REVIEW 3 cited by
Quantum Algorithm for Fidelity Estimation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
For two unknown mixed quantum states $\rho$ and $\sigma$ in an $N$-dimensional Hilbert space, computing their fidelity $F(\rho,\sigma)$ is a basic problem with many important applications in quantum computing and quantum information, for example verification and characterization of the outputs of a quantum computer, and design and analysis of quantum algorithms. In this paper, we propose a quantum algorithm that solves this problem in $\operatorname{poly}(\log (N), r, 1/\varepsilon)$ time, where $r$ is the lower rank of $\rho$ and $\sigma$, and $\varepsilon$ is the desired precision, provided that the purifications of $\rho$ and $\sigma$ are prepared by quantum oracles. This algorithm exhibits an exponential speedup over the best known algorithm (based on quantum state tomography) which has time complexity polynomial in $N$.
Forward citations
Cited by 3 Pith papers
-
On estimating operator norm distance, with optimal trace distance estimation when one state is pure
Rank-independent quantum estimators achieve Θ(1/ε) queries for operator-norm (and trace) distance when one state is pure, and Õ(1/ε^{3/2}) queries for general states, proving BQP-completeness.
-
Optimal fidelity estimation when one state is pure via algorithmic Uhlmann transform
When at least one of two quantum states is pure, the Uhlmann fidelity can be estimated with Θ(1/ε) queries and Θ(1/ε²) samples without knowing which state is pure, matching the optimal lower bounds.
-
Quantum similarity learning for anomaly detection
A hybrid Transformer-quantum circuit similarity-learning network reaches AUC 96.1% on simulated di-Higgs anomaly detection, slightly above a classical baseline, with clustering mitigating shot noise.
Discussion (0). Continue with ORCID to comment.