6 papers
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…
Disentanglement by means of action-induced representations
Gorka Muñoz-Gil, Hendrik Poulsen Nautrup, Arunava Majumder +4
Learning interpretable representations with variational autoencoders (VAEs) is a major goal of representation learning. The main challenge lies in obtaining disentangled representa…
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias
Philip A. LeMaitre, Marius Krumm, Hans J. Briegel
With the impressive progress of deep learning, applications relying on machine learning are increasingly being integrated into daily life. However, most deep learning models have a…
A Universal Quantum Computer From Relativistic Motion
Philip A. LeMaitre, T. Rick Perche, Marius Krumm +1
We present an explicit construction of a relativistic quantum computing architecture using a variational quantum circuit approach that is shown to allow for universal quantum compu…
Variational measurement-based quantum computation for generative modeling
Arunava Majumder, Marius Krumm, Tina Radkohl +4
Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms. Indeed, due to the inherent randomness of quantum measurements, th…
Free Energy Projective Simulation (FEPS): Active inference with interpretability
Joséphine Pazem, Marius Krumm, Alexander Q. Vining +2
In the last decade, the free energy principle (FEP) and active inference (AIF) have achieved many successes connecting conceptual models of learning and cognition to mathematical m…