{"total":5,"items":[{"citing_arxiv_id":"2606.23102","ref_index":14,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Understanding Squeezed States of Light Through Wigner's Phase-Space","primary_cat":"quant-ph","submitted_at":"2026-06-22T09:44:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"The paper provides an expository review of squeezed states of light and related phenomena using the Wigner phase-space formalism and groups such as Lorentz and symplectic.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09689","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Low-Rank Acceleration of the Operator Fourier Transform","primary_cat":"math.NA","submitted_at":"2026-06-08T16:06:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Combines Operator Fourier Transform with low-rank Cross-DEIM to accelerate 2D Helmholtz equation solutions via pseudo-time Schrödinger integrals.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01763","ref_index":277,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Polaron Transport in TiO$_{2}$ from Machine Learning Molecular Dynamics","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-01T06:43:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DeepPolaron ML-MD simulations show rutile electrons form Ti-localized polarons hopping along [001] with 39 meV barrier and 4.4e-2 cm2/Vs mobility, while anatase holes form O-localized polarons hopping to second neighbors with 139 meV barrier and 1.4e-3 cm2/Vs mobility.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01016","ref_index":18,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantum Flow algorithm: quantum simulations of chemical systems using reduced quantum resources and constant depth quantum circuits","primary_cat":"physics.chem-ph","submitted_at":"2026-05-01T18:27:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"QFlow-SD matches canonical UCCSD energies for tested molecules while using substantially fewer qubits via reduced active spaces and constant-depth circuits, with a composite classical-quantum downfolding strategy demonstrated for water.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"entering the QFlow algorithm can be constructed system- atically over the full many-body Fock space. Specifically, the electronic Hamiltonian, the external cluster operator, the corresponding unitary transformations, the similarity- transformed Hamiltonian, and the active space effective 4 Hamiltonian are each represented as matrices: H→H,(17) Text →T ext,(18) eσext ≃e Text−T † ext →e Text−T† ext,(19) e−σext ≃e −(Text−T † ext) →e −(Text−T† ext),(20) ¯Hext → ¯Hext,(21) Heff →H eff.(22) The similarity-transformed Hamiltonian ¯Hext is constructed explicitly in matrix form by applying the external similar- ity transformation to the Hamiltonian matrix. For each active space Mi, an effective Hamiltonian Heff(i) is ob-"},{"citing_arxiv_id":"2506.14665","ref_index":108,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Accurate and scalable exchange-correlation with deep learning","primary_cat":"physics.chem-ph","submitted_at":"2025-06-17T15:56:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications, 13(1):2453, May 2022. ISSN 2041-1723. doi: 10.1038/s41467-022-29939-5. URL https://www.nature.com/articles/s41467-022-29939-5. Publisher: Nature Publishing Group. [108] F. Sestak, L. Schneckenreiter, J. Brandstetter, S. Hochreiter, A. Mayr, and G. Klambauer. VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification, Apr. 2024. URL http://arxiv.org/abs/2404.07194. arXiv:2404.07194 [cs]. [109] B. Alkin, A. Fürst, S. Schmid, L. Gruber, M. Holzleitner, and J. Brandstetter."}],"limit":50,"offset":0}