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Representational separation between unitary and channel quantum generative models via shared classical randomness at shallow depth
Arunava Majumder, Marius Krumm, Hendrik Poulsen Nautrup +1
Near-term quantum hardware limits circuit depth and often imposes geometrically local connectivity for quantum generative models, restricting the output distributions accessible to…
Interpreting Quantum Learning Models via Stochastic Processes
Johannes Fankhauser, Lukas J. Fiderer, Hans J. Briegel
Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement. While such models can exhibit computational advantages,…
Discovering quantum phenomena with Interpretable Machine Learning
Paulin de Schoulepnikoff, Hendrik Poulsen Nautrup, Hans J. Briegel +1
Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent advances in variational autoen…
Minimizing classical resources in variational measurement-based quantum computation for generative modeling
Arunava Majumder, Hendrik Poulsen Nautrup, Hans J. Briegel
Measurement-based quantum computation (MBQC) is a framework for quantum information processing in which a computational task is carried out through one-qubit measurements on a high…
Efficient Preparation of Resource States for Hamiltonian Simulation and Universal Quantum Computation
Thierry N. Kaldenbach, Isaac D. Smith, Hendrik Poulsen Nautrup +2
The direct compilation of algorithm-specific graph states in measurement-based quantum computation (MBQC) can lead to resource reductions in terms of circuit depth, entangling gate…
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
Paulin de Schoulepnikoff, Gorka Muñoz-Gil, Hendrik Poulsen Nautrup +1
Interpretable machine learning is rapidly becoming a crucial tool for scientific discovery. Among existing approaches, variational autoencoders (VAEs) have shown promise in extract…