233 citations · 1.1k across the 21 of their papers we have counts for
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Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
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…
Neural Projected Quantum Dynamics: a systematic study
Luca Gravina, Vincenzo Savona, Filippo Vicentini
We investigate the challenge of classical simulation of unitary quantum dynamics with variational Monte Carlo approaches, addressing the instabilities and high computational demand…
Efficiency of neural quantum states in light of the quantum geometric tensor
Sidhartha Dash, Luca Gravina, Filippo Vicentini +2
Neural quantum state (NQS) ansätze have shown promise in variational Monte Carlo algorithms by their theoretical capability of representing any quantum state. However, the reason b…
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…
Variational dynamics of open quantum systems in phase space
Debbie Eeltink, Filippo Vicentini, Vincenzo Savona
We present a method to simulate the dynamics of large driven-dissipative many-body open quantum systems using a variational encoding of the Wigner or Husimi-Q quasi-probability dis…
Empirical Sample Complexity of Neural Network Mixed State Reconstruction
Haimeng Zhao, Giuseppe Carleo, Filippo Vicentini
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 comp…
Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution
Alessandro Sinibaldi, Clemens Giuliani, Giuseppe Carleo +1
We analyze the accuracy and sample complexity of variational Monte Carlo approaches to simulate the dynamics of many-body quantum systems classically. By systematically studying th…
Learning ground states of gapped quantum Hamiltonians with Kernel Methods
Clemens Giuliani, Filippo Vicentini, Riccardo Rossi +1
Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statisti…
Variational Benchmarks for Quantum Many-Body Problems
Dian Wu, Riccardo Rossi, Filippo Vicentini +30
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…
Positive-definite parametrization of mixed quantum states with deep neural networks
Filippo Vicentini, Riccardo Rossi, Giuseppe Carleo
We introduce the Gram-Hadamard Density Operator (GHDO), a new deep neural-network architecture that can encode positive semi-definite density operators of exponential rank with pol…
From Tensor Network Quantum States to Tensorial Recurrent Neural Networks
Dian Wu, Riccardo Rossi, Filippo Vicentini +1
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 2D la…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Variational dynamics as a ground-state problem on a quantum computer
Stefano Barison, Filippo Vicentini, Ignacio Cirac +1
We propose a variational quantum algorithm to study the real time dynamics of quantum systems as a ground-state problem. The method is based on the original proposal of Feynman and…
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
Filippo Vicentini, Damian Hofmann, Attila Szabó +8
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms…
An efficient quantum algorithm for the time evolution of parameterized circuits
Stefano Barison, Filippo Vicentini, Giuseppe Carleo
We introduce a novel hybrid algorithm to simulate the real-time evolution of quantum systems using parameterized quantum circuits. The method, named "projected - Variational Quantu…
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
Giuseppe Carleo, Kenny Choo, Damian Hofmann +15
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and…
Variational neural network ansatz for steady states in open quantum systems
Filippo Vicentini, Alberto Biella, Nicolas Regnault +1
We present a general variational approach to determine the steady state of open quantum lattice systems via a neural network approach. The steady-state density matrix of the lattic…
Optimal stochastic unraveling of disordered open quantum systems: application to driven-dissipative photonic lattices
Filippo Vicentini, Fabrizio Minganti, Alberto Biella +2
We propose an efficient numerical method to compute configuration averages of observables in disordered open quantum systems whose dynamics can be unraveled via stochastic trajecto…
Critical slowing down in driven-dissipative Bose-Hubbard lattices
Filippo Vicentini, Fabrizio Minganti, Riccardo Rota +2
We theoretically explore the dynamical properties of a first-order dissipative phase transition in coherently driven Bose-Hubbard systems, describing, e.g., lattices of coupled non…