Quantum Machine Learning
Tensor networks or tensor network states are a class of variational wave functions used in the study of many-body quantum systems.[1] Tensor networks extend one-dimensional matrix product states to higher dimensions while preserving some of their useful mathematical properties.[Wiki]
Developer familly with Machine Learning framework such as tensorflow and Pytorchcan be prepared to simulate quantum algorithms on GPU High Performance Computing. At time of this draft, there are two classes of simulator method that are state vector simulator and Tensor
Quantum-classical hybrid paradigm and variational quantum algorithms
Tensor network for quantum wave WorkFlow for HPC QC AI/ML =========================
The following guides are intend to introduce basic tools for Pythonic workflow tool for computational scientists, AI/ML software engineers, and anyone who needs to run workloads on limited or expensive computing resources including HPC clusters, GPU arrays, quantum computers and cloud services..
Covalent
Covalent is a powerful open-source orchestration platform designed to harmonize the dynamic and complex world of advanced computing. It streamlines the utilization and management of disparate computational resources, encompassing classical high-performance computing (HPC), quantum computing, and AI/ML paradigms. Covalent is a workflow management tool, designed for the orchestration of, and seamless communication between these distinct systems, empowering teams to focus on higher-value R&D objectives.
Quantum computing with Dask Parallel:
We are rework on Quantum Algorithms for Time Series Anomaly Detection to verify workflow is work on HPC cluster and Dask. Then next PennyLane Lighting.gpu.
Let solve notebook issue change ‘requirement.txt’ for notebook to version 6.5.6 Quantum Variational Rewinding for Time Series Anomaly Detection