2.5k citations · 11k across the 97 of their papers we have counts for
98 papers
Precise Quantum Chemistry calculations with few Slater Determinants
Clemens Giuliani, Jannes Nys, Rocco Martinazzo +2
Slater determinants have underpinned quantum chemistry for nearly a century, yet their full potential has remained challenging to exploit. In this work, we show that a variational…
Nuclear responses with neural-network quantum states
Elad Parnes, Nir Barnea, Giuseppe Carleo +3
We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique to compute the dynamical properties of se…
Neural Wave Functions for High-Pressure Atomic Hydrogen
David Linteau, Saverio Moroni, Giuseppe Carleo +1
We leverage the power of neural quantum states to describe the ground state wave function of solid and liquid atomic hydrogen, including both electronic and protonic degrees of fre…
Challenging the Quantum Advantage Frontier with Large-Scale Classical Simulations of Annealing Dynamics
Linda Mauron, Giuseppe Carleo
Recent demonstrations of D-Wave's annealing-based quantum simulators have established new benchmarks for quantum computational advantage [arXiv:2403.00910]. However, the precise lo…
Correcting and extending Trotterized quantum many-body dynamics
Gian Gentinetta, Friederike Metz, Giuseppe Carleo
A complex but important challenge in understanding quantum mechanical phenomena is the simulation of quantum many-body dynamics. Although quantum computers offer significant potent…
Non-stabilizerness of Neural Quantum States
Alessandro Sinibaldi, Antonio Francesco Mello, Mario Collura +1
We introduce a methodology to estimate non-stabilizerness or "magic", a key resource for quantum complexity, with Neural Quantum States (NQS). Our framework relies on two schemes b…
Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians
Riccardo Rende, Luciano Loris Viteritti, Federico Becca +3
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…
Phase diagram and crystal melting of helium-4 in two dimensions
David Linteau, Gabriel Pescia, Jannes Nys +2
We study the zero-temperature phase diagram of two-dimensional helium-4 using neural quantum states. Our variational description allows us to address liquid and solid phases using…
Time-dependent Neural Galerkin Method for Quantum Dynamics
Alessandro Sinibaldi, Douglas Hendry, Filippo Vicentini +1
We introduce a classical computational method for quantum dynamics that relies on a global-in-time variational principle. Unlike conventional time-stepping approaches, our scheme c…
Simulating continuous-space systems with quantum-classical wave functions
Friederike Metz, Gabriel Pescia, Giuseppe Carleo
Most non-relativistic interacting quantum many-body systems, such as atomic and molecular ensembles or materials, are naturally described in terms of continuous-space Hamiltonians.…
Unusual energy spectra of matrix product states
J. Maxwell Silvester, Giuseppe Carleo, Steven R. White
In approximate ground states obtained from imaginary-time evolution, the spectrum of the state -- its decomposition into exact energy eigenstates -- falls off exponentially with th…
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
Beatriz Borges, Negar Foroutan, Deniz Bayazit +87
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they a…
Unveiling nonmagnetic phase and many-body entanglement in two-dimensional random quantum magnets SrCuTeWO
Dian Wu, Fan Yang, Giuseppe Carleo
We apply a random-plaquette - model on the square lattice to capture the physics of a series of spin- Heisenberg antiferromagnet compounds SrCuTeWO$_…
Predicting Topological Entanglement Entropy in a Rydberg analog simulator
Linda Mauron, Zakari Denis, Jannes Nys +1
Predicting the dynamical properties of topological matter is a challenging task, not only in theoretical and experimental settings, but also numerically. This work proposes a varia…
Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations
Imelda Romero, Jannes Nys, Giuseppe Carleo
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 s…
Accurate neural quantum states for interacting lattice bosons
Zakari Denis, Giuseppe Carleo
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 gr…
Simulating adiabatic quantum computation with a variational approach
Giuseppe Carleo, Bela Bauer, Matthias Troyer
The theoretical analysis of the Adiabatic Quantum Computation protocol presents several challenges resulting from the difficulty of simulating, with classical resources, the unitar…
Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation
Jannes Nys, Gabriel Pescia, Alessandro Sinibaldi +1
Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials,…
Sparse Autoregressive Neural Networks for Classical Spin Systems
Indaco Biazzo, Dian Wu, Giuseppe Carleo
Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Re…
Comment on "Can Neural Quantum States Learn Volume-Law Ground States?"
Zakari Denis, Alessandro Sinibaldi, Giuseppe Carleo
Passetti et al. [Physical Review Letters 131, 036502 (2023)] recently assessed the potential of neural quantum states (NQS) in learning ground-state wave functions with volume-law…
Variational Embeddings for Many Body Quantum Systems
Stefano Barison, Filippo Vicentini, Giuseppe Carleo
We propose a variational scheme to represent composite quantum systems using multiple parameterized functions of varying accuracies on both classical and quantum hardware. The appr…
Overhead-constrained circuit knitting for variational quantum dynamics
Gian Gentinetta, Friederike Metz, Giuseppe Carleo
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 hol…
Real-time quantum dynamics of thermal states with neural thermofields
Jannes Nys, Zakari Denis, Giuseppe Carleo
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.…
Adaptive projected variational quantum dynamics
David Linteau, Stefano Barison, Netanel Lindner +1
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) algor…