18 citations · 34 across the 12 of their papers we have counts for
13 papers
QuantumMatterExplorer: An Autonomous Agent for the Discovery of Quantum Many-Body Phases of Matter
Jannes Nys, Andrew Jreissaty, Juan Carrasquilla
Mapping the phase diagram of a new quantum Hamiltonian demands expert judgement at every step: which observables to measure, where in parameter space to compute, or how to separate…
Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States
Hang Zhang, Victor Armegioiu, Juan Carrasquilla +4
Deep learning offers a powerful approach to quantum many-body problems via neural network wavefunctions, but their optimization remains a severe bottleneck. Existing optimization m…
Stabilizer-based quantum simulation of fermion dynamics with local qubit encodings
Anthony Gandon, Samuele Piccinelli, Max Rossmannek +4
Simulating the dynamical properties of large-scale many-fermion systems is a longstanding goal of quantum chemistry, material science and condensed matter. Local fermion-to-qubit e…
Fermionic neural Gibbs states
Jannes Nys, Juan Carrasquilla
We introduce fermionic neural Gibbs states (fNGS), a variational framework for modeling finite-temperature properties of strongly interacting fermions. fNGS starts from a reference…
Real-Time Dynamics in Two Dimensions with Tensor Network States via Time-Dependent Variational Monte Carlo
Yantao Wu, Jannes Nys
Reliably simulating two-dimensional many-body quantum dynamics with projected entangled pair states (PEPS) has long been a difficult challenge. In this work, we overcome this barri…
Majorana string simulation of nonequilibrium dynamics in two-dimensional lattice fermion systems
Matteo D'Anna, Jannes Nys, Juan Carrasquilla
The study of real-time dynamics of fermions remains one of the last frontiers beyond the reach of classical simulations and is key to our understanding of quantum behavior in chemi…
Accurate ground states of lattice gauge theory in 2+1D and 3+1D
Thomas Spriggs, Eliska Greplova, Juan Carrasquilla +1
We present a neural network wavefunction framework for solving non-Abelian lattice gauge theories in a continuous group representation. Using a combination of equivariant n…
Biorthogonal Neural Network Approach to Two-Dimensional Non-Hermitian Systems
Massimo Solinas, Brandon Barton, Yuxuan Zhang +2
Non-Hermitian quantum many-body systems exhibit a rich array of physical phenomena, including non-Hermitian skin effects and exceptional points, that remain largely inaccessible to…
Functional Neural Wavefunction Optimization
Victor Armegioiu, Juan Carrasquilla, Siddhartha Mishra +4
We propose a framework for the design and analysis of optimization algorithms in variational quantum Monte Carlo, drawing on geometric insights into the corresponding function spac…
Network control by a constrained external agent as a continuous optimization problem
Jannes Nys, Milan van den Heuvel, Koen Schoors +1
Social science studies dealing with control in networks typically resort to heuristics or describing the static control distribution. Optimal policies, however, require interventio…
Noise robustness of persistent homology on greyscale images, across filtrations and signatures
Renata Turkeš, Jannes Nys, Tim Verdonck +1
Topological data analysis is a recent and fast growing field that approaches the analysis of datasets using techniques from (algebraic) topology. Its main tool, persistent homology…
Communication coordination in network controllability
Milan van den Heuvel, Jannes Nys
Better understanding our ability to control an interconnected system of entities has been one of the central challenges in network science. The theories of node and edge controllab…
Neural Additive Vector Autoregression Models for Causal Discovery in Time Series
Bart Bussmann, Jannes Nys, Steven Latré
Causal structure discovery in complex dynamical systems is an important challenge for many scientific domains. Although data from (interventional) experiments is usually limited, l…