Quantum Machine LearningDeep LearningReal-World ApplicationsFake News DetectionQuantum AdvantageMany prevalent issues in today's society, such as fake news detection, can be efficiently addressed using artificia
题目:Quantum Machine Learning and its applications 时间:2022年3月17日(周四)16:00 主办方:Intelligent Computing 报告人简介: 俞上,男,之江实验室量子传感研究中心博士后。2020年于中国科学技术大学获博士学位,长期从事量子计算、量子模拟领域的实验研究,获得2018年度博士研究生国家奖学金,2020年度中科院院长奖。研究...
The efficiency of Machine Learning systems can be increased by the usage of Quantum Computing. Even AI systems may be made far more effective and efficient by employing a quantum computer to analyze data and determine the most effective ways to carry out different activities. This kind of optimiz...
Frey NC, Akinwande D, Jariwala D,et al.Machine learning-enabled design of point defects in 2D materials for quantum and neuromorphic information processing. ACS Nano, 2020, 14: 13406–13417 et al.Unveiling the complex structure-property correlation of defects in 2D materials based on high through...
In the context of quantum technologies, the generation and manipulation of single photons has become a key element for applications such as quantum communication and quantum computing, as well as quantum metrology, biology and experiments probing the foundations of quantum physics discussed in an accomp...
In the last decades a new class of atomistic simulation techniques has emerged that combines machine learning (ML) with simulation methods based on quantum mechanical (QM) calculations. Such ML-based acceleration can dramatically increase the computational efficiency of QM-based simulations and enable ...
The last decade has seen spectacular activity and successes in the general area of data-driven atomistic computations. All modern atomistic computations use either some form of quantum mechanical scheme (e.g., DFT) or a suitably parameterized semi-empirical method to predict the properties of materi...
PennyLane is a platform for Quantum Computing and Machine Learning (ML). It integrates with ML libraries such as PyTorch and TensorFlow, allowing researchers to create and optimize circuits and algorithms. It also offers a range of hardware backends from different QPU providers....
One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research.
The last decade has seen spectacular activity and successes in the general area of data-driven atomistic computations. All modern atomistic computations use either some form of quantum mechanical scheme (e.g., DFT) or a suitably parameterized semi-empirical method to predict the properties of materi...