7 papers
QuantGraph: A Receding-Horizon Quantum Graph Solver
Pranav Vaidhyanathan, Aristotelis Papatheodorou, David R. M. Arvidsson-Shukur +3
Dynamic programming is a cornerstone of graph-based optimization. While effective, it scales unfavorably with problem size. In this work, we present QuantGraph, a two-stage quantum…
Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures
Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares +1
Physics-informed deep learning has achieved remarkable progress by embedding geometric priors, such as Hamiltonian symmetries and variational principles, into neural networks, enab…
Meta-learning characteristics and dynamics of quantum systems
Lucas Schorling, Pranav Vaidhyanathan, Jonas Schuff +7
While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Meta-learning approaches, ho…
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence
Pranav Vaidhyanathan, Aristotelis Papatheodorou, Mark T. Mitchison +2
Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to mo…
Entropic costs of extracting classical ticks from a quantum clock
Vivek Wadhia, Florian Meier, Federico Fedele +12
We experimentally realize a quantum clock by using a charge sensor to count charges tunneling through a double quantum dot (DQD). Individual tunneling events are used as the clock'…
Rapid optimal work extraction from a quantum-dot information engine
Kushagra Aggarwal, Alberto Rolandi, Yikai Yang +8
The conversion of thermal energy into work is usually more efficient in the slow-driving regime, where the power output is vanishingly small. Efficient work extraction for fast dri…