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Avi Kaufman

Publications and source records attributed to Avi Kaufman.

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Finite size scaling of bitstring probability distributions for Rydberg arrays

We calculate the probabilities $p_{\{n\}}$ of the measured bitstrings $\{n\}$ for the vacuum of Rydberg ladders with $N_q$ atoms. As $N_q$ increases, the $p_{\{n\}}$ decrease but become more dense in the low $p$ region raising the possibility that their smallness could be compensated by their large number. The importance of the low probability states can be estimated from the cumulative probability distribution $\Sigma(p_{\Lambda},N_q)$, which is the probability to observe any state having a probability $p\leq p_{\Lambda}$. For not too large values of $p_{\Lambda}$, it is possible to approximately collapse the $\Sigma(p_{\Lambda},N_q)$ for successive $N_q$ into a function resembling the Fermi function when plotted as a function of $-\ln(p_{\Lambda})$. We show that the number of shots necessary to reduce $\Sigma(p_{\Lambda},N_q)$ to some low enough value grows exponentially with $N_q$. We discuss the implications for calculating observables associated with the vacuum.

quant-ph

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

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.

quant-ph

Improved entanglement entropy estimates from filtered bitstring probabilities

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 $S^{vN}_A$. We show that in many cases these lower bounds can be improved by removing the bitstrings with a probability lower than some value $p_{min}$ 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 $S^{vN}_A$. 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 $S^{vN}_A$. We briefly discuss practical implementations with QuEra's Aquila device.

quant-ph