(Berlin, 03/04.12.2024) Funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK), the initiative “Quantum Computing – Applications for Industry” supports various projects and evaluates the program’s scientific impact. Over two days, the workshop at the Forum Digitale Technologien in Berlin gathered more than 70 participants from research and industry to discuss progress and future challenges.

Key projects presented include:

• QUASIM: Simulating rotor blade milling for aircraft engines, with the potential to reduce waste by 80% using quantum computing.
• QuaST: A decision-tree tool for optimizing problem-solving across quantum and classical computing, aiding industries like semiconductor manufacturing.
• Qompiler: A new programming language, Qrisp, designed to simplify software development for quantum computers.

Additional contributions came from:

• The Quantum Computing User Network (QuCUN), funded by the Federal Ministry of Education and Research.
• The QUA-SAR project of the DLR Quantum Computing Initiative.
• Bench-QC from Munich Quantum Valley, focusing on application-driven benchmarking.
• The Bundesdruckerei Group.

This collaborative effort showcased innovative tools and strategies to make quantum computing more accessible and impactful for businesses.

(Berlin, October 7, 2024) Under the motto “Transformation through innovation – sustainable, sovereign, networked”, the conference of the Federal Ministry of Economics and Climate Protection (BMWK) – Tage der digitalen technologien 2024 (Days of digital Technologies 2024) – took place in Berlin on October 7 and 8. Representing the QCHALLenge consortium, members of Siemens, SAP and LMU were present with a exhibition stall and informed conference visitors about the consortium’s progress and ideas. Goals and use cases of quantum computing from industry and science were presented to interested visitors as part of two larger guided tours and several individual pitches.

(Berlin, 25.9 2024) At this year’s Bitkom event in Berlin (24th – 26th of September 2024), Dr. Abhishek Awasthi (BASF) and Florian Krellner (SAP) presented progress and insights gained on one of the central QCHALLenge use case problems, Production Assignment. The problem involves the planning and balancing of orders on various industrial production machines with a non-linear objective function. Various modeling techniques for classical and quantum computing were developed and tested for QCHALLenge.
In their presentation Case-studies on hybrid quantum solvers for business relevant optimization use-cases, approaches and details from the following related publication were presented.

Real World Application of Quantum-Classical Optimization for Production Scheduling
Abhishek Awasthi, Nico Kraus, Florian Krellner, David Zambrano (arxiv.org/abs/2408.01641)

(October 10, 2024/Garching) The QCHALLenge consortium was widely represented at the 5th Quantum Computing and Engineering Conference (QCE 2024, 15.09 – 20.09 in Montreal, Canada) and was able to present several publications. With the participation of BASF, Siemens, SAP, LMU and Aqarios, many interesting discussions with industry and research were held and valuable contacts were made. A tutorial by Jonas Stein (LMU) and Sebastian Feld (TU Delft) on simulation-based optimization-problems was also very well received. We are pleased to report on the following publications, among others, which will appear in the proceedings of the IEEE International Conference on Quantum Computing and Engineering in Sep. 2024:

• A.A. Awasthi, N. Kraus, F. Krellner, D. Zambrano, „Real World Application of Quantum-Classical Optimization for Production Scheduling”.
• C. Jones, N. Kraus, P. Bhardwaj, M. Adler, M. Schrödl-Baumann and D. Zambrano Manrique, “Benchmarking Quantum Models for Time-series Forecasting”.
• M. Zorn, J. Stein, P. Altmann, M. Kölle, C. Linnhoff-Popien and T. Gabor, “Cohesive Quantum Circuit Layer Construction with Reinforcement Learning”.
• M. Kölle, D. Seidl, M. Zorn, P. Altmann, J. Stein and T. Gabor, “Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning”. [arXiv:2408.01187.]
• J. Stein, J. Blenninger, D. Bucher, J. P. Eder, E. Çetiner, M. Zorn and C. Linnhoff-Popien, “CUAOA: A Novel CUDA-Accelerated Simulation Framework for the QAOA”. [arXiv:2407.13012.]
• T. Rohe, D. Schuman, J. Nüßlein, L. Sünkel, J. Stein and C. Linnhoff-Popien, “The Questionable Influence of Entanglement in Quantum Optimisation Algorithms”. [arXiv:2407.17204.]

(June 11, 2024/Garching) For our annual meetup more than twenty members of the consortium accompanied by the DLR got together for a full day workshop at the new Siemens campus in Garching near Munich to update each other on the current status of the project. Starting with internal kickoffs of work complexes 8 and 9, and continuing with an on premise tour of the LRZ Quantum Integration Center to get a feeling of qauntum hardware, the official part started after lunchtime and covered in-depth updates of the major work packages, including use cases, the software framework as well as fundamental reasearch on QML and decomposition techniques for quantum optimization.
After intensive technical discussions the workshop concluded in a positive outlook for the success of the project from all parties involved.

(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.

(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.

(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.

(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.

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.