This is the Qiskit s machine learning module. There is an initial set of function here that will be built out over time. At present it has sample sets that can be used with classifiers and circuits used in machine learning applications. Class for errors returned by Qiskit's machine learning module. A neural network is a parametrized network which may be defined as a artificial neural network - classical neural network - or as parametrized quantum circuits - quantum neural network.
IBM is releasing a new module as part of its open-source quantum software development kit, Qiskit, to let developers leverage the capabilities of quantum computers to improve the quality of their machine-learning models. Qiskit Machine Learning is now available and includes the computational building blocks that are necessary to bring machine-learning models into the quantum space. Machine learning is a branch of artificial intelligence that is now widely used in almost every industry. The technology is capable of crunching through ever-larger datasets to identify patterns and relationships, and eventually discover the best way to calculate an answer to a given problem. Researchers and developers, therefore, want to make sure that the software comes up with the most optimal model possible – which means expanding the amount and improving the quality of the training data that is fed to the machine-learning software.
Why did you think to combine Qiskit, a quantum-computing framework, with PyTorch, a machine-learning framework? Karel Dumon: Classical machine learning is currently benefiting hugely from the open-source community, and this is something we want to leverage in quantum too. Our project focuses on the potential application of quantum computing for machine learning, but also on the use of machine learning to help progress quantum computing itself. Through our project, we hope to make it easier for machine learning developers to explore the quantum world. Patrick Huembeli: To that effect, it makes Qiskit very accessible for people with a classical machine learning background -- they can treat the quantum nodes just as another layer of their machine learning algorithm.
Mark your calendar: The Qiskit Global Summer School is back, July 12-23, 2021! Last year, the IBM Quantum team made history by hosting a free, virtual quantum computing crash course for over 4,000 learners. This year, we're hoping to host another 4,000 students -- now with a focus on quantum machine learning (QML). This year's Qiskit Global Summer School will feature two weeks of live lectures and hands-on laboratory sessions where students can apply what they've learned using Qiskit code using the new Qiskit machine learning application module. Lectures will begin with an introduction to quantum computing and simple quantum algorithms, before diving into the foundations of classical machine learning and understanding how these concepts translate to quantum computing.
Working with real quantum computers just got easier for experts in chemistry, artificial intelligence, and optimization. Building on QISKit, our open source quantum information science kit for software development, we've released ACQUA -- Algorithms and Circuits for Quantum Applications. This new open source software allows classical computer applications to send complex operations to be run on quantum computers, over the cloud. Let me start by explaining the quantum software stack, and where QISKit and ACQUA fit. At the lowest level is the hardware where the qubits sit at the very cold temperature of 15 mK.