Machine Learning - IBM Research
Feb 20, 2024 · Machine Learning. Machine learning uses data to teach AI systems to imitate the way that humans learn. They can find the signal in the noise of big data, helping businesses improve their operations. We've been in the field since since the beginning: IBMer Arthur Samuel even coined the term “Machine Learning” back in 1959. Our work
Quantum Machine Learning - IBM Research
Jul 12, 2021 · Quantum Machine Learning. We now know that quantum computers have the potential to boost the performance of machine learning systems, and may eventually power efforts in fields from drug discovery to fraud detection. We're doing foundational research in quantum ML to power tomorrow’s smart quantum algorithms. Our work
Systematic literature review: Quantum machine learning and its ...
Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning ...
Overview of algorithms in quantum machine learning - IBM Research
These quantum improvements either try to improve over classical algorithms or consider quantum learning when the data itself could be presented in a quantum manner. In this talk, I will look at a few specific problems/computational models for which quantum algorithms provide a speedup to machine learning tasks in a rigorous manner.
Artificial Intelligence - IBM Research
Dec 20, 2024 · International Conference on Machine Learning. ... ICLR 2024. 38. International Conference on Learning ...
Snap machine learning - IBM Research
Snap Machine Learning (Snap ML in short) is a library for training and scoring traditional machine learning models. Such traditional models power most of today's machine learning applications in business and are very popular among practitioners as well (see the 2019 Kaggle survey for details). Snap ML has been designed to address some of the ...
Machine Learning with Graphs in High-Performance Computing …
The intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for learning graph data. This workshop will focus on the greatest challenges in utilizing high-performance computing (HPC) for machine learning with graphs and methods for exploiting ...
Neuro-symbolic AI - IBM Research
Oct 12, 2021 · Neuro-symbolic AI. We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence. By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we're aiming to create a revolution in AI, rather than an evolution.
When Machine Learning meets Dynamical Systems: Theory and …
The recent wave of using machine learning to analyze and manipulate real-world systems has inspired many research topics in the joint interface of machine learning and dynamical systems. However, the real world applications are diverse and complex with vulnerabilities such as simulation divergence or violation of certain prior knowledge.
Quantum kernels can solve machine learning problems that are …
Jul 12, 2021 · Machine learning is the theme of this year's Qiskit Global Summer School, focusing specifically on the quantum kernel estimation algorithm plus its strengths and limitations. We hope to continue building and disseminating quantum computing research for the wider quantum community, and hope that we can develop a quantum ecosystem that can ...