On the sample complexity of purity and inner product estimation

October 16, 2024 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Weiyuan Gong, Jonas Haferkamp, Qi Ye, Zhihan Zhang arXiv ID 2410.12712 Category quant-ph: Quantum Computing Cross-listed cs.DS, cs.IT, cs.LG Citations 21 Venue arXiv.org Last Checked 5 months ago
Abstract
We study the sample complexity of the prototypical tasks quantum purity estimation and quantum inner product estimation. In purity estimation, we are to estimate $tr(ρ^2)$ of an unknown quantum state $ρ$ to additive error $Ξ΅$. Meanwhile, for quantum inner product estimation, Alice and Bob are to estimate $tr(ρσ)$ to additive error $Ξ΅$ given copies of unknown quantum state $ρ$ and $Οƒ$ using classical communication and restricted quantum communication. In this paper, we show a strong connection between the sample complexity of purity estimation with bounded quantum memory and inner product estimation with bounded quantum communication and unentangled measurements. We propose a protocol that solves quantum inner product estimation with $k$-qubit one-way quantum communication and unentangled local measurements using $O(median\{1/Ξ΅^2,2^{n/2}/Ξ΅,2^{n-k}/Ξ΅^2\})$ copies of $ρ$ and $Οƒ$. Our protocol can be modified to estimate the purity of an unknown quantum state $ρ$ using $k$-qubit quantum memory with the same complexity. We prove that arbitrary protocols with $k$-qubit quantum memory that estimate purity to error $Ξ΅$ require $Ξ©(median\{1/Ξ΅^2,2^{n/2}/\sqrtΞ΅,2^{n-k}/Ξ΅^2\})$ copies of $ρ$. This indicates the same lower bound for quantum inner product estimation with one-way $k$-qubit quantum communication and classical communication, and unentangled local measurements. For purity estimation, we further improve the lower bound to $Ξ©(\max\{1/Ξ΅^2,2^{n/2}/Ξ΅\})$ for any protocols using an identical single-copy projection-valued measurement. Additionally, we investigate a decisional variant of quantum distributed inner product estimation without quantum communication for mixed state and provide a lower bound on the sample complexity.
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