{"total":16,"items":[{"citing_arxiv_id":"2606.29293","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Private training in quantum machine learning","primary_cat":"quant-ph","submitted_at":"2026-06-28T09:28:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Hybrid QML models trained with classical DP-SGD retain higher accuracy than classical models under fixed privacy budgets on synthetic and image-classification tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28169","ref_index":77,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Time Evolution on Hybrid Tensor Networks -- A Novel and Parallelizable Algorithm","primary_cat":"quant-ph","submitted_at":"2026-06-26T15:02:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces a parallelizable hybrid tensor network algorithm for time-evolving matrix product states that combines classical BUG integration with quantum methods without synchronization barriers.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.26873","ref_index":16,"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":"2606.18914","ref_index":16,"ref_count":3,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Benchmark of Pauli Correlation Encoding for different optimisation problems","primary_cat":"quant-ph","submitted_at":"2026-06-17T10:40:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Pauli Correlation Encoding framework achieves competitive or superior solutions on QOPTLib benchmark instances for combinatorial optimization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23719","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Hybrid Quantum-Classical Approach for Melt Pool Prediction in Laser Powder Bed Fusion","primary_cat":"quant-ph","submitted_at":"2026-06-17T03:11:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Hybrid quantum-classical model with quantum feature encoding and clustering outperforms classical neural networks for LPBF melt pool prediction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.13244","ref_index":56,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection","primary_cat":"quant-ph","submitted_at":"2026-06-11T11:56:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09988","ref_index":7,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Absence of poor local minima in matrix product states","primary_cat":"quant-ph","submitted_at":"2026-06-08T18:00:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"MPS energy landscapes lack poor local minima because gauge freedom induces overparametrization that concentrates local minima near the global minimum, with the local minimum distribution proven invariant under orthogonality center moves.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09964","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks","primary_cat":"quant-ph","submitted_at":"2026-06-08T15:40:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"JGRA framework extracts geometric descriptors from noise-conditioned Jacobians in QNNs after entropy-matched calibration and noise-aware training, and empirically shows these descriptors predict robustness under unseen noise.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00368","ref_index":27,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Signal Processing for Linear PDEs: Circuit Design and Experimental Validation","primary_cat":"quant-ph","submitted_at":"2026-05-29T21:21:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"End-to-end QSP-based quantum circuits solve linear PDEs on IBM hardware with tunable error and handle non-homogeneous Dirichlet boundaries for a plasma Poisson problem.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31248","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Trainable Quantum Spectral Models for Partial Differential Equations","primary_cat":"quant-ph","submitted_at":"2026-05-29T12:47:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Trainable quantum spectral models with an intermediate parameterized mixer (ε ≈ 0.5) outperform standard variational quantum circuits for PDEs by learning in spectral representation, with HHL-inspired architectures showing fastest convergence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21286","ref_index":11,"ref_count":2,"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":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Introduces a JAX-based framework for pulse-level QML with composable ansatze, end-to-end pulse optimization, and Fourier-analytic diagnostics.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07866","ref_index":48,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hybrid Quantum-Classical Logistic Regression for Calibrated Classification of Pulsar Candidates","primary_cat":"quant-ph","submitted_at":"2026-05-08T15:25:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25631","ref_index":17,"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":"background","top_context_polarity":"background","context_text":"need to rethink variational quantum computing, 2024. URL:https://arxiv.org/abs/2312.09121, arXiv:2312.09121. [15] Jens Eisert and John Preskill. Mind the gaps: The fraught road to quantum advantage, 2025. URL: https://arxiv.org/abs/2510.19928,arXiv:2510.19928. [16] John Preskill. Beyond nisq: The megaquop machine, 2025. URL:https://arxiv.org/abs/2502. 17368,arXiv:2502.17368,doi:10.1145/3723153. [17] Zolt' an Zimbor' as, B' alint Koczor, Zo¨ e Holmes, Elsi-Mari Borrelli, Andr' as Gily' en, Hsin-Yuan Huang, Zhenyu Cai, Antonio Ac' ın, Leandro Aolita, Leonardo Banchi, Fernando G. S. L. Brand˜ ao, Daniel Cavalcanti, Toby Cubitt, Sergey N. Filippov, Guillermo Garc' ıa-P' erez, John Goold, Orsolya K' alm' an, Elica Kyoseva, Matteo A. C. Rossi, Boris Sokolov, Ivano Tavernelli, and Sabrina Maniscalco."},{"citing_arxiv_id":"2604.19076","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning","primary_cat":"quant-ph","submitted_at":"2026-04-21T04:37:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Since the kernel is determined by the choice of encoding cir- cuit, different circuits lead to different quantum kernels and thus different similarity structures over the data. Unlike variational approaches such as quantum neural net- works [9-11] and the variational quantum eigensolver [12], which require iterative optimization of circuit parameters and can suffer from barren plateaus [13, 14], the encoding circuit in QKMs remains fixed during training, and the learning task is car- ried out by a classical algorithm using the resulting kernel matrix [15]. This design eliminates exposure to barren plateaus, yields a convex training objective with a guar- anteed global optimum, and ensures that the optimal model is always recoverable via"},{"citing_arxiv_id":"2604.13735","ref_index":14,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Reachability Constraints in Variational Quantum Circuits: Optimization within Polynomial Group Module","primary_cat":"quant-ph","submitted_at":"2026-04-15T11:21:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.07195","ref_index":28,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Accelerating Inference for Multilayer Neural Networks with Quantum Computers","primary_cat":"quant-ph","submitted_at":"2025-10-08T16:26:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"algorithms (VQA) [19, 20] train parametrized quantum circuits (PQC) [21] in an analogue to multi- layer neural networks. However, these algorithms face trainability issues in the form of poor local minima [22, 23] and vanishing gradients, orbarren plateaus[ 24, 25]. Moreover, techniques mitigating these issues often result in the algorithms being classically simulable [ 26, 27]. While alternate approaches such as quantum kernel methods [28, 29] and others have been proposed [30, 31], they often face similar trainability issues [32, 33]. ∗arthur.rattew.science@gmail.com 1 arXiv:2510.07195v1 [quant-ph] 8 Oct 2025 Erf Normalize Flatten Square Linear L2 Pool Normalize Square \"Cat\" 2D Conv Normalize Erf Normalize Flatten Square Linear L2 Pool Normalize Square \"Cat\" 2D Conv NormalizeNeural Net"}],"limit":50,"offset":0}