{"total":25,"items":[{"citing_arxiv_id":"2605.23377","ref_index":15,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"SAFE ma-QAOA: Surrogate-Assisted and Fine-Tuning Enhanced Multi-Angle QAOA with Parameter Distillation","primary_cat":"quant-ph","submitted_at":"2026-05-22T08:43:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SAFE ma-QAOA achieves 64.3% fewer active parameters and 94.5% lower estimated QPU workload via surrogate pre-training and parameter distillation on Sherrington-Kirkpatrick, 2D spin glass, and Max-Cut instances.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22029","ref_index":32,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Sensitivity Evaluation of SU(1,1) Interferometers with Arbitrary Input Probe State and Homodyne 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GYNIa Luganoa Subsets obtained via abstractinformation-theoretic approaches All q. causal structures (cyclic) PM QC-QC GYNIc Luganoc Cor. 3 (from Thm. 2+[42]) Ex. 7 Ex. 6 Ex. 1 Figure 1:Visual overview of main result and examplesOn both sides, the largest set labelled as"},{"citing_arxiv_id":"2604.23317","ref_index":5,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Constrained Quantum Optimization meets Model Reduction","primary_cat":"quant-ph","submitted_at":"2026-04-25T14:11:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A projection-based model reduction enables exponential state-space reduction for constrained quantum optimization applied to random 3-SAT and agent coordination on 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In:Quantum Information Processing24.5 (May 2025).doi: 10.1007/s11128-025-04743-4. [14] Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson, et al. \"Parameter Transfer for Quantum Ap- proximate Optimization of Weighted MaxCut\". In:ACM Transactions on Quantum Computing4.3 (Sept. 2023).doi:10.1145/3584706. [15] Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, et al. \"Parameter Setting in Quantum Approximate Optimization of Weighted Problems\". In:Quantum8 (Jan. 2024).doi:10.22331/q- 2024-01-18-1231. [16] Vladimir Kremenetski, Anuj Apte, Tad Hogg, et al.Quantum Alternating Operator Ansatz (QAOA) beyond low depth with gradually changing unitaries. arXiv:2305."},{"citing_arxiv_id":"2602.12193","ref_index":20,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"A Framework for Spatial Quantum Sensing","primary_cat":"quant-ph","submitted_at":"2026-02-12T17:24:51+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A new framework for spatial quantum sensing constructs non-local estimators for field properties using quantum sensor networks, with algebraic geometry for exact placements, entanglement for maximal precision, and error-free subspaces to cut sensor requirements.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.01736","ref_index":40,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Cobble: Compiling Block Encodings for Quantum Computational Linear Algebra","primary_cat":"cs.PL","submitted_at":"2025-11-03T16:48:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Cobble is a domain-specific language for quantum block encodings that compiles high-level matrix expressions to optimized circuits using analyses and quantum singular value transformation, achieving 2.6x-25.4x speedups over unoptimized baselines on benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.08153","ref_index":20,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony","primary_cat":"quant-ph","submitted_at":"2025-10-09T12:35:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.02725","ref_index":27,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Congestion bounds via Laplacian eigenvalues and their application to tensor networks with arbitrary geometry","primary_cat":"cs.DS","submitted_at":"2025-10-03T04:58:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Spectral bounds relate graph Laplacian eigenvalues to the congestion of binary-tree embeddings, with an efficient spectral-ordering algorithm and applications to tensor-network contraction complexity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.14075","ref_index":40,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Comparing a Few Qubit Systems for Superconducting Hardware Compatibility and Circuit Design Sensitivity in Qiskit","primary_cat":"quant-ph","submitted_at":"2025-06-17T00:18:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"Compares QFT, GHZ, and W circuits on 4-10 qubits between simulator and IBM hardware to assess noise impact and hardware compatibility in the NISQ era.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.09794","ref_index":76,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Wasserstein Distances on Quantum Structures: an Overview","primary_cat":"quant-ph","submitted_at":"2025-06-11T14:39:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A literature review synthesizing developments in quantum Wasserstein distances, their applications, and unresolved questions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"qj |ψj⟩ ⟨ψj|    . (60) They show that this measure T satisfies the four postulates [9] of a measure of quantum coherence, and also gave it an operational meaning in terms of the time needed to transform a pure state into an incoherent state under unitary evolution. Coherence is a key ingredient in fields such as quantum algorithms [45], solid-state physics [76, Section VIIB], and nanoscale thermodynamics [21]. 5.5 Transport plans method 5.5.1 Definition A more recent work by the author of this review gives a generalisation from the coupling point of view which focuses more on flexibility in definition, avoiding the use of cost matrices altogether. Instead, [10] starts from any distance d on the projective Hilbert space PH = (H \\ {0})/C, and defines a cost based on the distance itself."},{"citing_arxiv_id":"2506.06790","ref_index":4,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Benchmarking Swarm Optimization Algorithms for Parameter Initialization in the Quantum Approximate Optimization Algorithm","primary_cat":"quant-ph","submitted_at":"2025-06-07T13:14:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Swarm methods such as PSO, FIPSO, and QPSO yield lower approximation gaps and more stable convergence than Adam, COBYLA, or SPSA when tuning QAOA parameters on weighted MaxCut instances, especially under noise and limited shots.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.21172","ref_index":9,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Iceberg Beyond the Tip: Co-Compilation of a Quantum Error Detection Code and a Quantum Algorithm","primary_cat":"quant-ph","submitted_at":"2025-04-29T20:47:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.17548","ref_index":20,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Quantum Autoencoder for Multivariate Time Series Anomaly Detection","primary_cat":"quant-ph","submitted_at":"2025-04-24T13:40:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A quantum autoencoder for multivariate time series anomaly detection achieves competitive performance with neural-network autoencoders using fewer trainable parameters.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2410.06429","ref_index":10,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"A QUBO Formulation for the Generalized LinkedIn Queens and Takuzu/Tango Game","primary_cat":"quant-ph","submitted_at":"2024-10-08T23:54:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"QUBO formulations are derived for generalized LinkedIn Queens, Takuzu/Tango, Tents & Trees, and two new chess-piece problems to enable solution on quantum hardware.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2409.00433","ref_index":33,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"High-Precision Multi-Qubit Clifford+T Synthesis by Unitary Diagonalization","primary_cat":"quant-ph","submitted_at":"2024-08-31T12:10:32+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2406.17995","ref_index":40,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Managing Classical Processing Requirements for Quantum Error Correction","primary_cat":"quant-ph","submitted_at":"2024-06-26T00:50:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A two-level decoder scheduling framework reduces classical processing requirements for quantum error correction by 10-40% on fault-tolerant benchmarks by managing bursty workloads as shared resources.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2312.03508","ref_index":11,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Convolutional neural network based decoders for surface codes","primary_cat":"quant-ph","submitted_at":"2023-12-06T14:07:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Convolutional neural network decoders achieve good performance on surface code error correction and adapt across noise models, with explainable AI used to inspect their decisions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}