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Giuseppe Carleo

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Publications associées (47)

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Quantum circuits for solving local fermion-to-qubit map- pings

Giuseppe Carleo, Jannes Willy E. Nys

Local Hamiltonians of fermionic systems on a lattice can be mapped onto local qubit Hamiltonians. Maintaining the lo-cality of the operators comes at the ex-pense of increasing the Hilbert space with auxiliary degrees of freedom. In order to retrieve the l ...

VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF2023

Overhead-constrained circuit knitting for variational quantum dynamics

Giuseppe Carleo, Friederike Metz, Gian Florin Gentinetta

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 Monte Carlo Calculations of A

Giuseppe Carleo

The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum m ...

AMER PHYSICAL SOC2021

Neural tensor contractions and the expressive power of deep neural quantum states

Giuseppe Carleo

We establish a direct connection between general tensor networks and deep feed-forward artificial neural networks. The core of our results is the construction of neural-network layers that efficiently perform tensor contractions and that use commonly adopt ...

AMER PHYSICAL SOC2022

A rapidly mixing Markov chain from any gapped quantum many-body system

Giuseppe Carleo

We consider the computational task of sampling a bit string x from a distribution pi(x) = |(x|0)|2, where 0 is the unique ground state of a local Hamiltonian H. Our main result describes a direct link between the inverse spectral gap of H and the mixing ti ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2023

From tensor-network quantum states to tensorial recurrent neural networks

Giuseppe Carleo, Filippo Vicentini, Riccardo Rossi, Dian Wu

We show that any matrix product state (MPS) can be exactly represented by a recurrent neural network (RNN) with a linear memory update. We generalize this RNN architecture to two-dimensional lattices using a multilinear memory update. It supports perfect s ...

AMER PHYSICAL SOC2023

Quantum process tomography with unsupervised learning and tensor networks

Giuseppe Carleo

The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, r ...

Nature Portfolio2023

Broken-Symmetry Ground States of the Heisenberg Model on the Pyrochlore Lattice

Giuseppe Carleo

The spin-1/2 Heisenberg model on the pyrochlore lattice is an iconic frustrated three-dimensional spin system with a rich phase diagram. Besides hosting several ordered phases, the model is debated to possess a spin-liquid ground state when only nearest-ne ...

American Physical Society2021

Variational benchmarks for quantum many-body problems

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

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

Gauge Equivariant Neural Networks for Quantum Lattice Gauge Theories

Giuseppe Carleo

Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials. Motivated by the desire to efficiently simulate many-body quantum systems with ...

AMER PHYSICAL SOC2021

Classical variational simulation of the Quantum Approximate Optimization Algorithm

Giuseppe Carleo

A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating quantum systems is ...

2021

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

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 ...

American Physical Society (APS)2025

Stochastic Approximation of Variational Quantum Imaginary Time Evolution

Giuseppe Carleo

The imaginary-time evolution of quantum states is integral to various fields, ranging from natural sciences to classical optimization or machine learning. Since simulating quantum imaginary-time evolution generally requires storing an exponentially large w ...

Institute of Electrical and Electronics Engineers Inc.2023

Accurate neural quantum states for interacting lattice bosons

Giuseppe Carleo, Zakari Denis

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

Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states

Giuseppe Carleo

First-quantized deep neural network techniques are developed for analyzing strongly coupled fermionic systems on the lattice. Using a Slater-Jastrow-inspired ansatz which exploits deep residual networks with convolutional residual blocks, we approximately ...

AMER PHYSICAL SOC2020

Message-passing neural quantum states for the homogeneous electron gas

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

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

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

Predicting topological entanglement entropy in a Rydberg analogue simulator

Linda Mauron, Giuseppe Carleo, Jannes Willy E. Nys, Zakari Denis

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

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