Jacob Biamonte is an American physicist, quantum computer scientist and Associate Professor at the Skolkovo Institute of Science and Technology and Lead of Skoltech’s Quantum Software Initiative. Biamonte has made several contributions to the theory and implementation of quantum computers.
Biamonte was employed as one of the world’s first quantum software programmers at D-Wave Systems Inc. in Vancouver B.C., Canada. From this work, he published several celebrated proofs establishing the computational universality of specific quantum many-body ground states used in adiabatic quantum computing. He played a central role in developing aspects of contemporary quantum computing theory, including research recognized as pioneering the emerging field that unites quantum information with complex network theory and machine learning.
Biamonte earned an award winning Doctorate at the University of Oxford and his collective research was recognized in 2013 with the Shapiro Fellowship in Mathematical Physics. He has advised both government agencies and industry and lead several successful interdisciplinary research teams and projects, comprised of students and research staff with backgrounds spanning physics, mathematics and computer science. He was invited to become a lifelong member of the Foundational Questions Institute (FQXi) and serves on the editorial boards of several journals including NJP.
Quantum Complexity Science
- quantum machine leanring
- quantum computing algorithms for chemical physics simulation
Tensor Network States
Network Information Theory
Our research induced a topical dichotomy: (i) we work to understand collective quantum effects at the fundamental or basic science level and (ii) this understanding enables us to utilize these same collective quantum effects in e.g. quantum enhanced machine learning software or quantum computer algorithms simulating chemical physics.
Collective quantum effects arise when at least one variable is unbounded and the system/problem exhibits an emergent property: such as an empirically exhibited—though not yet proven—exponential separation between quantum and stochastic computational processes. (Here the unbounded variable would be the size of the problem instance.)
We call our topic, quantum complexity science, as we merge ideas from several disciplines to contrast stochastic and quantum systems in increasingly precise terms. We utilize this knowledge primarily as a quantum computational resource.
Quantum Machine Learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe and Seth Lloyd
Nature 549, 195-202 (2017) 10.1038/nature23474
Tensor Networks in a Nutshell
Jacob Biamonte and Ville Bergholm
to appear in Contemporary Physics – arXiv:1708.00006
Topological classification of time-asymmetry in unitary quantum processes
Jacob Biamonte and Jacob Turner
in review (2017) arXiv:1703.02542
Charged String Tensor Networks
Proceedings of the National Academy of Sciences 114:10, 2447 (2017)
Complex Networks: from Classical to Quantum
Jacob Biamonte, Mauro Faccin and Manlio De Dominico
in review (2017) arXiv:1702.08459
- Lectures on Quantum Machine Learning