sQUlearn − it is a user-friendly library that integrates quantum machine learning with classical machine learning libraries or tools such as scikit-learn. PyQuil − It is developed by Rigetti Computing. It is
1) generated with themake_moons()function of thescikit-learnPython library. The dataset is again not linearly separable in a 2D space (see Fig.5d). In order to classify
With this connection we hope to unlock new and exciting paths for Quantum Computing research that would not have otherwise been possible. Installation See the installation instructions. Examples All of our examples can be found here in the form of Python notebook tutorials Report issues Report bugs...
Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tool...
Machine Learning Theory and ApplicationsEnables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries\nMachine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring...
One of the most emerging areas has been the machine learning field. With existing quantum devices and algorithms, quantum algorithms have already improved some classical processes, and, in contrast, classical machine learning is used to enhance quantum procedures. Within the application of classical ...
From the implementation side, we used Python and the well-known Flask framework for the web-deployment infrastructure. 2.3. Quantum machine learning with QuantuMoonLight To enable experimentations, QuantuMoonLight relies on the APIs provided by Qiskit. Specifically, the tool allows users to exploit...
MachineLearningTheory and Applications: Hands-on Use Cases with Python on Classical and Quantum Machines Author: Xavier Vasques (Author) Publisher finelybook 出版社: Wiley Edition 版本: 1st Publication Date 出版日期: 2024-01-31
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that ar...
ensemble-learningensemble-modelquantum-machine-learningquantum-artificial-intelligencequantum-ensemblequantum-algorithm UpdatedAug 1, 2020 Jupyter Notebook Gate-Level Implementations of a Qubit-Efficient Quantum State Preparation Protocol with Performance Benchmark ...