Srinivasan Arunachalam

Stefan Wörner

Dmitri Maslov

Jin-Sung Kim

Sergey Bravyi

Theodore J. Yoder

Sarah Sheldon

David Sutter

Lior Horesh

Yunchao Liu

Kristan Temme

Amira Abbas

Junye Huang

News

Research

IBM Quantum Challenge generates better solutions than challenge creators thought possible

Proving the advantage of quantum scratch space over classical scratch space

New tensor algebra changes the rules of data analysis

The power of quantum neural networks

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

Quantum

ACS Spring 2021

VLSI Technology 2021

ACL-IJCNLP 2021

Quantum Error Correction

Machine Learning

Mathematical Sciences

Quantum Community

Quantum Information Science

Quantum Circuits and Software

Quantum Machine Learning

IBM Quantum has a clear roadmap to practical quantum advantage in the coming years. This goal requires scalable, error-corrected quantum systems. We develop bottom-up approaches to the problem of noisy qubits and incorporate error correction techniques to realize this technology’s true potential.

Our long history of research has had an enduring impact on computer science, operations research, and information theory. We’re currently focused on optimization, probability, complexity, geometry of data, as well as linear and multi-linear algebra, to deliver tools that are fundamental to big data and AI.

We’re focused on foundational mathematical research with the aim of delivering tools to that are fundamental to big data and AI.

Our physical sciences researchers seek to understand materials and their properties through advanced characterization methods, explore novel devices and materials for new computational platforms, and apply physics to improve AI algorithms and interpretability.



Physical Sciences

We’re using physics to improve AI algorithms and interpretability, as well as novel materials for new computational platforms. 


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 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

Our work

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

New tensor algebra changes the rules of data analysis

The power of quantum neural networks

Proving the advantage of quantum scratch space over classical scratch space

Publications

Leveraging Abstract Meaning Representation for Knowledge Base Question Answering

Proving the advantage of quantum scratch space over classical scratch space

Quantum Computing with Superconducting Circuits

Sample-efficient learning of interacting quantum systems

Improved accuracy with a smaller footprint on the quantum computer

Role of materials in quantum computing with superconducting circuits

Related teams

Mathematical Sciences

Physical Sciences

Quantum Circuits and Software

Quantum Error Correction