Neuromorphic Quantum Computing for Prediction and Optimization (NeMoQC)
Christoph Räth, Joel Steinegger, Fabian Fischbach
The application of AI-based forecasting methods has led to great progress in the prediction of complex systems. Among the AI-methods being used reservoir computing (RC) turns out to be so far the most promising approach as it combines superior prediction results with little CPU-needs for training. In Quantum Reservoir Computing (QRC) the reservoir that is commonly a random network, is replaced by a system of entangled qubits. Using a spin-network as ad hoc quantum reservoir it was demonstrated that a NARMA process can be well predicted using as few as 10 qubits [1]. Our recent first results suggest that even a 4-6 qubit system may be sufficient to predict the Lorenz-system as well as with “conventional” RC [2]. In the forthcoming research project “Neuromorphic Quantum Computing (NeMoQC)” within the framework of the DLR Quantum Computing Initiative (QCI) we will now investigate systematically which quantum reservoirs are best suited for prediction and optimization tasks. In a software/hardware codesign we will seek for hardware realizations of QRC. Further, these newly developed QC-based forecasting and optimization methods are to be utilized in real world applications. Industrial partners for both the hardware design and the applications in real world use cases are welcome to join our project. [1] R. Martinez-Pena et al., PRL, 127, 100502 (2021) [2] J. Steinegger, Masterthesis LMU (ongoing)
•Jan 1, 2023•0 citations