{"total":14,"items":[{"citing_arxiv_id":"2606.29245","ref_index":37,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"QmDFT for Polycyclic Aromatics: Balancing Embedding Ground-State Fidelity and Experimental Gap Estimation","primary_cat":"quant-ph","submitted_at":"2026-06-28T07:27:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An adaptive damping and DIIS protocol stabilizes QmDFT embedding with hybrid functionals on 10 PAHs, yielding LDA agreement with FCI for ground states and B3LYP agreement with experimental gaps while bypassing explicit excited-state computations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05387","ref_index":31,"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":"2606.05297","ref_index":56,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Continuous-variable ADAPT-VQE for bosonic lattice models","primary_cat":"quant-ph","submitted_at":"2026-06-03T18:00:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CV-ADAPT-VQE with tailored symmetry-preserving pools achieves significantly shallower circuits than Hamiltonian-based VQE for bosonic lattice models in GPU classical simulations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01291","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework","primary_cat":"quant-ph","submitted_at":"2026-05-31T15:23:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25552","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Beyond Logical Circuits: Hardware-Aware Analysis of Expressibility and Trainability in Variational Quantum Algorithms","primary_cat":"quant-ph","submitted_at":"2026-05-25T08:09:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Hardware transpilation of parameterized quantum circuits produces ansatz-dependent shifts in expressibility (up to 125%) and trainability (up to 25%), altering the expected trade-off between them.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21286","ref_index":63,"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":"2605.10667","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Simulation of Magnetic Materials: from Ab-Initio to NISQ","primary_cat":"quant-ph","submitted_at":"2026-05-11T14:50:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"NISQ quantum simulation of spin-wave spectra in 2D chromium tri-halide magnets achieves agreement with classical benchmarks at quasi-constant wall-time scaling.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Science Advances11(25), 9991 (2025) https://doi.org/ 10.1126/sciadv.adu9991 [12] Selisko, J., Amsler, M., Wever, C., Kawashima, Y., Samsonidze, G., Ul Haq, R., Tacchino, F., Taver- nelli, I., Eckl, T.: Dynamical mean field theory for real materials on a quantum computer. npj Computa- tional Materials11, 325 (2025) https://doi.org/10.1038/ s41524-025-01772-6 [13] Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J.M., Gambetta, J.M.: Hardware- efficient variational quantum eigensolver for small molecules and quantum magnets. Nature549(7671), 242-246 (2017) https://doi.org/10.1038/nature23879 [14] Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J.C., Barends, R., Bengtsson, A., Boixo, S."},{"citing_arxiv_id":"2605.08251","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"The finite-shot help-harm boundary of zero-noise extrapolation","primary_cat":"quant-ph","submitted_at":"2026-05-07T17:01:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"tional hybrid quantum-classical algorithms.New Journal of Physics18, 023023 (2016). https://doi.org/10.1088/1367-2630/18/2/023023 [32] McClean, J.R., Boixo, S., Smelyanskiy, V.N., Babbush, R., Neven, H.: Barren plateaus in quantum neural network training landscapes.Nature Communications9, 4812 (2018). https://doi.org/10.1038/s41467-018-07090-4 21 [33] Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J.M., Gambetta, J.M.: Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets.Nature549, 242-246 (2017). https://doi.org/10.1038/nature23879 [34] Greenberger, D.M., Horne, M.A., Zeilinger, A.: Going beyond Bell's theorem. In: Kafatos, M. (ed.)Bell's Theorem, Quantum Theory and Conceptions of the Universe, pp."},{"citing_arxiv_id":"2605.03434","ref_index":41,"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":"2604.04414","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks","primary_cat":"cs.ET","submitted_at":"2026-04-06T04:43:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.03346","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks","primary_cat":"quant-ph","submitted_at":"2026-04-03T10:24:14+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"By varying all circuit parameters, these hypothesis functions collectively form the hypothesis spaceof the model. Other QPINN models can also employ hypothesis functions based on Chebyshev polynomials [23] or Lagrange polynomials [12]. Moreover, QPINN with QFM [26, 27] and other VQAs designed to solve PDEs [12, 23] often employ a hardware efficient ansatz (HEA) [33] as the training block. In practice, this introduces hidden constraints and makes it diffi- cult to precisely characterize the hypothesis space from a theoretical perspective. Such limitations highlight a broader issue in VQA: most quantum models offer little theoretical guarantees that their hypothesis space contains an appropriate approximation of the target function [34]."},{"citing_arxiv_id":"2511.13677","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Open-shell frozen natural orbital approach for quantum eigensolvers","primary_cat":"physics.chem-ph","submitted_at":"2025-11-17T18:32:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ZAPT2 frozen natural orbitals reduce virtual space for systematic convergence of open-shell T1-S0 gaps in CASCI and iQCC quantum eigensolvers, demonstrated on H2O2, O2, CH2 and Ir(ppy)3.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.03237","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Improved Strategies for Fermionic Quantum Simulation with Global Interactions","primary_cat":"quant-ph","submitted_at":"2025-04-04T07:37:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Quantum circuits for single and double fermionic excitations on ion traps reduce MS gate counts by factors of 2 and 4 respectively by using global interactions for optimal parallelism.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.05030","ref_index":85,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Many-Body Physics and Quantum Simulations with Strongly Interacting Photons","primary_cat":"quant-ph","submitted_at":"2019-07-11T07:34:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":1.0,"formal_verification":"none","one_line_summary":"Review of proposals and experiments using coupled cavity arrays and superconducting circuits to realize many-body physics with photons, including Mott transitions, fractional quantum Hall states, and dissipative phase transitions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"0 Φ2m, where Q = C ∂Ltransmon ∂ ˙Φ is a conjugate momentum. As before, we promote Q and Φ to operators as ˆΦ = √ LJ ω 2 (ˆa† + ˆa), ˆQ = i √ Cω 2 (ˆa†− ˆa),(20) MANY-BODY PHYSICS AND QUANTUM SIMULATIONS WITH STRONGLY INTERACTING PHOTONS31 Fig. 15. - Arrays of coupled transmon qubits fabricated by (a) Google withL = 9 [178, 70] , (b) IBM with L = 5 [85], (c) Regetti with L = 19 [86]. A 72-site superconducting chip implementing the JCH model to study dissipative phase transition [80] is shown in (d). where [ ˆa, ˆa†] = i. We then apply normal ordering of the operators ˆa and ˆa† inHtransmon using the formula [168] (a + a†)2m = m∑ k=0 2m−2k∑ i=0 (2m)!(a†)ia2m−2k−i 2kk!i!(2m− 2k− i)! . In the limit EJ /EC≈ 50− 100, the higher order terms inHtransmon can be truncated up"}],"limit":50,"offset":0}