2.5k citations · 11k across the 97 of their papers we have counts for
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Dilute neutron star matter from neural-network quantum states
Bryce Fore, Jane M. Kim, Giuseppe Carleo +2
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 r…
Interaction quenches in Bose gases studied with a time-dependent hypernetted-chain Euler-Lagrange method
Mathias Gartner, David Miesbauer, Michael Kobler +3
We present a new variational method to study the dynamics of a closed bosonic many-body system, the time-dependent hypernetted-chain Euler-Lagrange method, tHNC . Based on the Jast…
Ab-initio quantum chemistry with neural-network wavefunctions
Jan Hermann, James Spencer, Kenny Choo +5
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also…
Quantum circuits for solving local fermion-to-qubit mappings
Jannes Nys, Giuseppe Carleo
Local Hamiltonians of fermionic systems on a lattice can be mapped onto local qubit Hamiltonians. Maintaining the locality of the operators comes at the expense of increasing the H…
A rapidly mixing Markov chain from any gapped quantum many-body system
Sergey Bravyi, Giuseppe Carleo, David Gosset +1
We consider the computational task of sampling a bit string from a distribution , where is the unique ground state of a local Hamiltonian .…
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…
Hidden-nucleons neural-network quantum states for the nuclear many-body problem
A. Lovato, C. Adams, G. Carleo +1
We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schrödinger eq…
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…
Exponential challenges in unbiasing quantum Monte Carlo algorithms with quantum computers
Guglielmo Mazzola, Giuseppe Carleo
Recently, Huggins et. al. [Nature, 603, 416-420 (2022)] devised a general projective Quantum Monte Carlo method suitable for implementation on quantum computers. This hybrid approa…
Entanglement Forging with generative neural network models
Patrick Huembeli, Giuseppe Carleo, Antonio Mezzacapo
The optimal use of quantum and classical computational techniques together is important to address problems that cannot be easily solved by quantum computations alone. This is the…
Phenomenological Theory of Variational Quantum Ground-State Preparation
Nikita Astrakhantsev, Guglielmo Mazzola, Ivano Tavernelli +1
The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver…
Variational solutions to fermion-to-qubit mappings in two spatial dimensions
Jannes Nys, Giuseppe Carleo
Through the introduction of auxiliary fermions, or an enlarged spin space, one can map local fermion Hamiltonians onto local spin Hamiltonians, at the expense of introducing a set…
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…
Matrix Product States with Backflow correlations
Guglielmo Lami, Giuseppe Carleo, Mario Collura
By taking inspiration from the backflow transformation for correlated systems, we introduce a novel tensor network ansatz which extend the well-established Matrix Product State rep…