86 citations · 342 across the 12 of their papers we have counts for
13 papers
Scalable Neural Wave Functions via Information Mixing and Hierarchical Network Architectures
Faith Oyedemi, Stefanie Czischek
Numerically simulating quantum many-body systems is one of the central challenges in modern physics, as the exponential growth of the Hilbert space with system size renders large-s…
Reinforcement Learning for Optimal Control of Spin Magnetometers
Logan W. Cooke, Stefanie Czischek
Quantum optimal control in the presence of decoherence is difficult, particularly when not all Hamiltonian parameters are known precisely, as in quantum sensing applications. In th…
Fast and Automated Optical Polarization Compensation of Fiber Unitaries
Niklas Braband, Arman Mansouri, Riza Fazili +2
The polarization of light is critical in various applications, including quantum communication, where the photon polarization encoding a qubit can undergo uncontrolled changes when…
End-to-end variational quantum sensing
Benjamin MacLellan, Piotr Roztocki, Stefanie Czischek +1
Harnessing quantum correlations can enable sensing beyond the classical limits of precision, with the realization of such sensors poised for transformative impacts across science a…
Variational Monte Carlo with Large Patched Transformers
Kyle Sprague, Stefanie Czischek
Large language models, like transformers, have recently demonstrated immense powers in text and image generation. This success is driven by the ability to capture long-range correl…
Data-Enhanced Variational Monte Carlo Simulations for Rydberg Atom Arrays
Stefanie Czischek, M. Schuyler Moss, Matthew Radzihovsky +2
Rydberg atom arrays are programmable quantum simulators capable of preparing interacting qubit systems in a variety of quantum states. Due to long experimental preparation times, o…
Simulating a measurement-induced phase transition for trapped ion circuits
Stefanie Czischek, Giacomo Torlai, Sayonee Ray +2
The rise of programmable quantum devices has motivated the exploration of circuit models which could realize novel physics. A promising candidate is a class of hybrid circuits, whe…
Miniaturizing neural networks for charge state autotuning in quantum dots
Stefanie Czischek, Victor Yon, Marc-Antoine Genest +8
A key challenge in scaling quantum computers is the calibration and control of multiple qubits. In solid-state quantum dots, the gate voltages required to stabilize quantized charg…
Spiking neuromorphic chip learns entangled quantum states
Stefanie Czischek, Andreas Baumbach, Sebastian Billaudelle +8
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structura…
Neural network quantum state tomography in a two-qubit experiment
Marcel Neugebauer, Laurin Fischer, Alexander Jäger +4
We study the performance of efficient quantum state tomography methods based on neural network quantum states using measured data from a two-photon experiment. Machine learning ins…
Sampling scheme for neuromorphic simulation of entangled quantum systems
Stefanie Czischek, Jan M. Pawlowski, Thomas Gasenzer +1
Due to the complexity of the space of quantum many-body states the computation of expectation values by statistical sampling is, in general, a hard task. Neural network representat…
Quenches near criticality of the quantum Ising chain---power and limitations of the discrete truncated Wigner approximation
Stefanie Czischek, Martin Gärttner, Markus Oberthaler +2
The semi-classical discrete truncated Wigner approximation (dTWA) has recently been proposed as a simulation method for spin- systems. While it appears to provide a powerful a…
Quenches near Ising quantum criticality as a challenge for artificial neural networks
Stefanie Czischek, Martin Gärttner, Thomas Gasenzer
The near-critical unitary dynamics of quantum Ising spin chains in transversal and longitudinal magnetic fields is studied using an artificial neural network representation of the…