Programme

In this talk Enrique Solano will describe digital, analog, and digital-analog quantum computing paradigms. Furthermore, he will discuss the possibility of reaching quantum advantage for industry use cases with current quantum computers in trapped ions, superconducting circuits, neutral atoms, and photonic systems.

While there’s a lot of research and development ongoing in the field of quantum computing in general, most of the hardware so far is big, heavy, energy consuming and bound to a specific location. Mobile systems would be of great interest especially to the defense and security sector, as they wouldn’t require a connection to a local data centre. Especially in a crisis situation or a state of defense, the ability for quick relocation is essential. But small, mobile systems would also provide great benefits for other use cases. The devices could e.g. be integrated into vehicles or drones to implement highly complex image recognition algorithms. The german Cyberagentur is currently financing a research project on mobile quantum computers, which aims on significantly reducing size, weight and energy consumption. At the same time, the performance has to be strongly improved. The goal is a system, which is both mobile and powerful. The presentation will give an introduction to the idea itself, to possible use cases for mobile quantum computers, and first insights into the ongoing project.

Excitement about the promise of quantum computers is tempered by the reality that the hardware remains exceptionally fragile and error-prone, forming a bottleneck in the development of novel applications. In this talk we show how quantum control delivered by software could accelerate the adoption of quantum technologies by improving the performance of commercial quantum computers. We will illustrate how our automated performance management software solution improves the execution of a suite of algorithms, including hybrid algorithms with direct application to high-value logistic optimization problems.

Considering the full value chain of quantum computing from the perspective of end users of that technology the presentation argues that software (potentially also including closely related professions such as consulting and integration) will, in the long term, be the dominant value driver. Following this hypothesis, we will further put forward the idea that software in the quantum computing age will bifurcate or split into two feedback cycles: One tightly coupled to actual quantum systems and the other one linking the classical enterprise computing domain with the quantum domain. This categorization will significantly support (software) enterprises in developing their respective strategy vis-á-vis quantum computing.

The Federal government sees great potential in quantum technologies for the German economy, with the main economic impact expected through application of quantum computing.

 According to a recent study, the value creation from quantum computing in Germany could reach up to 53 billion euro annually by 2050, mainly driven by software and application. 

The groundwork to achieve these levels has to be laid now by developing the full software stack all the way through to the final application. 

I will detail how the BMWK's unit VIB3 "Development of Digital Technologies" supports the goals of the Action Plan on Quantum Technologies to make quantum computing accessible for German companies, especially SMEs.

Quantum Machine Learning (QML) is an emerging field with numerous potential applications. Due to the rapid development of the field, new security risks as well as security opportunities emerge hat aren't found in classical machine learning systems. We provide a survey of security topics relevant to QML and discuss them based on recent literature as well as original proposals. We focus on newly emerging security issues that are based on the quantum paradigm, namely the robustness of quantum data encodings to adversarial perturbations, using quantum noise as a new attack vector on QML systems and finally discuss the hypothesis that inherently present quantum noise may make QML systems more robust to adversarial attacks as well as offering some hypotheses to the effect of rapidly growing Hilbert spaces on security. We present recent theoretical and practical findings and offer original proposals for future research.

The advantage and the need of ecosystems when it comes to developing new technologies is the conviction of QAI Ventures. With the instruments of investing in and accelerating Quantum Startups QAI Ventures builds up a global ecosystem out of Switzerland. Egosystem vs Ecosystems, needs of Quantum Startups vs non-Quantum Startups, requirements and mindsets for building an ecosystem will be adressed.

Quantum computing has long been an object of fundamental research. However, recent advancements in quantum technology have given rise to a quantum industry, thereby raising expectations for practical applications. This panel focuses on the interaction of fundamental research and industry in this dynamic environment. 

The quantum researchers Paul K. Faehrmann and Johannes Jakob Meyer view the hype surrounding quantum computing critically. Quantum computing is a highly technical and subtle topic, which increases the risk for exaggerated expectations and overinflated performance claims. It is therefore vital to establish realistic expectations and avoid overinflated claims to guarantee a sustainable development of quantum technologies. 

Regina Kirschner, product owner for quantum technologies at Porsche Digital, outlines how Porsche Digital navigates this challenging domain on the industry side. In order to effectively handle quantum computing, Porsche Digital believes it is necessary to develop in-house capabilities and a competence for the proper assessment of quantum technology proposals. This entails building expertise in close proximity to research, such as through the inclusion of in-house PhDs or participation in research projects.

In the BMWK funded project QCHALLenge, comprised of the partners BASF, BMW, SAP, Siemens, and QAR-Lab at LMU Munich, we explore many industrially relevant use cases in the context of Quantum Computing and search for possible Quantum Advantages. While focusing on the domains of logistics and production line management, all of the three main application areas of QC get examined: Optimization, Machine Learning and Simulation. Driving the knowledge transfer between academia and industry, this project cooperates with the "QC-Optimization Challenge", a practical c ourse for students at LMU Munich, in which Quantum Computing is employed to solve given industry problems on state-of-the-art quantum computers. In this talk, we present the latest results on these proofs-of-concept implementations and provide an outlook on the integration and deployment of the developed solutions into industrial environments.

The rise in renewable power generation leads invariably to increased volatility in power grids, requiring more detailed modeling of grid constraints and more frequent solving of forecasted overloads while minimizing societal costs of required redispatching actions.

This presents a business-critical dynamic optimization challenge for transmission system operators (TSO) like Elia/50Hertz (Elia Group), a Belgian-German TSO.

Deloitte launched its yearly Quantum Climate Challenge in 2022 to foster the practical implementation of climate-relevant quantum use cases together with industry partners. In the 2023 installment of the challenge, participants explore quantum computing for materials research. Specifically, binding characteristics and selectivity of metal-organic frameworks are investigated that are used to filter carbon dioxide out of the atmosphere and thus may help to reach the 2°C goal.

The quantum challenge concept is an open crowd innovation approach, enabling quantum developers from academia, research, industry, and startups to tackle the given problem in an assisted way by providing real-life data, guideposts, and access to state-of-the-art quantum hardware and software.

The developed solutions and their practical implementations are assessed by an expert jury both of the quantum domain and the addressed industry sector. In 2023, juror affiliations include IBM, Intel, Evonik, Verband der Chemischen Industrie, BASF Chemovator, Boehringer Ingelheim, and Quantistry.

In this session, Dr. Barbara Wellmann (Lead Quantum.Link at Deloitte Consulting) together with the winning team of the challenge present the solution achieved. A particular focus will be on the practical implementations together with the involved industry partners and further application. In addition to the specific practical outcomes of this year’s challenge, Dr. Wellmann will also talk on how to unlock innovation in the quantum space and foster industry collaboration. Together with the challenge winner, we will conclude the session with an outlook on raising quantum chemistry approaches that will advance nowadays solutions further with increased quantum hardware capabilities.

Key takeaways from the session are both practical insights on how to effectively integrate and implement quantum simulation in material research and best practices on ecosystem collaboration and industry involvement in cross-business innovation.

The use of quantum processing units (QPUs) promises speedups for solving computational problems, but the quantum devices currently available possess only a very limited number of qubits and suffer from considerable imperfections. To step towards practical utility, one approach is to apply hardware-software co-design methods. This can involve tailoring problem formulations and algorithms to the quantum computing environment, but also entails the possibility of adapting physical properties of the QPU to specific applications. We investigate how key figures such as circuit depth, gate count and runtime vary with tailored hardware properties when solving different NP-complete problems relevant for industrial applications using the Quantum Approximate Optimization Algorithm. Our results reveal that achieving near-optimal performance and properties does not necessarily require optimal quantum hardware but can be satisfied with simpler structures that can potentially be realized for many hardware approaches.

Pre-production vehicle testing can come at a substantial cost to manufacturers. The goal is to perform a maximum number of tests on a given number of vehicles, while incorporating various technical, legal and organisational constraints. The problem of configuring a given set of vehicles such that all required and as many optional tests as possible can be performed leads to a complex large sca le optimisation task. We evaluated two different approaches to solving the complete problem including the assignment of tests to vehicles and the scheduling of the tests within the scope of the BMW Quantum Challenge. 

The first approach is an implementation for fault tolerant quantum computing devices where we apply a combination of the Dürr–Høyer algorithm for Quantum Minimum Finding and Quantum Amplitude Amplification. 

The second approach is a native-hybrid local optimization routine utilizing a fast classical or quantum MAX-SAT solver as a subsolver such as D-Wave or the NTT Coherent Ising Machine – a optical quantum annealing device operating at room temperature. The approach was implemented using a classical MAX-SAT solver and benchmarked on an industrial-size dataset. We observe a significant improvement in solution times while retaining acceptable solution quality. 

We report on our working setup which involved a team comprised of researchers from 1QBit, NTT DATA, NTT Research, and the University of Notre Dame working together with experts form BMW as well as the results we achieved and the lessons learned in applying quantum computing to explicit industry problems.

We will demonstrate an easy and fast way to implement, deploy, test and monetize quantum applications using the PlanQK platform. Additionally, we will go through a couple of use cases of quantum computers and will demonstrate a selected use case that covers credit card fraud detection.

In train operating systems, incidents, i.e., technical failures or deployment of rescue teams, occur regularly and lead to delays. These delays might result in a lot of conflicts with the regular timetable or other delayed trains. Today delay and conflict handling is solved by a local approach by area managers with computational support. Thereby a country, i.e., Germany is split up in small zones and each manager is just looking at its own area and hands over delays at the area borders to the next manager, without knowing, if this may lead to conflicts in the next area. This leads to non-optimal solutions and an increasing overall delay time. Quantum and Quantum-Inspired Computing offers the possibility, to solve these conflicts globally – for a whole country for example while minimizing the overall delay. To achieve this, we worked together with our customer on a simulation and optimizing model using QUBOs and the Digital Annealer. As parts of the project, we simulated a rail network with several stations and parallel tracks, injected a regular timetable and delays, generated alternative for the trains and solved the combinatorial optimization problem, when and on which tracks, the trains must ride. To solve the combinatorial problem, we developed a QUBO model which selects from upfront generated alternative conflict avoiding rides. The optimization targets were defined as “no remaining conflicts” and a minimized overall delay time for all trains. In our solution we showed that a global alternative generation with a global optimization compared to a progressive resolution approach as a local conflict solver is able to halve the overall delay times of the delayed trains.
We’ll present the use case together with our customer and outline the value of this solution in a productive environment.

Modern-day transportation and logistics happen in increasingly smart environments. New data sources hold both an opportunity and a challenge. Vehicle routes need to be planned efficiently to exploit the new potential. For example, w aste management in large cities traditionally does not inhibit overflowing containers. Trucks follow a fixed route independent of the filling level which leads to inefficient routes. Containers can be equipped with sensors that provide real-time information on the filling level.

Optimal routes can then be computed dynamically to only visit full containers and utilize the truck’s capacity. Mathematically speaking, route planning for waste collection translates to a Capacitated Vehicle Routing Problem (CVRP). The CVRP is NP-hard, thus industry-sized problems with hundreds to thousands of nodes can only be solved heuristically. Quantum computing promises the potential to outperform classical solvers for several computational tasks. However, its practical application to industry-relevant optimization problems poses challenges such as finding good mathematical formulations, setting parameters of variational algorithms or measuring the solution quality. For the CVRP, we assess the viability of formulation, decomposition, and algorithmic options for noisy intermediate-scale quantum hardware with regards to problem-specific performance measures. Obtaining useful results with the Quantum Approximate Optimization Algorithm and its variants proves to be difficult even after reducing the CVRP to a set of Travelling Salesperson Problems, but the Variational Quantum Eigensolver shows more promise.

This work is part of the QuaST project funded by the Federal Ministry for Economic Affairs and Climate Action. QuaST aims to facilitate the access to quantum-based solutions for optimization problems and to bridge the gap between business and technology. Our work contributes to understanding the challenges therein for the CVRP.

We present Fujitsu's approach and current activities involving the whole quantum computing technology stack. In particular, we focus on the recent developments of a quantum gate computing chip based on superconducting transmon qubits which is done in collaboration with the RIKEN Center for Quantum Computing.

We present Fujitsu's approach and current activities involving the whole quantum computing technology stack. In particular, we focus on the recent developments of a quantum gate computing chip based on superconducting transmon qubits which is done in collaboration with the RIKEN Center for Quantum Computing.

"Using qubits instead of bits, quantum computers can solve problems that are intractable for even the most powerful supercomputers. Thus, they can address complex demands across industries that were inaccessible before.

Healthcare is one of the most important sectors, taking 10% and more of the GDP of developed countries.

In healthcare, quantum breaks new ground. It opens the door to a personalized medicine with better prevention and more precise treatments, e.g., in oncology where cancer patients can produce up to 100 terabytes of individual data, and a therapy plan requires processing thousands of variables. It can bring early detection of rare and life-threatening diseases, and transform healthcare from reactive to more predictive. Quantum will rapidly accelerate drug discovery and development. But it also raises concerns about cybersecurity, putting personal health information and sensitive data at risk.

In this presentation a review of the current state of quantum computing is given to figure out use cases for healthcare and beyond, and discuss practical, legal, scientific and other aspects of the technology, and how it will influence our industries in the next few years."

What is standardization and how does it work? Can I participate or do I have to wait until others or even the big players impose their standards on me? What kind of committees already exist in quantum technologies? And the most important question of all: Why does this actually make sense?

What is standardization and how does it work? Can I participate or do I have to wait until others or even the big players impose their standards on me? What kind of committees already exist in quantum technologies? And the most important question of all: Why does this actually make sense?

Full stack, on-premise, fully scalable quantum computing is available for research, education and industry already today and made in Europe.

In this talk, I will share the conclusions and insights from our investment strategy development process and share our analysis-based expectation that the quantum computing industry will significantly develop in the future: why we expect an excess growth in this industry, what problems would it solve and where are the opportunities for cooperation.

During this talk, I will discuss how easy it is to create applications for quantum computers with the Classiq platform. The Classiq platform will lower the requirements for companies to leverage quantum computing and will enable more people to leverage the future power of quantum computing technology

Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances in quantum computing toward practical applications, the need to analyze and compare different quantum solutions increases. While low-level benchmarks for QC exist, end-users of quantum solutions are primarily interested in real-world application-level performance. We propose an application-centric benchmark method and th e QUantum computing Application benchmaRK (QUARK) framework to foster the investigation and creation of application benchmarks for QC. QUARK is an open-source framework for designing, implementing, executing, and analyzing QC benchmarks available for users today. In this presentation, we introduce its concept and core elements, demonstrate how to use it for application-based benchmarking, and give an outlook on future development.

Transport phenomena remains nowadays still the most challenging unsolved problem in computational physics, though the high-performance computing has been applied. As future technology, quantum computing opens a grand new perspective for numerical simulations of transport phenomena. Taking computational fluid dynamics as a concrete application, the possible quantum algorithms and software are reviewed. The recent work of our research group using Lattice-Boltzmann Method (LBM) with quantum simulator software to solve the advection-diffusion equation will be presented namely how an algorithm on classical computer could be translated and implemented on a quantum computer.