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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…
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Paulin de Schoulepnikoff, Oriel Kiss, Sofia Vallecorsa +2
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies…
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…
Neural-network quantum states for ultra-cold Fermi gases
Jane Kim, Gabriel Pescia, Bryce Fore +5
Ultra-cold Fermi gases display diverse quantum mechanical properties, including the transition from a fermionic superfluid BCS state to a bosonic superfluid BEC state, which can be…
Stochastic Approximation of Variational Quantum Imaginary Time Evolution
Julien Gacon, Christa Zoufal, Giuseppe Carleo +1
The imaginary-time evolution of quantum states is integral to various fields, ranging from natural sciences to classical optimization or machine learning. Since simulating quantum…
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…
Message-Passing Neural Quantum States for the Homogeneous Electron Gas
Gabriel Pescia, Jannes Nys, Jane Kim +2
We introduce a message-passing-neural-network-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. Symmetry constraints, such as cont…
Variational Quantum Time Evolution without the Quantum Geometric Tensor
Julien Gacon, Jannes Nys, Riccardo Rossi +2
The real- and imaginary-time evolution of quantum states are powerful tools in physics, chemistry, and beyond, to investigate quantum dynamics, prepare ground states or calculate t…
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…