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This framework demonstrates that a single qubit can effectively simulate complex driven many-body quantum systems, significantly reducing computational costs compared to traditional simulations. By leveraging dissipative quantum reservoir computing, the approach learns input-output maps directly from data rather than relying on detailed microscopic models. While current results are promising for nonlinear phenomena like High-Harmonic Generation, the method's scalability to larger multi-qubit scenarios remains an open question for future research.
Single Qubit Reproduces Complex Quantum Behaviour with New Computing Framework

Digital twin modelling has moved beyond computationally expensive simulations with the creation of a framework utilising just one qubit. This minimal system matches and surpasses temporal convolutional and Kolmogorov, Arnold network models on key benchmarks while demanding far fewer resources. The approach learns complex responses directly from data, establishing a new avenue toward simulating driven many-body quantum systems.
Driven many-body quantum systems present challenges when modelling their response to input data due to nonlinearities and complex simulations that grow with system size. A dissipative quantum reservoir computing framework now creates digital twins capable of learning an input-output map directly from data using only a small quantum system as its “reservoir”. A minimal single-qubit reservoir successfully reproduced the High-Harmonic Generation response originating from a larger Ising…
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