Quantum Simulation
2,540 papers for year 2026
Quantum Simulation Research Context
This category collects papers on quantum simulation methods for chemistry, materials, many-body systems, condensed matter, and physics models.
Showing 109-120 of 2,540
Ab initio mixed polarizabilities responsible for the Raman-forbidden ν2 transition in carbon dioxide molecule.
Finenko AA, Chistikov DN, Kouzov AP
Ab initio potential energy surface of NC(4)N-He: rotationally inelastic collisions and rate coefficients.
Kaur G, Chahal P, Dhilip Kumar TJ
Absence of Far-Detuned Attractive Optical Traps for Alkali Rydberg Atoms
Gabriel E. Patenotte, Youngshin Kim, Samuel Gebretsadkan, Kang-Kuen Ni
Absence of thermalization after a local quench and strong violation of the eigenstate thermalization hypothesis
Peter Reimann, Christian Eidecker-Dunkel
Absorbing Many-Body Correlations into Core-Optimized Orbitals
Hao Zhang, Matthew Otten
Accelerated SPAD-Based Diffuse Optical Tomography With Data-Driven View Optimization.
Li L, Kuang K, Lin Y, Zhang J, Chen B, Jiang J, Jiang J, Bruschini C, Charbon E, Ren W
Accelerating Quantum Tensor Network Simulations with Unified Path Variations and Non-Degenerate Batched Sampling
Taylor Lee Patti, Paavai Pari, Yang Gao, Azzam Haidar, Thien Nguyen, Tom Lubowe, Daniel Lowell, Brucek Khailany
Accelerating State-Vector Quantum Simulation on Integrated GPUs via Cache Locality Optimization: A Cross-Architecture Evaluation
Gabriel Fernandes Thomaz, Jerusa Marchi, Eduarda Rodrigues Monteiro, Fernando Augusto Caletti de Barros, Evandro Chagas Ribeiro da Rosa
Accurate Core-Level Ionization Energies from an Affordable Second-Order Approach.
Mester D, Kállay M
Accurate ground state energy estimation with noise and imperfect state preparation
Alicja Dutkiewicz, Thomas E. O'Brien, Stefano Polla
Accurate helium-benzene potential: From CCSD(T) to Gaussian process regression.
Akram S, Paul S, Kovacs C, Maroulas V, Del Maestro A, Vogiatzis KD
Accurate Hydration Free Energy Calculations for Diverse Organic Molecules With a Machine Learning Force Field.
Xie X, Weber JL, Svensson M, Johnston RC, Harder ED, Jacobson LD