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arXiv:2301.06453 · Physical Review A

Blueprint for a digital-analog variational quantum eigensolver using Rydberg atom arrays

Antoine Michel, Sebastián Grijalva, Loïc Henriet, Christophe Domain, Antoine Browaeys

Published Apr 6, 2023• 14 citations
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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 ${\mathrm{H}}_{2}$, LiH, and ${\mathrm{BeH}}_{2}$ 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.

DOI
10.1103/physreva.107.042602

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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{\mathrm{H}}_{2}H2​, LiH, and BeH2{\mathrm{BeH}}_{2}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.
Digital-analog quantum computing (DAQC) combines digital gates with analog operations, offering an alternative paradigm for universal quantum computation. This approach leverages the higher fidelities of analog operations and the flexibility of local single-qubit gates. In this paper, we propose a quantum genetic algorithm within the DAQC framework using a Rydberg-atom emulator. The algorithm employs single-qubit operations in the digital domain and a global driving interaction based on the Rydberg Hamiltonian in the analog domain. We evaluate the algorithm performance by estimating the ground-state energy of Hamiltonians, with a focus on molecules such as H2,LiH{\mathrm{H}}_{2}, \mathrm{LiH}H2​,LiH, and BeH2{\mathrm{BeH}}_{2}BeH2​. Our results show energy estimations with less than 1% error and state overlaps nearing 1, with computation times ranging from a few minutes for H2{\mathrm{H}}_{2}H2​ (two-qubit circuits) to one to two days for LiH\mathrm{LiH}LiH and BeH2{\mathrm{BeH}}_{2}BeH2​ (six-qubit circuits). The gate fidelities of global analog operations further underscore DAQC as a promising quantum computing strategy in the noisy intermediate-scale quantum era.
Abstract Variational quantum eigensolvers (VQEs) are garnering significant attention for their ability to estimate the ground-state energy of target Hamiltonians. In particular, digital-analog quantum computation (DAQC) stands out as a crucial platform for VQEs in the noisy intermediate-scale quantum era, leveraging single-qubit operations and natural time evolution governed by parameterized Hamiltonians. On the other hand, the variational quantum-neural hybrid eigensolver is also a vital technique that enhances the accuracy of estimating expectation values, utilizing parameterized quantum circuits combined with neural networks. This hybrid approach typically requires the implementation of several controlled gates before measurements and post-processing. However, implementing controlled gates within the DAQC framework poses challenges. To address this, we propose a hybrid architecture that integrates DAQC with neural networks, substituting controlled gates with the classical shadow method. Our approach maintains a polynomial measurement cost as long as the number of qubits with Pauli X or Y operators in each term of the target Hamiltonian is constant or O ( log N q ) , where N q denotes the total number of qubits. We demonstrate the efficacy of our method through numerical simulations, applying it to estimate the ground-state energy of the H2 molecule.
We explore a pulse-based variational quantum eigensolver (VQE) algorithm for Rydberg atoms in optical tweezer arrays and evaluate its performance on prototypical quantum spin models. We numerically demonstrate that the ground states of the one-dimensional antiferromagnetic Heisenberg model and the mixed-field Ising model can be accurately prepared using an adaptive update algorithm that randomly segments pulse sequences, for systems of up to ten qubits. Furthermore, we propose and validate a hybrid scheme that integrates this pulse-level analog quantum algorithm with a variational quantum gate approach, where digital quantum gates are approximated by optimized analog pulses. This enables efficient measurement of the cost function for target many-body Hamiltonians.
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For the variational quantum eigensolver we propose to generate trial wavefunctions from a small amount of selected Pauli terms of the problem Hamiltonian. Two different approaches, one inspired by the quantum approximate optimization algorithm and the other by imaginary-time evolution, are proposed and studied in detail. Using numerical calculations, we study the efficiency of these trial wavefunctions for finding the ground-state energy of three molecules: H2, LiH and H2O. We find that only a small number of Pauli terms are needed to reach chemical accuracy, leading to short-depth quantum circuits with a small number of variational parameters. For the LiH molecule, the quantum circuit consists of 36 two-qubit gates, 45 one-qubit gates, and four variational parameters, with a favorable scaling for larger molecules.
Abstract We propose hybrid digital–analog (DA) learning algorithms on Rydberg atom arrays, combining the potentially practical utility and near-term realizability of quantum learning with the rapidly scaling architectures of neutral atoms. Our construction requires only single-qubit operations in the digital setting and global driving according to the Rydberg Hamiltonian in the analog setting. We perform a comprehensive numerical study of our algorithm on both classical and quantum data, given respectively by handwritten digit classification and unsupervised quantum phase boundary learning. We show in the two representative problems that DA learning is not only feasible in the near term, but also requires shorter circuit depths and is more robust to realistic error models as compared to digital learning schemes. Our results suggest that DA learning opens a promising path towards improved variational quantum learning experiments in the near term.