IBM’s new tool lets developers add quantum-computing power to machine learningQuantum machine learning proposes new types of models that leverage quantum computers’ unique capabilities to, for example, work in exponentially higher-dimensional feature spaces to improve the accuracy of models. Once they have built a quantum machine-learning model in Qiskit, developers will be able to test the algorithm on classical computers, but also on IBM’s cloud-based quantum systems. Quantum computation offers another potential avenue to increase the power of machine-learning models, and the corresponding literature is growing at an incredible pace,” said the Qiskit applications team. ” Finally, Qiskit Machine Learning allows users to integrate their new quantum neural networks directly into the PyTorch open-source machine-learning library. “Using classical and quantum machine-learning models may allow researchers to better understand quantum chemistry and physics, opening up plenty of new applications and research directions. For example, Qiskit Machine Learning provides QuantumKernel, a tool that computes kernel matrices for a given dataset into a quantum framework. Qiskit Machine Learning is now available and includes the computational building blocks that are necessary to bring machine-learning models into the quantum space.
Article from ZDNet.