Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers...
Machine learning techniques use mathematical algorithms and tools to search for patterns in data. These techniques have become powerful tools for many different applications, which can range from biomedical uses such as in cancer reconnaissance, in genetics and genomics, in autism monitoring and diagnosi...
Quantum Machine Learning 来自 Semantic Scholar 喜欢 0 阅读量: 13 作者: I Oshurko 摘要: Machine learning techniques are applied for solving a large variety of practical problems. The tasks attacked by machine learning algorithms include classification, regression, pattern recognition, etc. Traditionally...
Synergizing quantum techniques with machine learning for advancing drug discovery challenge Article Open access 28 December 2024 Exploring chemical compound space with quantum-based machine learning Article 12 June 2020 QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemic...
The science of climatology can be reformed by quantum machine learning (QML) approaches. The modeling and prediction of atmospheric science is a dynamic topic in weather forecasting, which depends on mathematics and data science. Applying QML techniques for weather forecasting is still in its early ...
Quantum machine learning techniques are likely to have far-reaching effects on many of the technologies we have become accustomed to,from aviation to agriculture, with companies such as Lockheed Martin, NASA and Google already on board. Quantum machine learning in Africa ...
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 ...
Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum computing and machine learning. Quantum computers use effects such as quantum coherence and quantum entanglement to process information, which is fundamentally different from classical computers. Quant...
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensivel
techniques of quantum machine learning to achieve a significant improvement in the retrieval efficiency110,111,112. We define the integrated unitary operations of an HRE quantum memory, prove the learning procedure, and evaluate the achievable output SNR values. The local unitaries of an HRE quantum...