Results 201 to 210 of about 40,499 (317)
Toward Solution‐Time Advantage With Error‐Mitigated Quantum Annealing for Combinatorial Optimization
This paper presents a novel error mitigation technique to address the qubit errors that occur when solving combinatorial optimization problems with quantum annealing. The approach significantly speeds up the computation to reach the global optimum solution for a correlated 3D image segmentation model for material microstructures, demonstrating a ...
Yushuang Sam Yang +3 more
wiley +1 more source
Artificial intelligence for quantum computing. [PDF]
Alexeev Y +27 more
europepmc +1 more source
Selective Sequestration of Toxic NOx Gases by P‐Doped Graphene: A Density Functional Theory Study
P‐doped graphene (P‐grap) is explored as an NOx sensor through DFT simulations. The analysis of its geometry, binding energies, electronic properties, and atom‐in‐molecule characteristics demonstrates that P‐grap is a selective sensor for NOx among a mixture of various gases.
Anwar Ali +3 more
wiley +1 more source
Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes [PDF]
Ye-Hua Liu, David Poulin
openalex +1 more source
Thermal Conductivity and Tunable Thermal Anisotropy of Magnetic CrSBr Monolayer
Top (left) and side view of single‐layer CrSBr (right). Phonon transport is strongly anisotropic, with a lattice thermal conductivity along the a‐lattice vector which is almost twice the one along the b‐vector (κxx$\kappa _{xx}$ = 1.8 κyy$\kappa _{yy}$). ABSTRACT We present first‐principles calculations of the thermal conductivity, κ${\bm \kappa }$, of
Marta Loletti +4 more
wiley +1 more source
Approximate quantum error correcting codes from conformal field theory [PDF]
Shengqi Sang +2 more
openalex +1 more source
Universal Sets of Quantum Gates for Detected Jump-Error Correcting Quantum Codes [PDF]
G. Alber, M. Mussinger, A. Delgado
openalex +1 more source
A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation
A hybrid semi‐inverse variational and machine‐learning framework is presented for solving the Schrödinger equation with complex quantum potentials. Physics‐based variational solutions generate high‐quality training data, enabling Random Forest and Neural Network models to deliver near‐perfect energy predictions.
Khalid Reggab +5 more
wiley +1 more source
Demonstrating quantum error mitigation on logical qubits. [PDF]
Zhang A +34 more
europepmc +1 more source

