Stefano Polla

Learning minimal representations of fermionic ground states

Felix Frohnert, Emiel Koridon, Stefano Polla

We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network architecture on reduced density matrix data from L -site Fermi-Hubbard models, we identify minimal latent spaces with a sharp reconstruction quality …

nl (code pays fourni par la source)

Accurate ground state energy estimation with noise and imperfect state preparation

Alicja Dutkiewicz, T. E. O'Brien, Stefano Polla

We introduce a classical estimator for the post-processing of quantum phase estimation (QPE) data when a single target phase is isolated within a known interval, as is typical of ground state energy estimation of gapped systems. Our estimator filters the QPE signal …

nl, us (code pays fourni par la source)

Accurate ground state energy estimation with noise and imperfect state preparation

Alicja Dutkiewicz, T. E. O'Brien, Stefano Polla

We introduce a classical estimator for the post-processing of quantum phase estimation (QPE) data when a single target phase is isolated within a known interval, as is typical of ground state energy estimation of gapped systems. Our estimator filters the QPE signal …

nl, us (code pays fourni par la source)

Optimizing and Comparing Quantum Resources of Statistical Phase Estimation and Krylov Subspace Diagonalization

Oumarou Oumarou, Pauline J. Ollitrault, Stefano Polla, Christian Gogolin

We develop a framework that enables direct and meaningful comparison of two early fault-tolerant methods for the computation of eigenenergies, namely \gls{qksd} and \gls{spe}, within which both methods use expectation values of Chebyshev polynomials of the Hamiltonian as input. For \gls{qksd} we …

Optimizing and Comparing Quantum Resources of Statistical Phase Estimation and Krylov Subspace Diagonalization

Oumarou Oumarou, Pauline J. Ollitrault, Stefano Polla, Christian Gogolin

We develop a framework that enables direct and meaningful comparison of two early fault-tolerant methods for the computation of eigenenergies, namely \gls{qksd} and \gls{spe}, within which both methods use expectation values of Chebyshev polynomials of the Hamiltonian as input. For \gls{qksd} we …

us, nl, de (code pays fourni par la source)

StefanoPolla/DFTQML: Machine Learning for Quantum Chemistry

Stefano Polla, Felix Frohnert, Emiel Koridon

Two projects for machine learning in quantum chemistry.

nl (code pays fourni par la source)

Error Mitigation and Circuit Division for Early Fault-Tolerant Quantum Phase Estimation

Alicja Dutkiewicz, Stefano Polla, Maximilian Scheurer, Christian Gogolin et autres

As fully fault-tolerant quantum computers capable of solving useful problems remain a distant goal, we anticipate an era of during which limited error correction is available. We propose a framework for designing early fault-tolerant algorithms by trading between error-correction overhead and residual …

nl, us, de (code pays fourni par la source)

Learning density functionals from noisy quantum data

Emiel Koridon, Felix Frohnert, Eric Prehn, Evert van Nieuwenburg et autres

Abstract The search for useful applications of noisy intermediate-scale quantum (NISQ) devices in quantum simulation has been hindered by their intrinsic noise and the high costs associated with achieving high accuracy. A promising approach to finding utility despite these challenges involves using …

nl (code pays fourni par la source)

FragPT2: Multifragment Wave Function Embedding with Perturbative Interactions

Emiel Koridon, Souloke Sen, Lucas Visscher, Stefano Polla

High Resolution Image Download MS PowerPoint Slide Embedding techniques allow the efficient description of correlations within localized fragments of large molecular systems while accounting for their environment at a lower level of theory. We introduce FragPT2: a novel embedding framework that addresses …

nl (code pays fourni par la source)

Luttinger liquid tensor network: Sine versus tangent dispersion of massless Dirac fermions

Vladimir A. Zakharov, Stefano Polla, A. Donís Vela, Patrick Emonts et autres

To apply the powerful many-body techniques of tensor networks to massless Dirac fermions, one wants to discretize the p · σ Hamiltonian and construct a matrix-product-operator (MPO) representation. We compare two alternative discretization schemes, one with a sine dispersion, the other with …

nl, de, pl (code pays fourni par la source)

Error mitigation and circuit division for early fault-tolerant quantum phase estimation

Alicja Dutkiewicz, Stefano Polla, Maximilian Scheurer, Christian Gogolin et autres

As fully fault-tolerant quantum computers capable of solving useful problems remain a distant goal, we anticipate an era of "early fault tolerance" where limited error correction is available. We propose a framework for designing early fault-tolerant algorithms by trading between error correction …

Learning Density Functionals from Noisy Quantum Data

Emiel Koridon, Felix Frohnert, Eric Prehn, Evert van Nieuwenburg et autres

The search for useful applications of noisy intermediate-scale quantum (NISQ) devices in quantum simulation has been hindered by their intrinsic noise and the high costs associated with achieving high accuracy. A promising approach to finding utility despite these challenges involves using quantum …