60 citations · 116 across the 21 of their papers we have counts for
23 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…
QMBL: the Quantum Many-Body Leaderboard. A live record of published ground-state energies for lattice Hamiltonians and the electron gas
Tristan Zerweck, Juan Carrasquilla
In condensed matter physics, scientists explore phase diagrams of matter through various methods (analysis, numerics, experiment) and achieve consensus when enough methods agree. T…
Multipole splats for optimized and inverted effective potentials
Matija Medvidović, Angel Rubio, Juan Carrasquilla
In modern density functional theory, effective Hamiltonians are constructed to reproduce densities and forces of electrons at equilibrium. However, their nonlocal potentials leave…
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…
Neural network quantum states in the grand canonical ensemble
Anton Hul, Matija Medvidović, Juan Carrasquilla
Variational Monte Carlo calculations have recently reached state-of-the-art accuracy in the approximation of ground state properties of quantum many-body systems. Making use of fle…
Distributions of Noisy Expectation Values over Sets of Measurement Operators
Matthew Duschenes, Roger G. Melko, Juan Carrasquilla +1
Expectation values of measurement operators, interpreted as measurement probabilities, arise frequently throughout quantum algorithms. When quantum states are randomly distributed,…
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…
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…
Adiabatic transport of neural network quantum states
Matija Medvidović, Alev Orfi, Juan Carrasquilla +1
Variational methods have offered controllable and powerful tools for capturing many-body quantum physics for decades. The recent introduction of expressive neural network quantum s…
CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers
Haining Pan, James V. Roggeveen, Erez Berg +16
Large language models (LLMs) have shown remarkable progress in coding and math problem-solving, but evaluation on advanced research-level problems in hard sciences remains scarce.…
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…
Entanglement and optimization within autoregressive neural quantum states
Andrew Jreissaty, Hang Zhang, Jairo C. Quijano +2
Neural quantum states (NQSs) are powerful variational ansätze capable of representing highly entangled quantum many-body wavefunctions. While the average entanglement properties of…
Discovery of energy landscapes towards optimized quantum transport: Environmental effects and long-range tunneling
Maggie Lawrence, Matthew Pocrnic, Erin Fung +3
Carrier transport in quantum networks is governed by a variety of factors, including network dimensionality and connectivity, on-site energies, couplings between sites and whether…
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…
Circuit compression for 2D quantum dynamics
Matteo D'Anna, Yuxuan Zhang, Roeland Wiersema +2
The study of out-of-equilibrium quantum many-body dynamics remains one of the most exciting research frontiers of physics, standing at the crossroads of our understanding of comple…
Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the triangular lattice
M. Schuyler Moss, Roeland Wiersema, Mohamed Hibat-Allah +2
Variational Monte Carlo simulations have been crucial for understanding quantum many-body systems, especially when the Hamiltonian is frustrated and the ground-state wavefunction h…
Comment on: "Dynamics of disordered quantum systems with two- and three-dimensional tensor networks" arXiv:2503.05693
Andrew D. King, Alberto Nocera, Marek M. Rams +8
In a recent preprint [1] (arXiv:2503.05693), Tindall et al. presented impressive classical simulations of quantum dynamics using tensor networks. Their methods represent a signific…
Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the square lattice
M. Schuyler Moss, Roeland Wiersema, Mohamed Hibat-Allah +2
Machine-learning-based variational Monte Carlo simulations are a promising approach for targeting quantum many-body ground states, especially in two dimensions and in cases where t…
Autoregressive neural quantum states of Fermi Hubbard models
Eduardo Ibarra-García-Padilla, Hannah Lange, Roger G Melko +4
Neural quantum states (NQS) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly-correlated systems. Here, we apply recurrent neural networks (…
Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Mohamed Hibat-Allah, Ejaaz Merali, Giacomo Torlai +2
Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study fo…
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
Mohamed Hibat-Allah, Marta Mauri, Juan Carrasquilla +1
Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in th…
Spectral tensor networks for many-body localization
A. Chandran, J. Carrasquilla, I. H. Kim +2
Subsystems of strongly disordered, interacting quantum systems can fail to thermalize because of the phenomenon of many-body localization (MBL). In this article, we explore a tenso…