Peter Waggett

Alessandro Curioni

Srinivasan Arunachalam

Stefan Woerner

David Sutter

Yunchao Liu

Kristan Temme

Panagiotis Barkoutsos

Anthony Annunziata

Ivano Tavernelli

Abe Asfaw

Amira Abbas

Josie Kies

Ryan Mandelbaum

Junye Huang

News

Research

Release

IBM Quantum Challenge generates better solutions than challenge creators thought possible

The power of quantum neural networks

Quantum kernels can solve machine learning problems that are hard for all classical methods

IBM welcomes CERN as a new hub in the IBM Quantum Network

The Qiskit Global Summer School is returning with a focus on Quantum Machine Learning

The Discovery Accelerator comes to Europe

An efficient method to learn quantum many-body systems

Quantum

STACS 2025

AI Festival 2025

Quantum Error Correction & Mitigation

Quantum Use Cases

Machine Learning

Quantum Community

Quantum Research

Quantum Software

Materials Discovery

Accelerated Discovery

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.

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. 

Quantum advantage requires innovation at every layer of the quantum stack. Our software team works alongside the hardware developers to build an expansive library of quantum circuits and to create programming tools so that anyone can develop their own quantum software.

Quantum Circuits and Software

Quantum computing represents a paradigm shift in the types of problems we can solve. Getting it right requires deep understanding of the underlying theory. We’re exploring the fundamentals of quantum science, from entanglement and superposition to the development of novel quantum algorithms. 

Quantum Information Science

To trust AI systems, explanations can go a long way. We’re creating tools to help debug AI, where systems can explain what they’re doing. This includes training highly optimized, directly interpretable models, as well as explanations of black-box models and visualizations of neural network information flows.

Explainable AI

We’re creating tools to help AI systems explain why they made the decisions they did. 

photograph of a large group of participants of the qiskit global summer school

The power of quantum neural networks (Credit: Nature Computational Science)

CERN & IBM Research exploring quantum computing for high-energy physics

Quantum Machine Learning

Our work

IBM welcomes CERN as a new hub in the IBM Quantum Network

Quantum kernels can solve machine learning problems that are hard for all classical methods

The power of quantum neural networks

IBM Quantum Challenge generates better solutions than challenge creators thought possible

The Discovery Accelerator comes to Europe

An efficient method to learn quantum many-body systems

Publications

Inner product estimation with limited quantum communication

Quantum convolutional neural networks to optimize the design of synthetic immune cells

Comparing Natural Language Processing and Quantum Natural Processing approaches in text classification tasks

Challenges and opportunities in quantum optimization

Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training

How can quantum computing be applied in clinical trial design and optimization?

Related topics