Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

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.

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

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 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.

Paper Structure

This paper contains 32 sections, 25 equations, 37 figures, 14 tables.

Table of Contents

  1. Introduction
  2. Rydberg simulators and lattice gauge theory
  3. Mutual information from a single copy
  4. Bitstrings and mutual information
  5. Entanglement entropy estimate from optimal filtration
  6. Cumulative probabilities
  7. Observations of individual states
  8. Cumulative probability distribution
  9. Sampling errors
  10. Volume effects
  11. Sources of Error
  12. Sorting fidelity
  13. Adiabatic ramping up
  14. Rampdown
  15. Mitigation of readout errors
  16. ...and 17 more sections

Figures (37)

  • Figure 1: $S^{vN}_A$, $I_{AB}^X$ and $I_{AB}^X/S^{vN}$ for a 5-rung ladder
  • Figure 2: Effects of the number of rungs on $I_{AB}^X(p_{min})$ for $R_b/a=2.35$, 6,8...22 rungs obtained via exact diagonalization; comparing sigmoid and mid height approach.
  • Figure 3: Cumulative probability distribution for 6 rungs $R_b/a=2.35$ and $\Delta/\Omega$=3.5. We used exact diagonalization compared with $10^9$ DMRG samples.
  • Figure 4: Cumulative probability distribution for 6 rungs $R_b/a=2.35$ and $\Delta/\Omega$=3.5. We used 3 independent DMRG $10^4$ samples obtained by resampling the $10^9$ DMRG samples.
  • Figure 5: Maximum probability versus rung size, with exponential fits of the form $A \cdot e^{-k N_{r}}$.
  • ...and 32 more figures