Dynamic, Symmetry-Preserving, and Hardware-Adaptable Circuits for Quantum Computing Many-Body States and Correlators of the Anderson Impurity Model
- URL: http://arxiv.org/abs/2405.15069v1
- Date: Thu, 23 May 2024 21:41:28 GMT
- Title: Dynamic, Symmetry-Preserving, and Hardware-Adaptable Circuits for Quantum Computing Many-Body States and Correlators of the Anderson Impurity Model
- Authors: Eric B. Jones, Cody James Winkleblack, Colin Campbell, Caleb Rotello, Edward D. Dahl, Matthew Reynolds, Peter Graf, Wesley Jones,
- Abstract summary: Hamiltonian expectation values are shown to require $omega(N_q) N_textmeas. leq O(N_textimpN_textbath)$ symmetry-preserving, parallel measurement circuits.
Our ansatz provides a useful tool to account for electronic correlations on early fault-tolerant processors.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a hardware-reconfigurable ansatz on $N_q$-qubits for the variational preparation of many-body states of the Anderson impurity model (AIM) with $N_{\text{imp}}+N_{\text{bath}}=N_q/2$ sites, which conserves total charge and spin z-component within each variational search subspace. The many-body ground state of the AIM is determined as the minimum over all minima of $O(N_q^2)$ distinct charge-spin sectors. Hamiltonian expectation values are shown to require $\omega(N_q) < N_{\text{meas.}} \leq O(N_{\text{imp}}N_{\text{bath}})$ symmetry-preserving, parallelizable measurement circuits, each amenable to post-selection. To obtain the one-particle impurity Green's function we show how initial Krylov vectors can be computed via mid-circuit measurement and how Lanczos iterations can be computed using the symmetry-preserving ansatz. For a single-impurity Anderson model with a number of bath sites increasing from one to six, we show using numerical emulation that the ease of variational ground-state preparation is suggestive of linear scaling in circuit depth and sub-quartic scaling in optimizer complexity. We therefore expect that, combined with time-dependent methods for Green's function computation, our ansatz provides a useful tool to account for electronic correlations on early fault-tolerant processors. Finally, with a view towards computing real materials properties of interest like magnetic susceptibilities and electron-hole propagators, we provide a straightforward method to compute many-body, time-dependent correlation functions using a combination of time evolution, mid-circuit measurement-conditioned operations, and the Hadamard test.
Related papers
- Neural network ensemble for computing cross sections for rotational transitions in H$_{2}$O + H$_{2}$O collisions [0.0]
We present a machine learning tool using an ensemble of neural networks (NNs) to predict cross sections.<n>The proposed methodology utilizes data computed with a mixed quantum-classical theory (MQCT)<n>Using only about 10% of the computed data for training, the NNs predict cross sections of state-to-state rotational transitions of H$_2$O + H$_2$O collision.
arXiv Detail & Related papers (2025-07-25T05:59:32Z) - Quantum Inspired Excited States Calculations for Molecules Based on Contextual Subspace and Symmetry Optimizations [2.2322840607996883]
Quantum-inspired methods for excited-state calculations remain underexplored in Noisy Intermediate-Scale Quantum (NISQ) hardware.
We propose a resource-efficient framework that integrates the contextual subspace (CS) method with the Variational Quantum Deflation (VQD) algorithm.
We demonstrate that the implementation of a spin-conserving hardware-efficient ansatz, namely the $mathcalN(theta_x,theta_y,theta_z)$ block ansatz, allows exploitation of spin symmetry within the projected subspace.
arXiv Detail & Related papers (2025-02-25T07:48:04Z) - Efficient Pseudomode Representation and Complexity of Quantum Impurity Models [0.7373617024876725]
Out-of-equilibrium fermionic quantum impurity models (QIM) describe a small interacting system coupled to a continuous fermionic bath.
We find efficient bath representations as that of approximating a kernel of the bath's Feynman-Vernon influence functional by a sum of complex exponentials.
To relate our findings to QIM, we derive an explicit Liouvillian that describes the time evolution of the combined impurity-pseudomodes system.
arXiv Detail & Related papers (2024-09-13T13:31:53Z) - Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks [54.177130905659155]
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks.
In this paper, we study a suitable function space for over- parameterized two-layer neural networks with bounded norms.
arXiv Detail & Related papers (2024-04-29T15:04:07Z) - Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Prediction [3.1690235522182104]
Emulation arises as the main near-term alternative to explore the potential of QRNNs.
We show how the present and past information from a time series is transmitted through the circuit.
We derive the analytical gradient and the Hessian of the network outputs with respect to its trainable parameters.
arXiv Detail & Related papers (2023-10-31T17:32:11Z) - Bounding the Width of Neural Networks via Coupled Initialization -- A
Worst Case Analysis [121.9821494461427]
We show how to significantly reduce the number of neurons required for two-layer ReLU networks.
We also prove new lower bounds that improve upon prior work, and that under certain assumptions, are best possible.
arXiv Detail & Related papers (2022-06-26T06:51:31Z) - Graph neural networks for fast electron density estimation of molecules,
liquids, and solids [0.0]
We present a machine learning framework for the prediction of $rho(vecr)$.
The model is tested across multiple data sets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo (1-y-z)O2 lithium ion battery cathodes (NMC)
arXiv Detail & Related papers (2021-12-01T16:57:31Z) - Improving the Accuracy of the Variational Quantum Eigensolver for
Molecular Systems by the Explicitly-Correlated Perturbative
[2]-R12-Correction [0.0]
We provide an integration of the universal, perturbative explicitly correlated [2]$_textR12$-correction in the context of the Variational Quantum Eigensolver (VQE)
This approach is able to increase the accuracy of the underlying reference method significantly while requiring no additional quantum resources.
arXiv Detail & Related papers (2021-10-13T15:52:01Z) - Determinant-free fermionic wave function using feed-forward neural
networks [0.0]
We propose a framework for finding the ground state of many-body fermionic systems by using feed-forward neural networks.
We show that the accuracy of the approximation can be improved by optimizing the "variance" of the energy simultaneously with the energy itself.
These improvements can be applied to other approaches based on variational Monte Carlo methods.
arXiv Detail & Related papers (2021-08-19T11:51:36Z) - Tightening the Dependence on Horizon in the Sample Complexity of
Q-Learning [59.71676469100807]
This work sharpens the sample complexity of synchronous Q-learning to an order of $frac|mathcalS|| (1-gamma)4varepsilon2$ for any $0varepsilon 1$.
Our finding unveils the effectiveness of vanilla Q-learning, which matches that of speedy Q-learning without requiring extra computation and storage.
arXiv Detail & Related papers (2021-02-12T14:22:05Z) - Stochastic Approximation for Online Tensorial Independent Component
Analysis [98.34292831923335]
Independent component analysis (ICA) has been a popular dimension reduction tool in statistical machine learning and signal processing.
In this paper, we present a by-product online tensorial algorithm that estimates for each independent component.
arXiv Detail & Related papers (2020-12-28T18:52:37Z) - Fermionic partial tomography via classical shadows [0.0]
We propose a tomographic protocol for estimating any $ k $-body reduced density matrix ($ k $-RDM) of an $ n $-mode fermionic state.
Our approach extends the framework of classical shadows, a randomized approach to learning a collection of quantum-state properties, to the fermionic setting.
arXiv Detail & Related papers (2020-10-30T06:28:26Z) - 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) - Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings.
In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$.
We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
arXiv Detail & Related papers (2020-02-25T19:18:36Z)
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.