Approximating dynamical correlation functions with constant depth quantum circuits
- URL: http://arxiv.org/abs/2406.03204v1
- Date: Wed, 5 Jun 2024 12:40:38 GMT
- Title: Approximating dynamical correlation functions with constant depth quantum circuits
- Authors: Reinis Irmejs, Raul A. Santos,
- Abstract summary: We show that it is possible to approximate the dynamical correlation functions up to exponential accuracy in the complex frequency domain $omega=Re(omega)+iIm(omega)$.
We prove that these algorithms generate an exponentially accurate approximation of the correlation functions on a region sufficiently far away from the real frequency axis.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: One of the most important quantities characterizing the microscopic properties of quantum systems are dynamical correlation functions. These correlations are obtained by time-evolving a perturbation of an eigenstate of the system, typically the ground state. In this work, we study approximations of these correlation functions that do not require time dynamics. We show that having access to a circuit that prepares an eigenstate of the Hamiltonian, it is possible to approximate the dynamical correlation functions up to exponential accuracy in the complex frequency domain $\omega=\Re(\omega)+i\Im(\omega)$, on a strip above the real line $\Im(\omega)=0$. We achieve this by exploiting the continued fraction representation of the dynamical correlation functions as functions of frequency $\omega$, where the level $k$ approximant can be obtained by measuring a weight $O(k)$ operator on the eigenstate of interest. In the complex $\omega$ plane, we show how this approach allows to determine approximations to correlation functions with accuracy that increases exponentially with $k$. We analyse two algorithms to generate the continuous fraction representation in scalar or matrix form, starting from either one or many initial operators. We prove that these algorithms generate an exponentially accurate approximation of the dynamical correlation functions on a region sufficiently far away from the real frequency axis. We present numerical evidence of these theoretical results through simulations of small lattice systems. We comment on the stability of these algorithms with respect to sampling noise in the context of quantum simulation using quantum computers.
Related papers
- Gauge-Fixing Quantum Density Operators At Scale [0.0]
We provide theory, algorithms, and simulations of non-equilibrium quantum systems.
We analytically and numerically examine the virtual freedoms associated with the representation of quantum density operators.
arXiv Detail & Related papers (2024-11-05T22:56:13Z) - Quantum Simulation of Nonlinear Dynamical Systems Using Repeated Measurement [42.896772730859645]
We present a quantum algorithm based on repeated measurement to solve initial-value problems for nonlinear ordinary differential equations.
We apply this approach to the classic logistic and Lorenz systems in both integrable and chaotic regimes.
arXiv Detail & Related papers (2024-10-04T18:06:12Z) - Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - KPZ scaling from the Krylov space [83.88591755871734]
Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang scaling in late-time correlators and autocorrelators has been reported.
Inspired by these results, we explore the KPZ scaling in correlation functions using their realization in the Krylov operator basis.
arXiv Detail & Related papers (2024-06-04T20:57:59Z) - Exact Spin Correlators of Integrable Quantum Circuits from Algebraic Geometry [2.7852431537059426]
We calculate the correlation functions of strings of spin operators for integrable quantum circuits exactly.
These observables can be used for calibration of quantum simulation platforms.
arXiv Detail & Related papers (2024-05-25T05:42:14Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Correlation functions for realistic continuous quantum measurement [0.0]
We propose a self-contained and accessible derivation of an exact formula for the $n$-point correlation functions of the signal measured when continuously observing a quantum system.
arXiv Detail & Related papers (2022-11-30T23:45:22Z) - Efficient Fully-Coherent Quantum Signal Processing Algorithms for
Real-Time Dynamics Simulation [3.3917542048743865]
We develop fully-coherent simulation algorithms based on quantum signal processing (QSP)
We numerically analyze these algorithms by applying them to the simulation of spin dynamics of the Heisenberg model.
arXiv Detail & Related papers (2021-10-21T17:56:33Z) - Out-of-time-order correlations and the fine structure of eigenstate
thermalisation [58.720142291102135]
Out-of-time-orderors (OTOCs) have become established as a tool to characterise quantum information dynamics and thermalisation.
We show explicitly that the OTOC is indeed a precise tool to explore the fine details of the Eigenstate Thermalisation Hypothesis (ETH)
We provide an estimation of the finite-size scaling of $omega_textrmGOE$ for the general class of observables composed of sums of local operators in the infinite-temperature regime.
arXiv Detail & Related papers (2021-03-01T17:51:46Z) - On Function Approximation in Reinforcement Learning: Optimism in the
Face of Large State Spaces [208.67848059021915]
We study the exploration-exploitation tradeoff at the core of reinforcement learning.
In particular, we prove that the complexity of the function class $mathcalF$ characterizes the complexity of the function.
Our regret bounds are independent of the number of episodes.
arXiv Detail & Related papers (2020-11-09T18:32:22Z) - Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with
an artificial neural-network correlator ansatz [62.997667081978825]
We introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei.
We compute the binding energies and point-nucleon densities of $Aleq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian.
arXiv Detail & Related papers (2020-07-28T14:52:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.