Publications by Filippo Vicentini

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Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel me ...

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 the relevant stochastic estimators, we are able to: (i) prove that the most us ...

We introduce a novel hybrid algorithm to simulate the real-time evolution of quantum systems using parameterized quantum circuits. The method, named "projected - Variational Quantum Dynamics" (p-VQD) realizes an iterative, global projection of the exact ti ...

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 ...

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 ...

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 ...

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 Kitaev to encode time into a register of auxiliary qubits. We prepare the ...

Filippo Vicentini

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Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution

An efficient quantum algorithm for the time evolution of parameterized circuits

Empirical Sample Complexity of Neural Network Mixed State Reconstruction

From tensor-network quantum states to tensorial recurrent neural networks

Variational benchmarks for quantum many-body problems

Variational dynamics as a ground-state problem on a quantum computer

Machine learning toolbox for quantum many body physics