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arXiv:2308.02647 · Physical Review A

Enhancing variational Monte Carlo simulations using a programmable quantum simulator

M. Schuyler Moss, Sepehr Ebadi, Tout T. Wang, Giulia Semeghini, Annabelle Bohrdt, Mikhail D. Lukin, Roger G. Melko

Published Mar 11, 2024• 17 citations
arXivHTMLPDF

Abstract

Programmable quantum simulators based on Rydberg atom arrays are a fast-emerging quantum platform, bringing together long coherence times, high-fidelity operations, and large numbers of interacting qubits deterministically arranged in flexible geometries. Today's Rydberg array devices are demonstrating their utility as quantum simulators for studying phases and phase transitions in quantum matter. In this paper, we show that unprocessed and imperfect experimental projective measurement data can be used to enhance in silico simulations of quantum matter, by improving the performance of variational Monte Carlo simulations. As an example, we focus on data spanning the disordered-to-checkerboard transition in a $16\ifmmode\times\else\texttimes\fi{}16$ square-lattice array [S. Ebadi et al., Nature (London) 595, 227 (2021)] and employ the data-enhanced variational Monte Carlo algorithm to train powerful autoregressive wave-function ans\"atze based on recurrent neural networks (RNNs). We observe universal improvements in the convergence times of our simulations with this hybrid training scheme. Notably, we also find that pretraining with experimental data enables relatively simple RNN ans\"atze to accurately capture phases of matter that are not learned with a purely variational training approach. Our work highlights the promise of hybrid quantum-classical approaches for large-scale simulation of quantum many-body systems, combining autoregressive language models with experimental data from existing quantum devices.

DOI
10.1103/physreva.109.032410

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Programmable quantum simulators based on Rydberg atom arrays are a fast-emerging quantum platform, bringing together long coherence times, high-fidelity operations, and large numbers of interacting qubits deterministically arranged in flexible geometries. Today's Rydberg array devices are demonstrating their utility as quantum simulators for studying phases and phase transitions in quantum matter. In this paper, we show that unprocessed and imperfect experimental projective measurement data can be used to enhance in silico simulations of quantum matter, by improving the performance of variational Monte Carlo simulations. As an example, we focus on data spanning the disordered-to-checkerboard transition in a 16\ifmmode×\else\texttimes\fi1616\ifmmode\times\else\texttimes\fi{}1616\ifmmode×\else\texttimes\fi16 square-lattice array [S. Ebadi et al., Nature (London) 595, 227 (2021)] and employ the data-enhanced variational Monte Carlo algorithm to train powerful autoregressive wave-function ans"atze based on recurrent neural networks (RNNs). We observe universal improvements in the convergence times of our simulations with this hybrid training scheme. Notably, we also find that pretraining with experimental data enables relatively simple RNN ans"atze to accurately capture phases of matter that are not learned with a purely variational training approach. Our work highlights the promise of hybrid quantum-classical approaches for large-scale simulation of quantum many-body systems, combining autoregressive language models with experimental data from existing quantum devices.
Rydberg atom arrays are programmable quantum simulators capable of preparing interacting qubit systems in a variety of quantum states. Due to long experimental preparation times, obtaining projective measurement data can be relatively slow for large arrays, which poses a challenge for state reconstruction methods such as tomography. Today, novel ground-state wave-function Ans\"atze like recurrent neural networks (RNNs) can be efficiently trained not only from projective measurement data, but also through Hamiltonian-guided variational Monte Carlo (VMC). In this paper, we demonstrate how pretraining modern RNNs on even small amounts of data significantly reduces the convergence time for a subsequent variational optimization of the wave function. This suggests that essentially any amount of measurements obtained from a state prepared in an experimental quantum simulator could provide significant values for neural-network-based VMC strategies.
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Machine learning has emerged as a promising approach to unveil properties of many-body systems. Recently proposed as a tool to classify phases of matter, the approach relies on classical simulation methods---such as Monte Carlo---which are known to experience an exponential slowdown when simulating certain quantum systems. To overcome this slowdown while still leveraging machine learning, we propose a variational quantum algorithm which merges quantum simulation and quantum machine learning to classify phases of matter. Our classifier is directly fed labeled states recovered by the variational quantum eigensolver algorithm, thereby avoiding the data-reading slowdown experienced in many applications of quantum enhanced machine learning. We propose families of variational ansatz states that are inspired directly by tensor networks. This allows us to use tools from tensor network theory to explain properties of the phase diagrams the presented quantum algorithm recovers. Finally, we propose a majority vote quantum classifier built from a nearest-neighbor (checkerboard) quantum neural network. This classifier is successfully trained to recognize phases of matter with 99% accuracy for the transverse field Ising model and 94% accuracy for the XXZXXZXXZ model. These findings suggest that a merger between quantum simulation and quantum enhanced machine learning offers a fertile ground to develop computational insights into quantum systems.
Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets. In particular, data-centric approaches lead to the possibility of automatically discovering structures in experimental datasets that manual inspection may miss. Here, we introduce an interpretable unsupervised-supervised hybrid machine learning approach, the hybrid-correlation convolutional neural network (hybrid-CCNN), and apply it to experimental data generated using a programmable quantum simulator based on Rydberg atom arrays. Specifically, we apply hybrid-CCNN to discover and identify new quantum phases on square lattices with programmable interactions. The initial unsupervised dimensionality reduction and clustering stage first reveals five distinct quantum phase regions. In a second supervised stage, we refine these phase boundaries and seek insights into the phases by training multiple CCNN classifiers. A learned spatial weighting, introduced to the CCNNs in this work, enables discovery of spatial structure at scales beyond the filter size. The characteristic spatial weightings and snippets of correlations specifically recognized in each phase capture quantum fluctuations in the striated phase and identify a previously undetected boundary-ordered phase as well as motifs of more exotic ordered phases. These observations demonstrate that a combination of programmable quantum simulators with machine learning can be used as a powerful tool for detailed exploration of correlated quantum states of matter.
Programmable quantum systems provide insights into many-body quantum phenomena and enable new methods for computation and metrology. The frontier challenge for these systems is scaling up in the number of quantum particles while retaining a high degree of quantum control. In recent years arrays of neutral atoms have emerged as a scalable and powerful platform for tackling these challenges, thanks to their large number of qubits, flexible geometry, and a well-established toolbox for preparation, detection, coherent control. At the forefront of quantum science and engineering, these systems have been used for quantum simulation of various spin models and have also demonstrated building blocks of quantum computation. In this thesis I will present advances in development of large two-dimensional atom arrays, where hundreds of individual atoms are trapped, rearranged, and initialized in various defect-free arrangements in one or two dimensions. Afterwards, interactions between atoms is controlled via coherent excitation to highly excited Rydberg states, resulting in strong long-range van der Waals forces. We utilize these interaction for quantum simulation of many-body spin Hamiltonians by demonstrating preparation of several exotic phases of matter, study phase transitions, and probe non-equilibrium dynamics. Further, we study the quantum speedup in solving a hard combinatorial optimization problem via efficiently mapping the problem onto the Rydberg atom array. We also utilize Rydberg interactions to demonstrate high-fidelity two-qubit gate operations in a reconfigurable atom array architecture. Lastly I will demonstrate recent technical improvements that have enabled larger system sizes, higher fidelity rydberg excitations, and local control of individual atoms, paving the way for novel approaches for studying quantum many-body phenomena.
Accurately determining the ground-state properties of quantum many-body systems remains a central challenge. In this work, we introduce an autoregressive projective quantum Monte Carlo (PQMC) framework that leverages recurrent neural networks (RNNs) to guide the stochastic dynamics. By incorporating autoregressive sampling into PQMC, we demonstrate substantial improvements in accuracy compared to standard unguided PQMC, while retaining polynomial computational cost. We benchmark our approach against conventional variational RNN ansätze and find that the autoregressive PQMC consistently achieves lower energies and higher fidelity, regardless of system size or whether the Hamiltonian is Hermitian or non-Hermitian. Our results highlight the versatility and power of neural-guided PQMC methods, paving the way for promising scalable simulations of low-energy states in complex quantum many-body systems.