08:50 - 09:00
09:00 - 09:40
Q-Exa: Quantum Computing extension of
Germany GmbH, Head of Partnerships, global (set 10/2021), Head of Operations
(03/2020-09/2021); Bayerisches Staatsministerium für Digitales, Referent
(08/2019-10/2020); Carl Zeiss SMT (ANÜ über Brunel), Projektmanager:
Externalisierungsstrategie: Aufbau von 100 externen MA, Einführung Agiler
Arbeitsweisen, Technisches Projekt: Steuersoftware für Produktion
(11/2018-08/2019). Abschluss als Doktor der Physik `(02/2022);
Wissenschaftlicher Mitarbeiter/Doktorand: Walther Meißner Institut
Quantum computers have a considerable potential to provide solutions for problems that cannot be solved with classical computers. Since all relevant experimental facilities are located abroad, the development of own hardware and software capacities in Germany is essential in order to reduce dependencies and the use of restricted resources. Therefore, the main goal of the Q-Exa project is to fund the development of a QC demonstrator in Germany with specific technical and temporal requirements. Within Q-Exa the consortium will provide a technology platform to enable top-level research and contribute to reach technology sovereignty on long term in Germany and Europe. In this talk we will present the project structure and preliminary results.
09:40 - 10:00
Leibniz Supercomputing Centre (LRZ), Germany
[Contributors: Mark Fellows, Stefan Huber and Stefano Mensa]
Simulating quantum algorithms with the Atos Quantum Learning Machine (AQLM): a datacentre perspective
Luigi Iapichino holds the position of Lead of the User Enablement and Applications group, in the Quantum Computing and Technologies department at LRZ. He is co-founder of the Bavarian Quantum Computing eXchange (BQCX). Among his research interests are quantum computing simulations on high-end HPC systems and HPC/QC integration. He completed in 2005 his PhD in physics at the Technical University of Munich, working at the Max Planck Institute for Astrophysics. Before moving to LRZ in 2014, he worked at the Universities of Würzburg and Heidelberg, involved in research projects related to computational astrophysics. At LRZ he was team lead of the Application Lab for Astro and Plasma Physics (AstroLab).
quantum hardware is still experimental under many viewpoints and only limited
chances for access are offered to the community of users of academic HPC
centres. Therefore, simulation of quantum computation is a viable alternative
for researchers and programmers working on quantum algorithm development. We
present in this talk the experiences on the Atos Quantum Learning Machine (QLM)
done from user and administration sides on the systems installed at the Leibniz
Supercomputing Centre (LRZ) in Garching and at the STFC Hartree Centre in
Warrington. The operation of these systems has shown us that the access
strategy and the workflow of the users are different from the ones seen in a
typical HPC environment, and this has a profound impact in the way resource
scheduling has to be done on the QLM. We highlight early user results on the
systems, preliminary performance outcomes and an outlook on how the QLM fits
into upcoming efforts on QC integration into the HPC ecosystem.
STFC Hartree Centre, UK
[Contributors: Stefano Mensa, Francesco Tacchino, Panagiotis Barkoutsos and Ivano Tavernelli]
Quantum Machine Learning Framework for Virtual Screening
in Drug Discovery
is a High-Performance Software Engineer at the Hartree Centre, home to some of
the most advanced computing, data and AI technologies in the UK. He has
experience of applied HPC solutions to a range of diversified research fields.
As a member of the Hartree National Centre for Digital Innovation (HNCDI), Emre
is working in partnership with IBM towards application of Quantum Machine
Learning methodologies in early stages of material science, drug discovery and
computational pathology. He also worked on projects focusing on tackling
compute and memory intensive problems and developing novel scalable stochastic
and hybrid mathematical methods and algorithms such as scalable hybrid Monte
Carlo algorithms for variety of supercomputing and accelerator architectures
for large-scale linear algebra, optimization, computational finance,
environmental models, computational biology.
Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases. In this paper, we reformulate a generic, classical Support Vector Classifier (SVC) algorithm for LB-VS of a real-world database
of molecules using a Quantum Kernel estimator to assess prospective quantum advantage. We heuristically prove that our quantum integrated framework can, at least in some relevant instances, provide a tangible advantage compared to the state-of-art classical algorithms operating on the same dataset, showing strong dependence on target and features selection method.
10:20 - 10:40
Leibniz Supercomputing Centre (LRZ), Germany
Co-design a quantum simulator for GMR material
Xiaolong Deng studied quantum simulation with trapped ions at the MPQ and obtained his doctorate in quantum physics from the TU Munich in 2007. From 2007 to 2009 he was a post-doctor at the CNRS in Grenoble. From 2009 to 2021 he was a research associate at the University of Hannover. Since 2021 he has been a scientist involved in quantum systems and applications and supporting the HPC-QC integration at the LRZ.
magnetoresistance (GMR) is a quantum effect in ferromagnetic material, and has
found a lot of applications in hard-disk drives, sensors, and etc. The essence
of GMR can be explained using the RKKY model (Ruderman-Kittel-Kasuya-Yosida).
We co-design a quantum simulator for the model with RKKY interactions, and
investigate the transport of qubit excitations of this model in the noisy
environment. By numerical simulations our result show that the qubit
information can be transported super-diffusively and diffusively under
different couplings, respectively. This quantum simulator may help us
understand and design new materials with the GMR effect.
10:40 - 11:00
State Preparation for Arrays of Rydberg Atoms:
[Contributors: Alvin Sashala Naik, Aleksander Wennersteen, Loic Henriet and Sebastian Grijalva]
Programmable arrays of Rydberg neutral atoms are one of the most versatile and promising architectures for NISQ era quantum processors. In recent years, the number of atoms controlled by experimental devices has significantly risen, beyond the limits of conventional numerical simulation methods. Here we focus on a recent significant task that these platforms have addressed: the preparation of states with antiferromagnetic order on two-dimensional arrays. By exploiting a computing cluster consisting of 80 Nvidia A100 GPUs and using matrix product state methods, we present a study of several properties of the ground state, up to hundreds of qubits. The quantities considered are useful to compare and benchmark future experimental implementations, as they probe these and higher system sizes.
11:00 - 11:30
11:30 - 11:50
STFC Hartree Centre, UK
An Overview of the State of Quantum Optimisation
Francesca is a High Performance Software Sngineer in extreme scale computing and engineering at the Hartree Centre in the UK. She is working on modelling 2D isotropic turbulence using machine learning and is interested in integrating quantum computing to this. Her Bachelor's Degree was in Mathematics at Boston University, and her Master is in Computational Science at the University of Amsterdam and Vrije University. While studying she participated in PRACE's Summer of HPC with Hartree Centre, creating a machine learning model for improving queue efficiency of SLURM jobs. Her thesis was on solving Poisson's equation in the presence of sharp objects, using geometric multigrid and level set methods. To continue developing her skills as a computational scientist she has recently been delving into the quantum world.
presentation aims to give a brief overview of the current state of the
literature in regard to quantum optimisation. It will delve into background
information about the challenges faced by the field, the recent solutions
proposed and the advancement of the algorithms over the past 10 years. The talk
wouldn’t be complete without starting from Shor’s algorithm, it would follow by
mentioning variational quantum algorithms, their successes and hindrances. One
of the challenges in searching for quantum supremacy is the fact that as
quantum algorithms compete with classical computing these are also advancing in
leap and bounds. As such some hybrid quantum-classical algorithms will also be
explored by giving some examples of their recent applications. This short
synopsis of the field will end by looking at some of the current difficulties
of quantum algorithms and their future potential.
11:50 - 12:10
[Contributors: Joseph Carlson, Rajan Gupta, Andy Li, Gabriel Perdue and Alessandro Roggero]
Quantum computing for nuclear physics
Alessandro Baroni got his PhD in Theoretical Nuclear Physics in 2017 at Old Dominion University. He has been a postdoctoral researcher at the University of South Carolina form September 2017 to May 2019 and, since June 2019, he has been a postdoctoral researcher at Los Alamos National Laboratory in the Theoretical Division. His current research interests lie at the intersection of quantum algorithms and quantum simulations on current (or near term) quantum hardware, and quantum many-body problems (originated in nuclear physics, astrophysics or condensed matter).
Quantum computing holds the promise of enabling calculations of real-time evolution of quantum systems, with a wide range of applications across many areas of current interest such as electronic many-body problems, nuclear and particle physics. In this talk I will discuss the problem of calculating the real time response functions on a Quantum Computer focusing on scattering problems originated in Nuclear Physics and described by a modified ?Hubbard model. After introducing quantum algorithms best suited to perform simulation of quantum dynamics for the problem at hand, I will describe their novel implementations on current gate based quantum computers. Finally, I will present results on current quantum hardware and discuss the error mitigation protocols used.
12:10 - 12:50
Scientific Discovery and Innovation with Quantum Simulation
Travis Humble is an Interim Director at the Department of Energy's Quantum Science Center, a Distinguished Scientist at Oak Ridge National Laboratory, and director of the lab's Quantum Computing Institute, leading the development of new quantum technologies and infrastructure to impact the DOE mission of scientific discovery. Travis is editor-in-chief for ACM Transactions on Quantum Computing and co-chair of the IEEE Quantum Initiative. Travis also holds a joint faculty appointment with the University of Tennessee Bredesen Center for Interdisciplinary Research and Graduate Education to work with students in developing energy-efficient computing solutions.
New methods for modelling and simulation of quantum mechanical systems offer a revolutionary merger between physics and computer science. Scientific disciplines will benefit through accelerating the time-to-solution of such simulations and broadening the regime of physical models. Here we present leading techniques for quantum simulation applied to specific scientific domains, testing and evaluation on current hardware, and the potential to realize quantum computational advantage. We report applications that simulate novel phases of quantum matter using state-of-the-art methods for error mitigation with noisy intermediate-scale quantum devices, and we detail the intermediate goal of integrating quantum processing units with high-performance computing workflows. We highlight the latest efforts of the Quantum Science Center to integrate these techniques and deliver the next-generation of quantum simulation platforms for empowering scientific discovery and innovation.
12:50 - 13:00