{"total":20,"items":[{"citing_arxiv_id":"2606.26873","ref_index":44,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy","primary_cat":"quant-ph","submitted_at":"2026-06-25T10:58:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.26312","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Tailor Made Embeddings for Quantum Machine Learning","primary_cat":"quant-ph","submitted_at":"2026-06-24T18:53:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10448","ref_index":40,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations","primary_cat":"cs.LG","submitted_at":"2026-06-09T05:55:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06456","ref_index":32,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum element-wise transforms","primary_cat":"quant-ph","submitted_at":"2026-06-04T17:50:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Quantum algorithms for element-wise polynomial matrix transforms achieve exponential space reduction in polynomial degree with corrections to prior constructions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05387","ref_index":44,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines","primary_cat":"quant-ph","submitted_at":"2026-06-03T19:46:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21286","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Software Between Quantum and Machine Learning -- And Down to Pulses","primary_cat":"quant-ph","submitted_at":"2026-05-20T15:20:07+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19233","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets","primary_cat":"cs.CR","submitted_at":"2026-05-19T01:05:16+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18416","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantum enhanced identification of boosted jets with quantum graph neural networks","primary_cat":"hep-ph","submitted_at":"2026-05-18T13:52:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16044","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model","primary_cat":"quant-ph","submitted_at":"2026-05-15T15:16:55+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09226","ref_index":43,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Injection Pathways for Implicit Graph Neural Networks","primary_cat":"quant-ph","submitted_at":"2026-05-09T23:51:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"§III-D makes this intuition precise, bound- ingLip F (Φ• Θ)for each pathway and ordering the resulting contraction budgets. D. Well-posedness and Lipschitz Contraction Analysis We now establish sufficient conditions under which each pathway's equilibrium operator is a contraction-and therefore admits a unique fixed point by Banach's fixed-point theorem [43]. The three results (Theorems III.1-III.3) follow the tem- plate of the IGNN analysis in Ref. [8], applied to operators that carry an additional quantum term. Throughout the analysis, matrix-valued state perturbations are measured in the Frobenius norm∥ · ∥ F , and fixed matrices acting by left or right multiplication are bounded in the induced spectral norm∥ · ∥ 2."},{"citing_arxiv_id":"2605.04945","ref_index":29,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Beyond Gates: Pulse Level Quantum Fourier Models","primary_cat":"quant-ph","submitted_at":"2026-05-06T14:13:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Index Terms-Quantum Computing, Quantum Machine Learning, Quantum Fourier Models, Pulse Level Quantum Computing I. INTRODUCTION In the field of quantum machine learning (QML), parameterised quantum circuits (PQCs) are generally used when training models conceptually similar to classical neural networks [27, 4]. Among these, the class of data-reuploading models [29], also known as quantum Fourier models (QFMs), are especially interesting as they impose a mathematically well defined structure on the PQC [35], allowing us to study trainability and dequantisability [19, 41]. The trainability of PQCs is generally bottlenecked by two distinct phenomena: Barren Plateaus (BPs) [32] (vanishing gradients globally) and sub-"},{"citing_arxiv_id":"2605.03434","ref_index":39,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits","primary_cat":"cs.LG","submitted_at":"2026-05-05T07:14:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25631","ref_index":31,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Local tensor-train surrogates for quantum learning models","primary_cat":"quant-ph","submitted_at":"2026-04-28T13:33:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"phase of these expensive quantum models via constructing an equivalent classical counterpart of these trained quantum learning models. A particular type of PQCs named reuploading quantum models [27, 28] wherein alternating layers of data-encoding gates and trainable unitary gates are used have been known to be exactly represented as truncated Fourier series [29, 30]. This property was used by Schreiber et al [31] to design an exact classical Fourier surrogates for this particular class of QML models. Hernich et al. [32] improved upon this work in terms of sample complexity by using random Fourier features instead of exact grid reconstruction. Jerbi et al [33] under a similar spirit introduced shadow models applicable to models beyond reuploading quantum models."},{"citing_arxiv_id":"2604.20706","ref_index":46,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"QuanForge: A Mutation Testing Framework for Quantum Neural Networks","primary_cat":"cs.SE","submitted_at":"2026-04-22T15:47:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"QCNN is hierarchical and employs amplitude encod- ing. Hierarchical Circuit Quantum Classifier (HCQC)[ 24] is another hierarchical model that reduces the circuit's degrees of freedom as depth increases, using translationally stacked, invariant ansatz blocks. Here we use the ansatz U_SO4 with amplitude encoding. Data Re-uploading Neural Network (DRNN)[ 46] re-uploads data features as rotation angles to multiple qubits, combined with extra trainable parts to form a universal classifier. It addresses the limited expressivity of a single qubit with fewer quantum computational resources. DRNN adopts a block-stacking structure and angle encoding. Considering the time overhead, we adjust the image size of the DRNN to8 × 8and configure it"},{"citing_arxiv_id":"2604.19832","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach","primary_cat":"quant-ph","submitted_at":"2026-04-20T23:03:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10362","ref_index":17,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Battery health prognosis using Physics-informed neural network with Quantum Feature mapping","primary_cat":"cs.LG","submitted_at":"2026-04-11T22:12:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10025","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs","primary_cat":"hep-ph","submitted_at":"2026-04-11T04:44:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"quantum devices. Here the appeal is not only speed. It is that the target quantity is Minkowskian from the out- set. Real-time hadron dynamics and scattering provide the strongest current hardware illustrations. Farrellet al. prepared and propagated hadron wave packets in the Schwinger model using 112 qubits on IBM's Heron archi- 6 tecture [34]. Refs. [35, 36] demonstrated efficient prepa- rationofinteractingmesonicwavepacketsontrapped-ion hardware and then used related constructions for digital quantum computation of hadron scattering in a lattice gauge theory. The first observation of scattering in a lat- tice gauge theory on IBM hardware has been reported in [37], including tunable post-collision dynamics in[1 + 1]-"},{"citing_arxiv_id":"2603.03853","ref_index":17,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Practical Quantum Federated Learning for Privacy-Sensitive Healthcare: Communication Efficiency and Noise Resilience","primary_cat":"quant-ph","submitted_at":"2026-03-04T09:04:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.14099","ref_index":79,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics","primary_cat":"quant-ph","submitted_at":"2025-10-15T21:15:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"1063/5.0095270 . arXiv:2107.10711 [physics]. Accessed 2025-08-29 [78] Goto, T., Tran, Q.H., Nakajima, K.: Universal Approximation Property of Quan- tum Machine Learning Models in Quantum-Enhanced Feature Spaces. Physical Review Letters127(9), 090506 (2021) https://doi.org/10.1103/PhysRevLett. 127.090506 . arXiv:2009.00298 [quant-ph]. Accessed 2025-08-30 [79] P' erez-Salinas, A., Cervera-Lierta, A., Gil-Fuster, E., Latorre, J.I.: Data re- uploading for a universal quantum classifier. Quantum4, 226 (2020) https: //doi.org/10.22331/q-2020-02-06-226 . arXiv:1907.02085 [quant-ph]. Accessed 2025-08-30 [80] Ranga, D., Rana, A., Prajapat, S., Kumar, P., Kumar, K., Vasilakos, A.V.: Quantum Machine Learning: Exploring the Role of Data Encoding Techniques,"},{"citing_arxiv_id":"2506.19461","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Iterative Quantum Feature Maps","primary_cat":"quant-ph","submitted_at":"2025-06-24T09:40:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}