“Computation with Tensor Networks”,
Pan Zhang
Abstract
Pan Zhang will present methods and algorithms for solving statistical mechanics problems, combinatorial optimization problems, and quantum circuit simulations, in an integrated framework based on tensor networks. In statistical mechanics problems, the partition function at a finite temperature can be obtained by contracting a tensor network that is converted from the statistical mechanics problem. When equipped with the “Tropical” algebra, the tensor network contraction can be used to obtain ground state energy and entropy of the model directly at zero temperature. When the interactions in the statistical mechanics model are complex, computing the partition function acts as estimating the amplitude of a basis vector of the final state of a quantum circuit, thus tensor network contractions can be used to simulate quantum computers. Pan Zhang will introduce approximate and exact algorithms for contracting tensor networks, and their wide applications, particularly in simulating Google’s Sycamore quantum circuits.
Bio
Pan Zhang is working as a professor at the Institute of Theoretical Physics, Chinese Academy of Sciences (ITP, CAS). He got his Ph.D. from Lanzhou University in 2009 and did post-docs at Turin, Paris and the Santa Fe Institute with Riccardo Zecchina, Florent Krzakala, and Cris Moore, respectively, before joining ITP, CAS in 2015. Pan Zhang’s research is in the interdisciplinary field of statistical physics, machine learning, quantum many-body, and quantum computation.
“Fusion, exploration and unmixing of EO data: challenges and perspectives”,
Paolo Gamba
Abstract
The more rich and detailed in terms of spectral, spatial and temporal resolution are the EO data, and the closer to global is the coverage by these data, the more complex are becoming the challenges to achieve a meaningful analysis. This talk intends to explore the major challenges ahead of researchers and practitioners in terms of fusion of EO data at multiple resolutions, EO and non-EO data, EO data exploration in long and complex sequences, and finally with respect to the problem of unmixing hyperspectral data in large geographical areas.
Bio
Paolo Gamba is Professor at the University of Pavia, Italy, where he leads the Telecommunications and Remote Sensing Laboratory. He has been working for more than 25 years in urban remote sensing applications, addressing the need to analyze large amounts of data from multiple satellites, from radar to multispectral, from hyperspectral to thermal, always considering a global perspective. He has been invited to give keynote lectures and tutorials on several occasions about urban remote sensing, data fusion, EO data for physical exposure and risk management. He published more than 170 papers in international peer-review journals.
“The current state of quantum computing”,
Vedral Vlatko
Abstract
I will review the basics of quantum computing and talk about different qubit platforms that are currently being pursued. I will also comment on different models of quantum computation and mention their advantages and disadvantages. In the short term, various small-scale quantum subroutines might find useful applications within the existing hybrid quantum-classical regime. However, in the long term, the quantum search algorithm will offer a ubiquitous speed-up. Applications range from quantum metrology to simulations of complex systems, including weather forecast and financial quantitative analysis.
Bio
Vlatko Vedral (PhD and BSc at Imperial College) is a professor of quantum information at Oxford and professor of physics at the National University of Singapore. He has published over 350 research papers on various topics in quantum physics and quantum computing and is one of the Clarivate Highly Cited Researchers. He has given numerous invited plenary and public talks during his career. These include a specialised talk at a Solvay meeting (2010) and a popular one at the International Safe Scientifique (2007). He was awarded the Royal Society Wolfson Research Merit Award in 2007, the World Scientific Medal and Prize in 2009, the Marko Jaric Award in 2010 and was elected a Fellow of the Institute of Physics in 2017 and a member of the European Academy of Sciences in 2020. He is consulting the World Economic Forum on the Future of Computation. Vlatko is the author of 4 textbooks and 2 popular books (“Decoding Reality” and “From Micro to Macro”). He gives regular interviews to the media and is actively engaged in popularization of physics also by writing for New Scientist, Scientific American and major UK and overseas newspapers.
“Towards quantum machine learning applications”,
Iordanis Kerenidis
Abstract
We discuss prospects and challenges towards the first quantum machine learning applications with NISQ machines.
Bio
Iordanis Kerenidis received his Ph.D. from the Computer Science Department at the University of California, Berkeley, in 2004, under Umesh Vazirani. After a two-year postdoctoral position at the Massachusetts Institute of Technology with Peter Shor, he joined the Centre National de Recherche Scientifique CNRS in Paris as a research director. He has been the coordinator of a number of EU-funded projects including an ERC Grant, and he is the founder and director of the Paris Centre for Quantum Computing. His research is focused on quantum algorithms for machine learning and optimization, including one of the first end-to-end quantum machine learning applications for recommendation systems, demonstration of quantum classification on trapped ion hardware and NISQ algorithms for Monte Carlo methods in Finance. He is currently working as the Head of Quantum Algorithms Int. at QC Ware Corp.
“Machine Learning tools for PDEs and PDEs tools for Machine Learning”,
Luca Biferale
Abstract
We briefly summarize the main obstacles for applications of data-driven tools to study complex fluid dynamics in general and turbulence in particular. We discuss new tools in the box based on the embedding of the PDE solver in the Machine Learning loop with two examples one for Data Assimilation and the other for sub-grid-scale modelling.
Bio
Luca Biferale is a Full Professor of Theoretical Physics at the University of Rome ‘Tor Vergata’. Main interests include Turbulence, Computational Physics, Kinetic Models and, more recently, Machine Learning and Reinforcement Learning for complex fluids and complex flow applications. He is elected fellow of the APS and Euromech and he has been awarded of 2 ERC AdG Grants (2014-2019) and (2021-2026).
“Quantum classical hybrid computing models in modular HPC systems with potential applications in Earth Observation”,
Kristel Michielsen
Abstract
Quantum computing and quantum annealing are new, innovative ways of computing for some of the most complex problems with potential applications in simulation, optimization and machine learning. The expectations for the use of quantum computers and quantum annealers in science and industry are high. Although it will still take many years for quantum computing technology to become fully mature, an early entry into the practical use of this new disruptive technology is of great urgency.
Practical application requires the integration of quantum computers into existing HPC infrastructures in the form of quantum-classical hybrid computing models. The “Jülich UNified Infrastructure for Quantum computing (JUNIQ)”, a quantum computer user facility which is set up at the Jülich Supercomputing Centre (JSC), will integrate quantum computing devices with various quantum technology readiness levels into the modular supercomputing architecture of JSC. Within JUNIQ, user support and training in HPC and quantum computer usage will be provided, software tools, modelling concepts and algorithms will be developed, and it will play an important role in the development of prototype applications.
We also discuss some preliminary results for prototype Earth-Observation applications, which are based on quantum-classical classification and regression algrithms for remote sensing data.
Bio
Prof. Dr. Kristel Michielsen received her PhD from the University of Groningen, (the Netherlands) for work on the simulation of strongly correlated electron systems in 1993. Since 2009 she is group leader of the research group Quantum Information Processing at the Jülich Supercomputing Centre, Forschungszentrum Jülich (Germany) and is also Professor of Quantum Information Processing at RWTH Aachen University (Germany). Kristel Michielsen and her research group have ample experience in performing large-scale simulations of quantum systems. She has expertise in, on the one hand simulating quantum computers and quantum annealers, and on the other hand in benchmarking and studying prototype applications for this new compute technology by using the various quantum computing and quantum annealing systems that are nowadays available. Together with Prof. Lippert she is building up the JUNIQ infrastructure at the JSC.