QCHALLengeNewsEN

a:3:{s:6:"locale";s:5:"en_US";s:3:"rtl";i:0;s:9:"flag_code";s:2:"us";}
QCHALLenge publications at ICAART 24 in Rome

QCHALLenge publications at ICAART 24 in Rome

Presenting four papers on quantum machine learning

(March 6, 2024/Rome) Contributing to the exploration of applications and fundamental research in quantum machine learning (QML), members of the QCHALLenge consortium presented four accepted papers at the Internatinal Conference on Agents and Machine Learning in Rome. Two of these papers originated from the QC Optimization Challenge at LMU, shedding light on the application of QML for modelling chemical processes as well as anomaly detection. in the other two papers, we prosed (1) an AI-inspired ansatz design for the architecture of parameterized quantum circuits, and (2), a sequential assembly of the Cost-Hamiltonian in the Variational Quantum Eigensolver, that shows one way of tackling the problem of barren-plateaus.

For more details, here the links to the published papers as well as their freely available preprints:

  • J. Stein, T. Rohe, F. Nappi, J. Hager, D. Bucher, M. Zorn, M. Kölle and C. Linnhoff-Popien, “Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 3, pages 1127–1134, Feb. 2024. DOI: 10.5220/0012449800003636. arXiv: 2306.05047.
  • J. Stein, N. Roshani, M. Zorn, P. Altmann, M. Kölle and C. Linnhoff-Popien, “Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 2, pages 99–109, Feb. 2024. DOI: 10.5220/0012312500003636. arXiv: 2312.05552.
  • J. Stein, M. Poppel, P. Adamczyk, R. Fabry, Z. Wu, M. Kölle, J. Nüßlein, D. Schuman, P. Altmann, T. Ehmer, V. Narasimhan and C. Linnhoff-Popien, “Benchmarking Quantum Surrogate Models on Scarce and Noisy Data”. In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 3, pages 352–359, Feb. 2024. DOI: 10.5220/0012348900003636. arXiv: 2306.05042.
  • J. Stein, D. Schuman, M. Benkard, T. Holger, W. Sajko, M. Kölle, J. Nüßlein, L. Sünkel, O. Salomon and C. Linnhoff-Popien, “Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection”.  In Proceedings of the International Conference on Agents and Artificial Intelligence – Volume 2, pages 177-185, Feb. 2024. DOI: 10.5220/0012326100003636. arXiv: 2306.04998.

Kickoff 7th QC Optimization Challenge

Kickoff 7th QC Optimization Challenge

Students from the LMU Munich will provide valuable insights into possible Quantum Applications

(May 7, 2024/Munich) The practical course will provide the ability to model optimization problems for quantum computers, as well as an introduction to practical work with existing quantum computers. Four computers are currently available in the QAR Lab for this purpose: IBM Q System Two, IonQ Aria-2, Fujitsu DAU, D-Wave Advantage.
In cooperation with our industry partners BMW Group, SAP and Siemens projects with high relevance for concrete applications are calculated by our students on quantum computers in this semester. With the three use cases (1) Topology Optimization (BMW), (2) VM Balance Assignment (SAP), (3) Image Classification (SAP), (4) BnB hybrid Optimization (Siemens) and (5) Distribution Modelling (Siemens), the students will explore the main applications of quantum computing: machine learning, simulation and optimization.

We are pleased to make this possible for the 7th time and are looking forward to the results at the end of this semester.

About the QCP

The Quantum Computing Optimization Challenge is a practical course initiated by Prof. Dr. Claudia Linnhoff-Popien, head of the Chair of Mobile and Distributed Systems at LMU Munich. It took place for the first time in 2020 and has been an integral part of the curriculum ever since. This practical course teaches the ability to model optimization problems for quantum computers, as well as an introduction to practical work with existing quantum computers. Four computers are currently available for this purpose in the QAR Lab: IBM Q System Two, IonQ Aria-2, Fujitsu DAU, D-Wave Advantage. In cooperation with well-known partners from industry, tasks with strong relevance for practical applications are assigned each semester. The students have the opportunity to execute and compare one task on two computers. In the summer semester 2024, it takes place for the 7th time. Use cases in areas of optimization, machine learning and simulation are on the agenda.


Quantum Summit 2023 in September

Quantum summit 2023 in september

Jonas Stein, Research Associate of the QAR Lab at LMU Munich will provide valuable insights into the current QC research projects - especially on the highlights from the Quantum Computing Optimization Challenge.

(Jun. 13, 2023/Munich) Quantum computing isn’t just a future trend anymore – it’s here to stay and promising to solve both local and global problems in various fields that are beyond the reach of today’s computers. In September 20th-21st, the Quantum Summit will take place in Berlin, Germany. It will bring together a wide range of experts from the research community, as well as decision-makers from business and government, to share important insights in the field of quantum computing. Jonas Stein, Research Associate of the QAR Lab at LMU Munich will provide valuable insights into the current QC research projects – especially on the highlights from the Quantum Computing Optimization Challenge.

The Quantum Computing Optimization Challenge is a practical course initiated by Prof. Dr. Claudia Linnhoff-Popien, head of the Chair of Mobile and Distributed Systems at LMU Munich. It took place for the first time in 2020 and has been an integral part of the curriculum ever since. This practical course teaches the ability to model optimization problems for quantum computers, as well as an introduction to practical work with existing quantum computers. Four computers are currently available for this purpose in the QAR Lab: IBM Q System One, Rigetti Aspen-11, Fujitsu DAU, D-Wave Advantage. In cooperation with well-known partners from industry, tasks with strong relevance for practical applications are assigned each semester. The students have the opportunity to execute and compare one task on two computers. In the summer semester 2023, it takes place for the 5th timeIn the QC Optimization Challenge, use cases in areas of optimization, machine learning and simulation are on the agenda.
You can look forward to an exciting presentation in September at the Quantum Summit.

For more information about the Quatum Summit, click here.

 


Kick-off project launch QCHALLenge

Kick-off project launch QCHALLenge

LMU Munich's QAR-Lab is leading the QCHALLenge quantum computing project funded by Bundesministerium für Wirtschaft und Klimaschutz - the companies AQARIOS, BASF SE, BMW AG, SAP SE and Siemens AG will be partners.

(Dec. 01, 2022/Munich, Germany) On December 1st, all project partners met for the first time to advance the common vision to achieve a quantum advantage in the field of production and logistics. With the project QCHALLenge optimization problems especially in these areas should be solved using existing quantum computing (QC) hardware. For this purpose, algorithms, concepts and tools are being developed that will enable the industry to use QC in a multi-sector and low-threshold implementation. The focus will be on the automated integration of QC into existing solutions, the development of generic quantum SDKs and the expansion of know-how in the application and development of QC solutions.

Project partners and their roles:

The project partners are represented by technology experts, software manufacturers and the user industry, in order to optimally combine their know-how from as many different perspectives as possible. In this context, LMU Munich as consortium leader takes the lead of QCHALLenge and contributes its many years of experience in the field of QC software through the Quantum Applications and Research Laboratory (QAR-Lab). Since 2016, the QAR-Lab has already been doing research in the field of quantum computing and working on numerous QC industry and funding projects. Among other things, this has resulted in the middleware UQO for the hardware agnostic use of QC. AQARIOS, founded in 2021 as a spin-off of LMU Munich, especially focuses as a software and technology partner  on the development and implementation of QC solutions. The companies BASF, BMW, SAP and Siemens represent the user perspective in the consortium. They are advancing QC in their business areas and have already been able to build up their know-how through numerous projects in the field of QC. Since QCHALLenge is specifically focused on the domains of production and logistics, this results, besides other topics, in use cases for the optimization of supply chains and warehouses as well as the application of QC in automation.

Goals of QCHALLenge:

QCHALLenge focuses on the integration of QC into existing software workflows. In particular, the project targets the optimization of methods in machine learning and simulation. In doing so, the consortium aims to achieve the following goals at the end of QCHALLenge:

  • the development of generic programming tools and environments,
  • the creation of user-friendly, engineering applications such as optimizations for specific use cases,
  • the development of software solutions for the integration of conventional computers and QC systems (hybrid quantum software),
  • the configuration of strategies and methods for the structured analysis of user-side problems regarding the target-oriented application and development of QC solutions.
Realization and outlook:

To bring QCHALLenge to success, the focus is on four main strategic cornerstones: In the first step, the goal is to identify suitable use cases and to work out a requirements analysis. The focus here is on which use cases are relevant in practice and also bring a possible quantum advantage. Particularly, the comparison to classical baselines will be considered and a prediction about the probability of a quantum advantage will be made. In the second step, general architectures are developed and finalized for integrating various software tools into existing software solutions. The focus here is on the interface definition for existing software solutions. In the third step, the first prototype software tools and hybrid use-case algorithms will be used. In the end, these prototypes can be further developed into a technically mature software tool. The software tools and algorithms are to be developed in such a way that they can be used operationally after the project period and made accessible to medium-sized companies primarily.

Quantum computing is the next technology that promises the potential for disruptive innovation. It offers breakthrough possibilities for solving problems that are unsolvable in practice on classical computers. It is hard to predict the opportunities that quantum computing, and quantum technology as a whole, will provide for humanity in the future. There are numerous fields of application in which it could be used. QCHALLenge starts an exciting project in the field of quantum computing, which all project partners are highly looking forward to. We can’t wait to see where this journey will take us.


5th QC Practical Course Kickoff with BASF, BMW and Siemens

5th QC Practical Course Kick-off with BASF, BMW and Siemens

Kick-off event with BASF, BMW and Siemens: 16 LMU students implement use cases on four quantum computers: IBM Q System Two, Rigetti Aspen-M-2, Fujitsu DAU and the D-Wave Advantage in the QC Optimization Challenge Practical Course.

The practical course will provide the ability to model optimization problems for quantum computers, as well as an introduction to practical work with existing quantum computers. Four computers are currently available in the QAR Lab for this purpose: IBM Q System Two, Rigetti Aspen-M-2, Fujitsu DAU, D-Wave Advantage.
In cooperation with our industry partners BASF, BMW Group and Siemens projects with high relevance for concrete applications are calculated by our students on quantum computers in this semester. With the three use cases (1) Financial Forecasting (BASF), (2) Drive Train Optimization (BMW) and (3) Train Routing (Siemens), the students will explore the main applications of quantum computing: machine learning, simulation and optimization.

We are pleased to make this possible for the 5th time and are looking forward to the results at the end of this semester.

More Information

Page 2 of 2