Improved Sample Upper and Lower Bounds for Trace Estimation of Quantum State Powers
May 14, 2025 Β· Declared Dead Β· π Annual Conference Computational Learning Theory
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kean Chen, Qisheng Wang
arXiv ID
2505.09563
Category
quant-ph: Quantum Computing
Cross-listed
cs.IT
Citations
5
Venue
Annual Conference Computational Learning Theory
Last Checked
5 months ago
Abstract
As often emerges in various basic quantum properties such as entropy, the trace of quantum state powers $\operatorname{tr}(Ο^q)$ has attracted a lot of attention. The recent work of Liu and Wang (SODA 2025) showed that $\operatorname{tr}(Ο^q)$ can be estimated to within additive error $\varepsilon$ with a dimension-independent sample complexity of $\widetilde O(1/\varepsilon^{3+\frac{2}{q-1}})$ for any constant $q > 1$, where only an $Ξ©(1/\varepsilon)$ lower bound was given. In this paper, we significantly improve the sample complexity of estimating $\operatorname{tr}(Ο^q)$ in both the upper and lower bounds. In particular: - For $q > 2$, we settle the sample complexity with matching upper and lower bounds $\widetilde Ξ(1/\varepsilon^2)$. - For $1 < q < 2$, we provide an upper bound $\widetilde O(1/\varepsilon^{\frac{2}{q-1}})$, with a lower bound $Ξ©(1/\varepsilon^{\max\{\frac{1}{q-1}, 2\}})$ for dimension-independent estimators, implying there is only room for a quadratic improvement. Our upper bounds are obtained by (non-plug-in) quantum estimators based on weak Schur sampling, in sharp contrast to the prior approach based on quantum singular value transformation and samplizer.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Quantum Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Quantum machine learning: a classical perspective
R.I.P.
π»
Ghosted
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers
R.I.P.
π»
Ghosted
ProjectQ: An Open Source Software Framework for Quantum Computing
R.I.P.
π»
Ghosted
Quantum Recommendation Systems
R.I.P.
π»
Ghosted
Traffic flow optimization using a quantum annealer
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted