Focus Industrial Applications
On the application side the QAPPS consortium is focused on the collaboration with industrial partners. We analyze problem sets and application scenarios of our partners and develop together pilot solutions using state of the art quantum machines of our technology partners. In R&D projects application scenarios are formalized, algorithms to approach them are implemented and evaluated.
QAPPS is dedicated to prepare Saxony as a location for business and industry towards the future application of the quantum computing technology in innovative products and services.
Focus Foundations and Education
On the research and education side the QAPPS consortium is focused on scientific questions in the field of quantum computation and quantum information and its future directions. Besides research work on the foundations of the field, we build up a knowledge base about the current techniques and algorithms to provide education and training at universities and for interested partners.
QAPPS aims to prepare specialists for the upcoming quantum revolution by providing sustainable education in the field and establish Saxony as leading center for quantum computing.
The idea of quantum computing dates back several decades, but after theoretical developments in the field, with today’s Noisy Intermediate-Scale Quantum Devices there are for the first time practicably applicable machines available. Besides the availability of quantum computing hardware, various initiatives have software frameworks under development to bring the technology as a new type of general purpose computer with extended capabilities compared to classical computers in practical application. Within the QAPPS consortium promising fields of application are identified and showcased in pilot applications to illustrate the potential of the new computing paradigm. Promising application fields are identified, quantum algorithmic solutions are implemented, tested and evaluated.
The QAPPS initiative identified particularly three practically highly relevant application fields of quantum computing, that have the potential for quantum supremancy: Simulation, Machine Learning and Optimization.
The idea of quantum computing originates from ideas of Paul Benioff, Yuri Manin and Richard Feynman with the central idea of simulating quantum mechanical systems efficiently.
Today there are hybrid quantum-classical algorithms for the simulation of quantum systems available and can be applied to the simulation of molecules, the development of drugs, nano-technology or new materials
Machine Learning is inherently probabilistic and tries to automatically derive knowledge from data. Quantum computing as paradigm is probabilitic at its core as well and it turns out that this similarity can be utilized in quantum machine learning algorithms (QML) that are potentially more powerful and efficient than classical algorithms. QML has the potential to revolutionize applications in various fields such as production systems, energy applications, logistics and medicine
Optimization tasks and in particular combinatorial optimization problems are ubiquitous in science and technology. Most of those problems are NP-complete and hence only approximate solutions can be found in reasonable runtime. Quantum computing introduces new approaches for various optimization tasks which utilize the quantum resources superpositon and entanglement to improve the efficiency and runtime behavior for difficult to solve optimization problems.
Fraunhofer and the QAPPS consortium collaborate exclusively with IBM as hardware and technology partner. Together with our industrial partners we have immediate access to the latest and most powerful available quantum devices. As subcontractor those devices are also available to our research and development as well as industrial partners. The field develops very dynamically. In Ehningen, Baden-Württemberg, a 27-qubit machine is available, which can be used by Fraunhofer and its partners exclusively.
IBM aims to improve the performance of ist devices according tot he road map below. It shows, e.g., how the number of Qubits is planned to increase towards 1121 qubits in 2023. At the same time also the so called quantum volume of the devices will increase, which is widely seen as a measure for the computational power of a quantum computer. While the initial plan was to double the quantum volume every year for at least the next 10 years, the current planning seems to be even much more ambitious.
Not visible so well in the grapics is the connection architecture of the qubits within the quantum processor. Here a heavy-hexagonal structure has been chosen to be able to implement the heavy-hexagonal error correcting code later on. In the roadmap it is also visible that the current planning is to stack several 1121 qubit structures starting in 2024 on the journey towards the one million qubit goal. This value is seen as a realm in which error correction is working well, since 1000 qubits are a value which is currently considered for the number of physical qubits necessary to produce one stable error correcting qubit. In this sense a one million (physical) qubits quantum computer could offer about 1000 error corrected stable qubits. This is widely considered as a roboust machine for a different class of quantum algorithms – beyond the current NISQ era (noisy intermediate-scale quantum) of quantum computing.
Beyond the hardware level, the roadmap also informs about the development at kernel level and algorithm level. The works with quantum circuits will become more flexible and more general. Beyond the different available application modules, the connection between quantum computing and classical HPC will form another form of hybrid approach of quantum-classical computation and the application modules will be available in modelling environments for better applicability.
Besides exclusive collaboration with IBM, QAPPS consortium also works together with industrial partners using other quantum devices, e.g. quantum annealers – DWave or Fujitsu Digital Annealer, quantum cloud services such as Amazon Braket or Azure Quantum and hence devices of e.g. Rigetti or IonQ.
The QAPPS initiative aims to bring the Quantum Computing Technology into demonstrators, pilot solutions and finally productive practical applications.
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Consider our case studies to see quantum computing in action! Please contact us to get into conversation how quantum computing can transform your business.
Quantum computing is not a future but a current technology. Although the current NISQ devices are still error prone and of limited capacity, the power of the new technology can be practically demonstrated.
The QAPPS consortium offers showcases in the three major application fields Simulation, Machine Learning and Optimization:
Simulations: Demonstration of innovative quantum algorithms for finite element methods and hybrid quantum-classical algorithms such as the variational quantum eigensolver to simulate physical systems.
Machine Learning: Demonstration of Quantum Neural Networks so small data sets with the potential of exponential speed-ups compared to classical neural network implementations.
Optimization: Demonstration of hybrid quantum-classical algorithms such as the Quantum Approximate Optimization Algorithm to NP-complete combinatorial optimization problems.
QAPPS is a collaboration of universities to address foundational research questions and educational goals and different Fraunhofer insituts to bring the innovation in application together with industrial partners.
Besides the core partners QAPPS is associated with a broad network of institutions with specific focus and collaborators with various interests to approach the field of quantum computing from different angles.