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This research establishes near-optimal query and sample complexities for estimating the state frame potential, a key metric for quantifying how closely quantum ensembles approximate Haar randomness. By improving upon previous bounds in the query model and introducing a store-and-estimate approach for single-copy access, the work advances fundamental algorithms for assessing quantum state randomness. These theoretical gains directly benefit quantum computing by enabling more efficient verification of ensemble properties without requiring full multi-copy access.
Towards Optimal Quantum Estimators for State Frame Potential
arXiv:2608.24711v1 Announce Type: new Abstract: The state frame potential is a standard diagnostic of how closely a quantum state ensemble approximates Haar randomness. In this work, we study the problem of estimating the state frame potential of order $t$ to within additive error $\varepsilon$ under three progressively weaker access models: (i) query access to a multi-state-preparation oracle, (ii) general sample access, and (iii) single-copy sample access. In the query model, we establish a near-optimal query complexity of $\widetilde{\Theta}(\sqrt{t}/\varepsilon)$, yielding a quadratic improvement in the dependence on $t$ over the previous best result of Nakata, Takeuchi, Kliesch, and Darmawan (PRX Quantum 2025). In the general sample model, we establish the optimal sample complexity $\Theta(t/\varepsilon^2)$. In the single-copy sample model, we present a store-and-estimate approach whose sample complexity depends on the R\'enyi entropy of the ensemble weights. As an application, we use the single-copy algorithm to assess the randomness of projected state ensembles, where the entropy term becomes the observational R\'enyi entropy associated with measuring one subsystem.
Abstract:The state frame potential is a standard diagnostic of how closely a quantum state ensemble approximates Haar randomness. In this work, we study the problem of estimating the state frame potential of order $t$ to within additive error $\varepsilon$ under three progressively weaker access models: (i) query access to a multi-state-preparation oracle, (ii) general sample access, and (iii) single-copy sample access. In the query model, we establish a near-optimal query complexity of $\widetilde{\Theta}(\sqrt{t}/\varepsilon)$, yielding a quadratic improvement in the dependence on $t$ over the previous best result of Nakata, Takeuchi, Kliesch, and Darmawan (PRX Quantum 2025). In the general sample model, we establish the optimal sample complexity $\Theta(t/\varepsilon^2)$. In the single-copy sample model, we present a store-and-estimate approach whose sample complexity depends on the Rényi entropy of the ensemble weights. As an application, we use the single-copy algorithm to assess the randomness of projected state ensembles, where the entropy term becomes the observational Rényi entropy associated with measuring one subsystem.
arXiv:2608.24711 [quant-ph]
(or arXiv:2608.24711v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.24711 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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