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arXiv:2507.14128

Diagnosing Device Performance in Rydberg-Ladder Gauge Simulators with Cumulative Probabilities and Filtered Mutual Information

Avi Kaufman, Muhammad Asaduzzaman, Zane Ozzello, Blake Senseman, James Corona, Yannick Meurice

Published Jul 18, 2025• 0 citations
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Abstract

We study bitstring measurements from the publicly available Aquila Rydberg-atom platform using a two-leg ladder that encodes a truncated lattice gauge model as a practical benchmark that can be directly implemented and simulated on current hardware. Our goal is diagnostic: we analyze how errors propagate into bitstring probability distributions and downstream information measures, focusing on ladders with 6, 8, and 10 rungs and $\mathcal{O}(10^3)$ shots. We introduce cumulative probability distributions as a compact way to compare Aquila data with high-accuracy density matrix renormalization group (DMRG) and exact references, and we use optimally filtered mutual information primarily as a robust device-data diagnostic rather than a direct entanglement estimator. By isolating finite sampling, sorting fidelity, adiabatic ramp-up, Rabi-frequency ramp-down, and readout errors, we find that readout mitigation performs well in controlled DMRG tests. Applying the same procedure on hardware shows accuracy limitations for the leading probabilities estimation, indicating that readout errors are not dominant and that residual error is instead driven by imperfect state preparation.

DOI
10.48550/arxiv.2507.14128

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We study bitstring measurements from the publicly available Aquila Rydberg-atom platform using a two-leg ladder that encodes a truncated lattice gauge model as a practical benchmark that can be directly implemented and simulated on current hardware. Our goal is diagnostic: we analyze how errors propagate into bitstring probability distributions and downstream information measures, focusing on ladders with 6, 8, and 10 rungs and O(103)\mathcal{O}(10^3)O(103) shots. We introduce cumulative probability distributions as a compact way to compare Aquila data with high-accuracy density matrix renormalization group (DMRG) and exact references, and we use optimally filtered mutual information primarily as a robust device-data diagnostic rather than a direct entanglement estimator. By isolating finite sampling, sorting fidelity, adiabatic ramp-up, Rabi-frequency ramp-down, and readout errors, we find that readout mitigation performs well in controlled DMRG tests. Applying the same procedure on hardware shows accuracy limitations for the leading probabilities estimation, indicating that readout errors are not dominant and that residual error is instead driven by imperfect state preparation.
Using the bitstring probabilities of ground states of bipartitioned ladders of Rydberg atoms, we calculate the mutual information, which is a lower bound on the corresponding bipartite von Neumann quantum entanglement entropy SAvNS^{vN}_ASAvN​. We show that in many cases these lower bounds can be improved by removing the bitstrings with a probability lower than some value pminp_{min}pmin​ and renormalizing the remaining probabilities (filtering). We propose a heuristic based on the change of the conditional entropy under filtering that very effectively improves the estimate of SAvNS^{vN}_ASAvN​. We consider various sizes, lattice spacings and bipartitions. Our numerical investigation suggest that the filtered mutual information obtained with samples having just a few thousand bitstrings can provide reasonably close estimates of SAvNS^{vN}_ASAvN​. We briefly discuss practical implementations with QuEra's Aquila device.
Analog quantum simulators promise access to complex many-body dynamics, yet their performance is ultimately set by how device imperfections compete with intrinsic physical mechanisms. Here we present an end-to-end study of correlation spreading in a programmable Rydberg-atom chain realizing a longitudinal-field transverse-field Ising model, focusing on the joint impact of confinement and effective disorder. Experiments performed on QuEra's Aquila quantum processor are benchmarked against large-scale coherent emulations using the Juelich Quantum Annealing Simulator (JUQAS), enabling the controlled inclusion of realistic hardware imperfections. In the ideal coherent limit, a tunable longitudinal field induces confinement of domain-wall excitations into mesonic bound states, leading to a progressive truncation of the correlation light cone. When experimentally relevant inhomogeneities and fluctuations are included, correlations instead saturate at finite distance even in the nominally deconfined regime, revealing localization driven by emergent disorder. The close quantitative agreement between noisy emulations and experimental data allows us to attribute the observed saturation to specific hardware error channels and to identify the dominant contribution. Our results establish a practical framework for diagnosing and modeling error-induced localization in Rydberg quantum processors, while demonstrating that confinement remains a robust and programmable mechanism for engineering non-ergodic dynamics on near-term quantum hardware.
Abstract Analog quantum simulators promise access to complex many-body dynamics, yet their performance is ultimately set by how device imperfections compete with intrinsic physical mechanisms. Here we present an end-to-end study of correlation spreading in a programmable Rydberg-atom chain realizing a longitudinal-field transverse-field Ising model, focusing on the joint impact of confinement and effective disorder. Experiments performed on QuEra's \emph{Aquila} quantum processor are benchmarked against large-scale coherent emulations using the J\"ulich Quantum Annealing Simulator (JUQAS), enabling the controlled inclusion of realistic hardware imperfections. In the ideal coherent limit, a tunable longitudinal field induces confinement of domain-wall excitations into mesonic bound states, leading to a progressive truncation of the correlation light cone. When experimentally relevant inhomogeneities and fluctuations are included, correlations instead saturate at finite distance even in the nominally deconfined regime, revealing localization driven by emergent disorder. The close quantitative agreement between noisy emulations and experimental data allows us to attribute the observed saturation to specific hardware error channels and to identify the dominant contribution. Our results establish a practical framework for diagnosing and modeling error-induced localization in Rydberg quantum processors, while demonstrating that confinement remains a robust and programmable mechanism for engineering non-ergodic dynamics on near-term quantum hardware.
We discuss the possibility of estimating experimentally the von Neumann entanglement entropy S A v N of a symmetric bipartite quantum system A B by using the basic measurement counts (bitstrings) for a single copy of a prepared state. Using exact diagonalization and analog simulations performed with the publicly available QuEra facilities for chains and ladders of Rydberg atoms, we calculate the Shannon entropy S A B X associated with the bitstrings of adiabatically prepared ground states and the reduced entropies S A X and S B X obtained from the marginal probabilities in A and B . We then calculate the classical mutual information I A B X = S A X + S B X − S A B X , which is a lower bound on S A v N . We show that for a broad range of lattice spacing and detuning, I A B X is typically 20% below S A v N in regions where S A v N is large and a less close bound in regions where S A v N is low. We argue that this use of the easily available bitstrings provides a robust and efficient way to explore empirically the phase diagram of qubit-based quantum simulators and identify critical regions.
We consider multileg ladders of Rydberg atoms which have been proposed as quantum simulators for the compact Abelian Higgs model (CAHM) in 1+11+11+1 dimensions [Y. Meurice, Phys. Rev. D 104, 094513 (2021)] and modified versions of theses simulators such as triangular prisms. Starting with the physical Hamiltonian for the analog simulator, we construct translation-invariant effective Hamiltonians by integrating over the simulator high-energy states produced by the blockade mechanism when some of the atoms are sufficiently close to each other. Remarkably, for all the simulators considered, the effective Hamiltonians have the three types of terms present for the CAHM (electric field, matter charge, and current energy), but in addition, they have terms quartic in the electric field. For the two-leg ladder, these additional terms cannot be removed by fine-tuning the adjustable parameters of currently available devices. For positive detuning, the new terms create highly degenerate vacua, resulting in a very interesting phase diagram. Using numerical methods, we demonstrate the close correspondence between the physical simulator and the effective description for the ground-state energy and real-time evolution. We discuss the phase diagram at fixed geometry with variable Rabi frequency and detuning, and we show that a rich variety of phases can be reached with potential interest in the context of QCD at finite density. We illustrate how the effective description can be used to design simulators with desirable properties from the point of view of constructing hybrid event generators.
This research paper provides a comprehensive review of quantum simulation methods for lattice gauge theories during the current era of noisy intermediate-scale quantum computing. It focuses on the fundamental framework used to understand strong interactions in high-energy physics and explores how quantum devices can overcome the limitations of traditional computational methods, such as the sign problem found in classical simulations. The manuscript is built around three primary pillars that are critical for near-term progress. The first pillar involves encoding strategies, describing the movement away from simple mapping techniques toward more advanced, compact methods that significantly decrease the number of required quantum bits by focusing only on physically relevant states and exploiting local gauge constraints. The second pillar analyzes specific variational quantum algorithms that use flexible circuit structures to find low-energy states, comparing different approaches based on their circuit depth and suitability for current hardware. The third pillar evaluates various error mitigation techniques used to extract accurate information from imperfect quantum hardware, such as methods that amplify and then remove noise or those that use physical symmetries to detect and discard errors. Additionally, the paper compares different hardware technologies—including those based on superconducting circuits, trapped atoms, and ions—to weigh their respective benefits, such as gate speed and connectivity, against their drawbacks. It also introduces emerging hybrid approaches that combine quantum circuits with artificial neural networks to improve simulation power and state representation. Ultimately, the review identifies pathways toward achieving practical advantages in the field and discusses the challenges that remain for scaling these systems to larger sizes.