ISC 2022 Workshop | Quantum and Hybrid Quantum-Classical Computing Approaches

Exploring the potential of quantum computing & hybrid quantum-classical computing approaches.

When: 2 June 2022 | 08:50 – 13:00 (CEST)​

Where: ISC 2022​ Hamburg, Germany

Quantum computing is an emerging technology that promises to solve extremely complex problems beyond the capabilities of conventional and future supercomputers. Further scaling up of the technology and additional fault tolerance is needed in order to solve relevant large-scale problems. ​Combining quantum computing strategies with the currently available classical high performance computing (HPC) approaches might provide better performance for a variety of scientific problems. For the short-term applicability of quantum computing, it is important to investigate h​ybrid quantum-classical approaches. ​

Organised by: 

  • Prof. Vassil Alexandrov –​ STFC Hartree Centre, UK
  • Prof. Dieter Kranzlmueller – Leibniz Supercomputing Centre (LRZ)​, Germany
  • Dr. Ivano Tavernelli – IBM, UK
  • Dr. Luke Mason – STFC Hartree Centre​, UK​


08:50 – 09:00Welcome
09:00 – 09:40Q-Exa: Quantum Computing extension of ExaScale-HPC​​Peter Eder
IQM, Germany
09:40 – 10:00Simulating quantum algorithms with the Atos Quantum Learning Machine (AQLM): a datacentre perspective​Luigi Iapichino
Leibniz Supercomputing Centre (LRZ)​, Germany
10:00 – 10:20​Quantum Machine Learning Framework for Virtual Screening in Drug DiscoveryEmre Sahin 
STFC Hartree Centre, UK
10:20 – 10:40​Co-design a quantum simulator for GMR materialXiaolong Denq
Leibniz Supercomputing Centre (LRZ)​, Germany
10:40 – 11:00​State Preparation for Arrays of Rydberg Atoms: Numerical Benchmarks​Kemal Bidzhiev
PASQAL, France
11:00 – 11:30BREAK
11:30 – 11:50An Overview of the State of Quantum Optimisation​Francesca Schiavello
STFC Hartree Centre​, UK​
11:50 – 12:10​Quantum computing for nuclear physics​Alessandro Baroni
12:10 – 12:50​Scientific Discovery and Innovation with Quantum SimulationTravis Humble 
12:50 – 13:00Concluding Remarks

Full Programme:​ ​

​09:00 – 09:40​Peter Eder
IQM, Germany
​Q-Exa: Quantum Computing extension of ExaScale-HPC​
​IQM 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 (01/2013-12/2017)​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​Luigi Iapichino
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).​Current 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.
​10:00 – 10:20
​Emre Sahin 
STFC Hartree Centre, UK

[Contributors: Stefano Mensa, Francesco Tacchino, Panagiotis Barkoutsos and Ivano Tavernelli]
​Quantum Machine Learning Framework for Virtual Screening in Drug Discovery
​Emre 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 – 10:40Xiaolong Denq
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.
​Giant 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 – 11:00Kemal Bidzhiev
PASQAL, France
​State Preparation for Arrays of Rydberg Atoms: Numerical Benchmarks
​[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.
​11:30 – 11:50​Francesca Schiavello
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.​This 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

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