Results 31 to 40 of about 5,709 (252)

Channel Decoding with Quantum Approximate Optimization Algorithm [PDF]

2019 IEEE International Symposium on Information Theory (ISIT), 2019
Motivated by the recent advancement of quantum processors, we investigate quantum approximate optimization algorithm (QAOA) to employ quasi-maximum-likelihood (ML) decoding of classical channel codes. QAOA is a hybrid quantum-classical variational algorithm, which is advantageous for the near-term noisy intermediate-scale quantum (NISQ) devices, where ...
Toshiki Matsumine, Toshiaki Koike-Akino, Ye Wang 0001   +2 more
openaire   +2 more sources

Quantum approximate optimization algorithm applied to the binary perceptron

Physical Review B, 2023
14 pages, 9 ...
Torta, P, Mbeng, GB, Baldassi, C, Zecchina, R, Santoro, GE   +4 more
openaire   +4 more sources

Quantum approximate optimization algorithm in non-Markovian quantum systems

Physica Scripta, 2023
Abstract Although quantum approximate optimization algorithm (QAOA) has demonstrated its quantum supremacy, its performance on Noisy Intermediate-Scale Quantum (NISQ) devices would be influenced by complicated noises, e.g. quantum colored noises.
Bo Yue, Shibei Xue, Yu Pan, Min Jiang
openaire   +2 more sources

Quantum dropout: On and over the hardness of quantum approximate optimization algorithm

Physical Review Research, 2023
تصبح مشكلة التحسين التوافقي صعبة للغاية في المواقف التي يكون فيها مشهد الطاقة وعرًا، ويقع الحد الأدنى العالمي في منطقة ضيقة من مساحة التكوين. عند استخدام خوارزمية التحسين التقريبي الكمومي (QAOA) لمعالجة هذه الحالات الأصعب، نجد أن الصعوبة تنشأ بشكل أساسي من الدائرة الكمومية QAOA بدلاً من دالة التكلفة. للتخفيف من حدة المشكلة، نتخلى بشكل انتقائي عن البنود
Zhenduo Wang, Pei-Lin Zheng, Biao Wu, Yi Zhang   +3 more
openaire   +3 more sources

Designing a New Continuous Quantum Evolutionary Algorithm for Nonlinear Optimization and Efficiency Frontier Evaluation [PDF]

Control and Optimization in Applied Mathematics
In this paper, we introduce a new continuous quantum evolutionary optimization algorithm designed for optimizing nonlinear convex functions, non-convex functions, and efficiency evaluation problems using quantum computing principles.
Tahereh Azizpour, Majid Yarahmadi
doaj   +1 more source

Density-Matrix Renormalization Group Algorithm for Simulating Quantum Circuits with a Finite Fidelity

PRX Quantum, 2023
We develop a density-matrix renormalization group (DMRG) algorithm for the simulation of quantum circuits. This algorithm can be seen as the extension of the time-dependent DMRG from the usual situation of Hermitian Hamiltonian matrices to quantum ...
Thomas Ayral, Thibaud Louvet, Yiqing Zhou, Cyprien Lambert, E. Miles Stoudenmire, Xavier Waintal   +5 more
doaj   +1 more source

Sampling frequency thresholds for the quantum advantage of the quantum approximate optimization algorithm

npj Quantum Information, 2022
Abstract We compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers Gurobi and MQLib to solve the MaxCut problem on 3-regular graphs. We identify the minimum noiseless sampling frequency and depth p required ...
Danylo Lykov, Jonathan Wurtz, Cody Poole, Mark Saffman, Tom Noel, Yuri Alexeev   +5 more
openaire   +3 more sources

Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer

Scientific Reports, 2022
Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage.
Pradeep Niroula, Ruslan Shaydulin, Romina Yalovetzky, Pierre Minssen, Dylan Herman, Shaohan Hu, Marco Pistoia   +6 more
doaj   +1 more source

Refinement of amino‐acid conformation vs. difference density maps in time‐resolved serial femtosecond crystallography data analysis

FEBS Open Bio, EarlyView.
The dFoCC pipeline starts with observed DED and resting‐state coordinates, which are then used to generate a library of triggered states. Correlation analysis of the calculated DED features of each candidate vs observed DED permits quantitative evaluation of candidate structural quality.
Meng Iao Fong, Yuhei Hosokawa, Lars Oliver Essen, Manuel Maestre‐Reyna   +3 more
wiley   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source