Publications by Giuseppe Carleo

Accurate neural quantum states for interacting lattice bosons

In recent years, neural quantum states have emerged as a powerful variational approach, achieving state-of-the-art accuracy when representing the ground-state wave function of a great variety of quantum many-body systems, including spin lattices, interacti ...

Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften2025

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Giuseppe Carleo, Jannes Willy E. Nys

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

NATURE PORTFOLIO2025

Predicting topological entanglement entropy in a Rydberg analogue simulator

Giuseppe Carleo, Jannes Willy E. Nys

Predicting the dynamical properties of topological matter is a challenging task, not only in theoretical and experimental settings, but also computationally. Numerical studies are often constrained to studying simplified models and lattices. Here we propos ...

2025

Interaction quenches in Bose gases studied with a time-dependent hypernetted-chain Euler-Lagrange method

Giuseppe Carleo

We present a new variational method to study the dynamics of a closed bosonic many-body system, the time-dependent hypernetted-chain Euler-Lagrange method, tHNC. Based on the Jastrow ansatz, it accounts for quantum fluctuations in a non-perturbative way. T ...

2025

Phase Diagram and Crystal Melting of Helium-4 in Two Dimensions

Giuseppe Carleo, Gabriel Maria Pescia, David Linteau, Jannes Willy E. Nys

American Physical Society (APS)2025

Unusual Energy Spectra of Matrix Product States

Giuseppe Carleo

In approximate ground states obtained from imaginary-time evolution, the spectrum of the state - its decomposition into exact energy eigenstates - falls off exponentially with the energy. Here we consider the energy spectra of approximate matrix product gr ...

2025

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Giuseppe Carleo

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundati ...

2025

Variational benchmarks for quantum many-body problems

Andreas Martin Läuchli Herzig, Giuseppe Carleo, Dian Wu, Filippo Vicentini, Riccardo Rossi, Jannes Willy E. Nys

The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained ...

American Association for the Advancement of Science2024

Adaptive projected variational quantum dynamics

Giuseppe Carleo, Stefano Barison, David Linteau

We propose an adaptive quantum algorithm to prepare accurate variational time evolved wave functions. The method is based on the projected variational quantum dynamics (pVQD) algorithm, that performs a global optimization with linear scaling in the number ...

Amer Physical Soc2024

Message-passing neural quantum states for the homogeneous electron gas

Giuseppe Carleo, Gabriel Maria Pescia, Jannes Willy E. Nys

We introduce a message-passing neural-network (NN)-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. Symmetry constraints, such as continuous translation symmetries, can be readily embedded in the model. We ...

2024

Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation

Giuseppe Carleo, Gabriel Maria Pescia, Alessandro Sinibaldi, Jannes Willy E. Nys

Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, yet it poses a significant theoretical and computational challenge. Our wor ...

Nature Research2024

Sparse autoregressive neural networks for classical spin systems

Giuseppe Carleo, Dian Wu, Indaco Biazzo

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Iop Publishing Ltd2024

Neural-network quantum states for ultra-cold Fermi gases

Giuseppe Carleo, Gabriel Maria Pescia, Jannes Willy E. Nys

Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be p ...

Berlin2024

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

Giuseppe Carleo, Sofia Vallecorsa

Entanglement forging based variational algorithms leverage the bipartition of quantum systems for addressing ground-state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis stat ...

Amer Physical Soc2024

Empirical Sample Complexity of Neural Network Mixed State Reconstruction

Giuseppe Carleo, Filippo Vicentini, Haimeng Zhao

Quantum state reconstruction using Neural Quantum States has been proposed as a viable tool to reduce quantum shot complexity in practical applications, and its advantage over competing techniques has been shown in numerical experiments focusing mainly on ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2024

Real-time quantum dynamics of thermal states with neural thermofields

Giuseppe Carleo, Jannes Willy E. Nys

Solving the time-dependent quantum many-body Schrödinger equation is a challenging task, especially for states at a finite temperature, where the environment affects the dynamics. Most existing approximating methods are designed to represent static thermal ...

2024

Overhead-constrained circuit knitting for variational quantum dynamics

Giuseppe Carleo, Gian Florin Gentinetta, Friederike Metz

Simulating the dynamics of large quantum systems is a formidable yet vital pursuit for obtaining a deeper understanding of quantum mechanical phenomena. While quantum computers hold great promise for speeding up such simulations, their practical applicatio ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2024

Variational quantum time evolution without the quantum geometric tensor

Giuseppe Carleo, Riccardo Rossi, Julien Sebastian Gacon, Stefan Woerner, Jannes Willy E. Nys

Realand imaginary -time quantum state evolutions are crucial in physics and chemistry for exploring quantum dynamics, preparing ground states, and computing thermodynamic observables. On near -term devices, variational quantum time evolution is a promising ...

Amer Physical Soc2024

Continuous-variable neural network quantum states and the quantum rotor model

Giuseppe Carleo

We initiate the study of neural network quantum state algorithms for analyzing continuous-variable quantum systems in which the quantum degrees of freedom correspond to coordinates on a smooth manifold. A simple family of continuous-variable trial wavefunc ...

SPRINGERNATURE2023

Dilute neutron star matter from neural-network quantum states

Giuseppe Carleo

Low-density neutron matter is characterized by fascinating emergent quantum phenomena, such as the formation of Cooper pairs and the onset of superfluidity. We model this density regime by capitalizing on the expressivity of the hidden-nucleon neural-netwo ...

AMER PHYSICAL SOC2023