Results 201 to 210 of about 40,499 (317)

Toward Solution‐Time Advantage With Error‐Mitigated Quantum Annealing for Combinatorial Optimization

open access: yesAdvanced Physics Research, EarlyView.
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]

open access: yesNat Commun
Alexeev Y   +27 more
europepmc   +1 more source

Contextuality of Quantum Error-Correcting Codes

open access: gold
Derek Khu   +3 more
openalex   +1 more source

Selective Sequestration of Toxic NOx Gases by P‐Doped Graphene: A Density Functional Theory Study

open access: yesAdvanced Physics Research, EarlyView.
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

Thermal Conductivity and Tunable Thermal Anisotropy of Magnetic CrSBr Monolayer

open access: yesAdvanced Physics Research, EarlyView.
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

A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation

open access: yesAdvanced Physics Research, EarlyView.
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]

open access: yesNat Commun
Zhang A   +34 more
europepmc   +1 more source

Home - About - Disclaimer - Privacy