67 citations · 243 across the 16 of their papers we have counts for
16 papers
A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
Owen Lockwood, Jérémy Béjanin, Joost Bus +4
To help address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack tha…
Machine learning the Ising transition: A comparison between discriminative and generative approaches
Difei Zhang, Frank Schäfer, Julian Arnold
The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems ca…
Phase Transitions in the Output Distribution of Large Language Models
Julian Arnold, Flemming Holtorf, Frank Schäfer +1
In a physical system, changing parameters such as temperature can induce a phase transition: an abrupt change from one state of matter to another. Analogous phenomena have recently…
Machine learning phase transitions: Connections to the Fisher information
Julian Arnold, Niels Lörch, Flemming Holtorf +1
Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here…
Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion
Julian Arnold, Frank Schäfer, Niels Lörch
Machine learning has been successfully used to study phase transitions. One of the most popular approaches to identifying critical points from data without prior knowledge of the u…
Mapping out phase diagrams with generative classifiers
Julian Arnold, Frank Schäfer, Alan Edelman +1
One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and un…
Performance Bounds for Quantum Feedback Control
Flemming Holtorf, Frank Schäfer, Julian Arnold +2
The limits of quantum feedback control have immediate consequences for quantum information science at large, yet remain largely unexplored. Here, we combine quantum filtering theor…
Data-driven reconstruction of spectral conductivity and chemical potential from thermoelectric transport data
Tomoki Hirosawa, Frank Schäfer, Hideaki Maebashi +2
The spectral conductivity, i.e., the electrical conductivity as a function of the Fermi energy, is a cornerstone in determining the thermoelectric transport properties of electrons…
Replacing neural networks by optimal analytical predictors for the detection of phase transitions
Julian Arnold, Frank Schäfer
Identifying phase transitions and classifying phases of matter is central to understanding the properties and behavior of a broad range of material systems. In recent years, machin…
Automated reconstruction of bound states in bilayer graphene quantum dots
Jozef Bucko, Frank Schäfer, František Herman +5
Bilayer graphene is a nanomaterial that allows for well-defined, separated quantum states to be defined by electrostatic gating and, therefore, provides an attractive platform to c…
AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
Frank Schäfer, Mohamed Tarek, Lyndon White +1
No single Automatic Differentiation (AD) system is the optimal choice for all problems. This means informed selection of an AD system and combinations can be a problem-specific var…
Control of Stochastic Quantum Dynamics by Differentiable Programming
Frank Schäfer, Pavel Sekatski, Martin Koppenhöfer +2
Control of the stochastic dynamics of a quantum system is indispensable in fields such as quantum information processing and metrology. However, there is no general ready-made appr…
Interpretable and unsupervised phase classification
Julian Arnold, Frank Schäfer, Martin Žonda +1
Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method…
A differentiable programming method for quantum control
Frank Schäfer, Michal Kloc, Christoph Bruder +1
Optimal control is highly desirable in many current quantum systems, especially to realize tasks in quantum information processing. We introduce a method based on differentiable pr…
Unsupervised identification of topological order using predictive models
Eliska Greplova, Agnes Valenti, Gregor Boschung +3
Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover ne…
Divergence of predictive model output as indication of phase transitions
Frank Schäfer, Niels Lörch
We introduce a new method to identify phase boundaries in physical systems. It is based on training a predictive model such as a neural network to infer a physical system's paramet…