.5% accuracy on MNIST 3-vs-5 classification with a quantum circuit, close to classical baselines and far above naive amplitude embeddings.
hub Mixed citations
Data re- uploading for a universal quantum classifier
Mixed citation behavior. Most common role is background (67%).
20
Pith papers citing it
540
external citations · Crossref
Background
67% of classified citations
hub tools
citation-role summary
background 4
method 2
citation-polarity summary
representative citing papers
A 10-qubit convolutional quantum graph neural network fed by autoencoder-compressed jet data achieves performance comparable to classical graph networks in distinguishing boosted Z jets from gluon jets.
Pulse-level parameterization of quantum Fourier models replaces single gate angles with multiple independent sub-angles, relaxing monomial couplings and improving gradient descent performance on Fourier series tasks.
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
Hybrid agent with variational quantum circuits for feature extraction in hierarchical RL outperforms classical baselines with 66% parameter savings, but quantum value estimation degrades results.
QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
Hybrid QFL cuts quantum transmissions from 3TNMP to {3t + 2(T-t)}NMP over T rounds while preserving near-centralized convergence and improving depolarizing-noise resilience via decentralized aggregation and Steane-code QEC.
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
Quantum algorithms for element-wise polynomial matrix transforms achieve exponential space reduction in polynomial degree with corrections to prior constructions.
Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.
QPINN combines quantum feature mapping via Nyström method with physics-informed constraints to achieve 99.46% average SOH estimation accuracy on a 310k-sample multi-chemistry battery dataset, outperforming baselines by up to 65% in MAPE.
IQFMs iteratively constructs deep quantum feature maps from shallow circuits via classical augmentation weights and contrastive layer-wise training, outperforming QCNNs on noisy quantum data and matching classical neural networks on image classification without variational parameter optimization.
Quantum advantage in hadronic tomography should be evaluated selectively for CFFs, GPDs, TMDs, and GTMDs because their light-front and real-time correlation functions create ill-posed inverse problems that quantum algorithms may address at algorithmic, computational, and inference levels.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
citing papers explorer
-
Local tensor-train surrogates for quantum learning models
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
-
Quantum Injection Pathways for Implicit Graph Neural Networks
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.