A blueprint for a Digital-Analog Variational Quantum Eigensolver using Rydberg atom arrays

Abstract

We address the task of estimating the ground-state energy of Hamiltonians coming from chemistry. We study numerically the behavior of a digital-analog variational quantum eigensolver for the H2, LiH and BeH2 molecules, and we observe that one can estimate the energy to a few percent points of error leveraging on learning the atom register positions with respect to selected features of the molecular Hamiltonian and then an iterative pulse shaping optimization, where each step performs a derandomization energy estimation.

13 pages, 8 figures

References in corpus (14)

• Variational Quantum Algorithms
• Many-Body Physics with Individually-Controlled Rydberg Atoms
• Programmable quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Quantum computing with neutral atoms
• Quantum Optimization of Maximum Independent Set using Rydberg Atom Arrays
• Continuous Symmetry Breaking in a Two-dimensional Rydberg Array
• Efficient estimation of Pauli observables by derandomization
• Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information
• Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays
• In-situ equalization of single-atom loading in large-scale optical tweezers arrays
• Overlapped grouping measurement: A unified framework for measuring quantum states
• Robustness to spontaneous emission of a variational quantum algorithm
• Using gradient-based algorithms to determine ground state energies on a quantum computer

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