Results 101 to 110 of about 745,886 (317)
Advanced Functional Materials, EarlyView.Heterojunctions combining halide perovskites with low‐dimensional materials enhance optoelectronic devices by enabling precise charge control and improving efficiency, stability, and speed. These synergies advance flexible electronics, wearable sensors, and neuromorphic computing, mimicking biological vision for real‐time image analysis and intelligent
Yu‐Jin Du +11 more
wiley +1 more source
Practical Quantum Circuit Implementation for Simulating Coupled Classical Oscillators
IEEE AccessSimulating large-scale coupled-oscillator systems presents substantial computational challenges for classical algorithms, particularly when pursuing first-principles analyses in the thermodynamic limit.
Natt Luangsirapornchai +6 more
doaj +1 more source
Low Rank Approximation in Simulations of Quantum Algorithms [PDF]
arXiv, 2021 Simulating quantum algorithms on classical computers is challenging when the system size, i.e., the number of qubits used in the quantum algorithm, is moderately large. However, some quantum algorithms and the corresponding quantum circuits can be simulated efficiently on a classical computer if the input quantum state is a low-rank tensor and all ...
arxiv
Advanced Functional Materials, EarlyView.An environmentally friendly transfer printing method of nm‐thick giant magnetoresistive (GMR) sensors is demonstrated. This method, relying on water and biocompatible polyvinyl alcohol (PVA) polymer without the need of complex treatments, allows transferring thin films to a wide range of biological, organic, and inorganic substrates.
Olha Bezsmertna +7 more
wiley +1 more source
Finding Key Nodes in Complex Networks Through Quantum Deep Reinforcement Learning
EntropyIdentifying key nodes in networks is a fundamental problem in network science. This study proposes a quantum deep reinforcement learning (QDRL) framework that integrates reinforcement learning with a variational quantum graph neural network, effectively ...
Juechan Xiong +2 more
doaj +1 more source
Quantum Hidden Subgroup Algorithms: An Algorithmic Toolkit [PDF]
arXiv, 2006 One of the most promising and versatile approaches to creating new quantum algorithms is based on the quantum hidden subgroup (QHS) paradigm, originally suggested by Alexei Kitaev. This class of quantum algorithms encompasses the Deutsch-Jozsa, Simon, Shor algorithms, and many more.
arxiv
Algorithms for Quantum Computers [PDF]
, 2012 This paper surveys the field of quantum computer algorithms. It gives a taste of both the breadth and the depth of the known algorithms for quantum computers, focusing on some of the more recent results. It begins with a brief review of quantum Fourier transform based algorithms, followed by quantum searching and some of its early generalizations.
Smith, Jamie, Mosca, Michele
openaire +2 more sources
Advanced Healthcare Materials, EarlyView.This review explores how advanced materials enhance Bio‐FETs for precision healthcare and biosensing. It covers their working principles, surface functionalization, and ultra‐sensitive detection capabilities. The integration of flexible designs, AI, and IoT for real‐time monitoring is discussed, along with challenges like material reproducibility and ...
Minal Pandey +5 more
wiley +1 more source
Quantum optimization algorithm based on multistep quantum computation
New Journal of PhysicsWe present a quantum algorithm for finding the minimum of a function based on multistep quantum computation, and apply the algorithm for solving optimization problems with continuous variables.
Hefeng Wang, Hua Xiang
doaj +1 more source
arXiv, 2007 A quantum algorithm is a set of instructions for a quantum computer, however, unlike algorithms in classical computer science their results cannot be guaranteed. A quantum system can undergo two types of operation, measurement and quantum state transformation, operations themselves must be unitary (reversible).
arxiv