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Challenging the Quantum Advantage Frontier with Large-Scale Classical Simulations of Annealing Dynamics
Linda Mauron, Giuseppe Carleo
Recent demonstrations of D-Wave's annealing-based quantum simulators have established new benchmarks for quantum computational advantage [arXiv:2403.00910]. However, the precise lo…
Correcting and extending Trotterized quantum many-body dynamics
Gian Gentinetta, Friederike Metz, Giuseppe Carleo
A complex but important challenge in understanding quantum mechanical phenomena is the simulation of quantum many-body dynamics. Although quantum computers offer significant potent…
Non-stabilizerness of Neural Quantum States
Alessandro Sinibaldi, Antonio Francesco Mello, Mario Collura +1
We introduce a methodology to estimate non-stabilizerness or "magic", a key resource for quantum complexity, with Neural Quantum States (NQS). Our framework relies on two schemes b…
Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians
Riccardo Rende, Luciano Loris Viteritti, Federico Becca +3
Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like…
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…
Simulating continuous-space systems with quantum-classical wave functions
Friederike Metz, Gabriel Pescia, Giuseppe Carleo
Most non-relativistic interacting quantum many-body systems, such as atomic and molecular ensembles or materials, are naturally described in terms of continuous-space Hamiltonians.…
Predicting Topological Entanglement Entropy in a Rydberg analog simulator
Linda Mauron, Zakari Denis, Jannes Nys +1
Predicting the dynamical properties of topological matter is a challenging task, not only in theoretical and experimental settings, but also numerically. This work proposes a varia…
Accurate neural quantum states for interacting lattice bosons
Zakari Denis, Giuseppe Carleo
In recent years, neural quantum states have emerged as a powerful variational approach, achieving state-of-the-art accuracy when representing the ground-state wave function of a gr…
Simulating adiabatic quantum computation with a variational approach
Giuseppe Carleo, Bela Bauer, Matthias Troyer
The theoretical analysis of the Adiabatic Quantum Computation protocol presents several challenges resulting from the difficulty of simulating, with classical resources, the unitar…
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…
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…
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 .…