Learning interactions between Rydberg atoms

• URL: http://arxiv.org/abs/2412.12019v1
• Date: Mon, 16 Dec 2024 17:45:30 GMT
• Title: Learning interactions between Rydberg atoms
• Authors: Olivier Simard, Anna Dawid, Joseph Tindall, Michel Ferrero, Anirvan M. Sengupta, Antoine Georges,
• Abstract summary: We introduce a scalable approach to Hamiltonian learning using graph neural networks (GNNs) We demonstrate that our GNN model has a remarkable capacity to extrapolate beyond its training domain.
• Score: 4.17037025217542
• License:
• Abstract: Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To fulfill their prospects, quantum simulators must be fully controllable, allowing for precise tuning of the microscopic physical parameters that define their implementation. We consider Rydberg-atom arrays, a promising platform for quantum simulations. Experimental control of such arrays is limited by the imprecision on the optical tweezers positions when assembling the array, hence introducing uncertainties in the simulated Hamiltonian. In this work, we introduce a scalable approach to Hamiltonian learning using graph neural networks (GNNs). We employ the Density Matrix Renormalization Group (DMRG) to generate ground-state snapshots of the transverse field Ising model realized by the array, for many realizations of the Hamiltonian parameters. Correlation functions reconstructed from these snapshots serve as input data to carry out the training. We demonstrate that our GNN model has a remarkable capacity to extrapolate beyond its training domain, both regarding the size and the shape of the system, yielding an accurate determination of the Hamiltonian parameters with a minimal set of measurements. We prove a theorem establishing a bijective correspondence between the correlation functions and the interaction parameters in the Hamiltonian, which provides a theoretical foundation to our learning algorithm. Our work could open the road to feedback control of the positions of the optical tweezers, hence providing a decisive improvement of analog quantum simulators.

Related papers

• Quantum network tomography of Rydberg arrays by machine learning [0.0]
  Rydberg atoms are a versatile platform for quantum computation and quantum simulation, also allowing controllable decoherence. We demonstrate theoretically, that they also enable proof-of-principle demonstrations for a technique to build models for open quantum dynamics by machine learning with artificial neural networks.
  arXiv  Detail & Related papers  (2024-12-07T20:41:34Z)
• 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)
• Boundary scattering tomography of the Bose Hubbard model on general graphs [0.0]
  We present a scheme for tomography of quantum simulators that can be described by a Bose-Hubbard Hamiltonian. We show that with the additional ability to switch on and off the on-site repulsion in the simulator, we can sense the Hamiltonian parameters beyond the standard quantum limit.
  arXiv  Detail & Related papers  (2023-10-22T05:42:48Z)
• Quantum Computation and Simulation using Fermion-Pair Registers [0.0]
  We propose and analyze an approach to realize quantum computation and simulation using fermionic particles under quantum gas microscopes. We describe how to engineer the SWAP gate and high-fidelity controlled-phase gates. We show that 2D quantum Ising Hamiltonians with transverse and longitudinal fields can be efficient simulated by modulating Feshbach interaction strengths.
  arXiv  Detail & Related papers  (2023-06-06T17:59:08Z)
• Variational waveguide QED simulators [58.720142291102135]
  Waveguide QED simulators are made by quantum emitters interacting with one-dimensional photonic band-gap materials. Here, we demonstrate how these interactions can be a resource to develop more efficient variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-02-03T18:55:08Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Robustly learning the Hamiltonian dynamics of a superconducting quantum processor [0.5564835829075486]
  We robustly estimate the free Hamiltonian parameters of bosonic excitations in a superconducting-qubit analog quantum simulator. Our results constitute an accurate implementation of a dynamical quantum simulation.
  arXiv  Detail & Related papers  (2021-08-18T18:01:01Z)
• Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
  We present a constructive algorithm for generating quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
  arXiv  Detail & Related papers  (2021-04-01T19:06:00Z)
• Entanglement Hamiltonian Tomography in Quantum Simulation [0.0]
  Entanglement in quantum simulators is an outstanding challenge in today's era of intermediate scale quantum devices. Here we discuss an efficient tomographic protocol for reconstructing reduced density matrices and entanglement spectra for spin systems. We show the validity and efficiency of the protocol for a long-range Ising model in 1D using numerical simulations.
  arXiv  Detail & Related papers  (2020-09-18T18:12:22Z)
• 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)
• Simulating nonnative cubic interactions on noisy quantum machines [65.38483184536494]
  We show that quantum processors can be programmed to efficiently simulate dynamics that are not native to the hardware. On noisy devices without error correction, we show that simulation results are significantly improved when the quantum program is compiled using modular gates.
  arXiv  Detail & Related papers  (2020-04-15T05:16:24Z)

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.