Our Work

In the Quantum Technology and Application Consortium (QUTAC), we, as leading companies in the German economy, want to raise quantum computing to the level of practical application and thus actively shape a new digital future together.

The Way We Work

As leading companies within the German and European economy, our aim at QUTAC is to develop quantum computing to industrial application. At the same time, we strive to contribute to the development of a sovereign European quantum ecosystem. Through our working groups, we further these goals — be it through political communication, conducting the necessary fundamental research, or by developing specific quantum computing use cases. In doing so, we provide important insights to the entire quantum community, from academia and startups to business and industry.

Our Working Groups

  • Material Science

    Complex molecules – complex simulations 

    Be it new drugs, cathode materials, or fertilizers: the simulation of complex molecules is of great importance to our industry. However, even today’s supercomputers do not offer the computing power to perform the necessary calculations at an acceptable speed and accuracy. They can only provide us with approximate solutions, which are sufficient for certain simple molecules, but unsuitable for complex structures.  

    Quantum computers offer the potential to simulate even large molecules accurately. They enable us to better understand and predict their behavior.

    What we do

    The Material Science working group investigates the performance of relevant quantum algorithms. We demonstrate their actual potential and identify their limitations.  

    With our findings, we provide relevant starting points for further basic research. Companies and startups that want to make the simulation of complex molecules a reality using quantum computing can build on our work.

  • Quantum Machine Learning & Optimization

    The next step in process optimization

    We have high expectations for how quantum computing will strengthen our economy and industry. 

    On the one hand, its computing power will open up new possibilities that were previously unimaginable with classical computers. Our industry faces many optimization problems that push today’s supercomputers to their limits, such as controlling supply chains or complex manufacturing processes.  

    On the other hand, we are counting on quantum computing to be productively linked with other future technologies, such as generative artificial intelligence. But how far does the potential of quantum technologies in this area really go? And where are its limits?

    What we do

    Our Quantum Machine Learning & Optimization working group is dedicated to addressing precisely these questions. We are investigating how quantum computers can help solve complex optimization problems such as the knapsack problem, but also how they can help overcome existing challenges in the field of AI, such as the need for large amounts of training data or high computing power.

  • Quantum Systems

    The race for the optimal quantum hardware is wide open

    In developing powerful quantum computers, researchers around the world are testing a wide range of possible hardware concepts. These are based on various physical systems, such as superconductors, ion traps, neutral atoms, photons, or spins in semiconductors. 

    Which technology will prevail remains to be seen. Especially in the current early phase of the quantum era, different types of hardware may be more or less suitable for solving specific problem classes. Companies that want to use quantum computing should be able to assess these differences. For them, the following questions are paramount: When will quantum computers be able to execute economically relevant applications faster than classical computers? What applications will these be? And which hardware is best suited for specific use cases?

    What we do

    Our Quantum Systems working group investigates these hardware variants from a clear end-user perspective. We focus on individual technological approaches and examine how their properties affect the effectiveness of various algorithms. We focus on physical qubit architectures and their influence on parameters such as qubit stability, the reliability of computing operations, and computing speed. In this way, we show how the respective hardware influences the time required to solve tasks and the quality of the results, and how likely it is that it will be possible to perform fault-tolerant quantum computations with it in the future.

  • Political Communication

    For a sovereign quantum ecosystem

    Quantum technologies will have to become an essential element of Germany’s digital strategy. Important industries see them as a future key strategic technology with great economic potential. Germany has a high level of scientific expertise in quantum technologies. At the same time, transferring this expertise to the economy remains challenging. Germany and Europe must invest in their technological sovereignty today to secure their economic competitiveness tomorrow. Theoretical knowledge must be translated into industrial applications. 

    This requires strategically coordinated political support. A strong commitment creates planning security for our industry and enables the necessary agile action.

    What we do

    As the Quantum Technology & Application Consortium and an association of several of the largest companies in Germany and Europe, we are committed to providing political support for the German quantum technology ecosystem. To this end, we participate in public events such as trade fairs, symposia, and panel discussions, engage in direct dialogue with political decision-makers, and use our publications to raise awareness and provide the informational basis necessary for constructive political discourse.

Publications

Material Science

Generating approximate ground states of strongly correlated quantum many-body systems through quantum imaginary time evolution

Most quantum algorithms designed to generate or probe properties of the ground state of a quantum many-body system require as input an initial state with a large overlap with the desired ground state. One approach for preparing such a ground state is Imaginary Time Evolution (ITE). Recent work by [Motta, M. , Sun, C. , Tan, A.T.K. et al (2020)] introduced an algorithm—which we will refer to as Quantum Imaginary Time Evolution (QITE)—that shows how ITE can be approximated by a sequence of unitary operators, making QITE potentially implementable on early fault-tolerant quantum computers. In this work, we provide a heuristic study of the capabilities of the QITE algorithm in approximating the ITE of lattice and molecular electronic structure Hamiltonians. We numerically study the performance of the QITE algorithm when provided with a good classical initial state for a large class of systems, some of which are of interest to industrial applications, and check if QITE is able to qualitatively replicate the ITE behavior and improve over a classical mean-field solution. The systems we consider in this work range from one- and two-dimensional lattice systems of various lattice geometries displaying short- and long-range interactions, to active spaces of molecular electronic structure Hamiltonians. In addition to the comparison of QITE and ITE, we explicitly show how imaginary time evolved fermionic Gaussian states can serve as initial states which can be efficiently computed on classical computers and efficiently implemented on quantum computers for generic spin Hamiltonians in arbitrary lattice geometries and dimensions, which can be of independent interest.

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Quantum Machine Learning & Optimization

TabularQGAN: A Quantum Generative Model for Tabular Data

In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real-world data is scarce or private, it can be used to augment or replace existing datasets. Real-world enterprise data is predominantly tabular and heterogeneous, often comprising a mixture of categorical and numerical features, making it highly relevant across various industries such as healthcare, finance, and software. We propose a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz to effectively model tabular data. The proposed approach is tested on the MIMIC III healthcare and Adult Census datasets, with extensive benchmarking against leading classical models, CTGAN, and CopulaGAN. Experimental results demonstrate that our quantum model outperforms classical models by an average of 8.5% with respect to an overall similarity score from SDMetrics, while using only 0.072% of the parameters of the classical models. Additionally, we evaluate the generalization capabilities of the models using two custom-designed metrics that demonstrate the ability of the proposed quantum model to generate useful and novel samples. To our knowledge, this is one of the first demonstrations of a successful quantum generative model for handling tabular data, indicating that this task could be well-suited to quantum computers.

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Quantum Machine Learning & Optimization

Generative-enhanced optimization for knapsack problems: an industry-relevant study

Optimization is a crucial task in various industries such as logistics, aviation, manufacturing, chemical, pharmaceutical, and insurance, where finding the best solution to a problem can result in significant cost savings and increased efficiency. Tensor networks (TNs) have gained prominence in recent years in modeling classical systems with quantum-inspired approaches. More recently, TN generative-enhanced optimization (TN-GEO) has been proposed as a strategy which uses generative modeling to efficiently sample valid solutions with respect to certain constraints of optimization problems. Moreover, it has been shown that symmetric TNs (STNs) can encode certain constraints of optimization problems, thus aiding in their solution process. In this work, we investigate the applicability of TN- and STN-GEO to an industry relevant problem class, a multi-knapsack problem, in which each object must be assigned to an available knapsack. We detail a prescription for practitioners to use the TN-and STN-GEO methodology and study its scaling behavior and dependence on its hyper-parameters. We benchmark 60 different problem instances and find that TN-GEO and STN-GEO produce results of similar quality to simulated annealing.

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The road towards industrial application

QUTAC focuses on industrial applications. The positive outlook for quantum computing has to substantiated, its early practical application to be prepared. In a first step, we will thus identify all possible applications and their potential for industrial realization. Through this, we intend to harmonize expectations towards a successful quantum ecosystem with its necessary investments. To achieve this, QUTAC has set the following goals in its position paper:

  1. Showcasing the demands for quantum computing within German industry.
  2. Creation of a basis for a cross-industry application portfolio
  3. Joint realization of referential applications and call to cooperation beyond the boundaries of the consortium

Together, we intend to lead Germany to digital sovereignty in quantum computing and thus also move forward Europa as a business location.

The position paper has been published by SpringerOpen. The short version is available for download here.

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Quantum Computing Use Cases

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Merck: Optimizing how Clinical Trials are Planned and Carried Out 

Clinical trials are an important step in getting new pharmaceuticals to market, but they are complex and rely on multiple interdependent factors. This makes it hard to plan them optimally. Attempts to optimize trial designs with conventional machine learning are hitting their limits. That’s why science and technology company Merck is testing the use of Bayesian networks and quantum computers - and reaching out to other experts in QUTAC.

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