Pith. sign in

REVIEW 8 cited by

Improved Quantum Algorithms 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

arxiv 2203.15993 v1 pith:OXDRPILS submitted 2022-03-30 quant-ph

Improved Quantum Algorithms for Fidelity Estimation

classification quant-ph
keywords quantumfidelitystatesestimationalgorithmscomplexitydimensiongeneral
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Fidelity is a fundamental measure for the closeness of two quantum states, which is important both from a theoretical and a practical point of view. Yet, in general, it is difficult to give good estimates of fidelity, especially when one works with mixed states over Hilbert spaces of very high dimension. Although, there has been some progress on fidelity estimation, all prior work either requires a large number of identical copies of the relevant states, or relies on unproven heuristics. In this work, we improve on both of these aspects by developing new and efficient quantum algorithms for fidelity estimation with provable performance guarantees in case at least one of the states is approximately low-rank. Our algorithms use advanced quantum linear algebra techniques, such as the quantum singular value transformation, as well as density matrix exponentiation and quantum spectral sampling. As a complementary result, we prove that fidelity estimation to any non-trivial constant additive accuracy is hard in general, by giving a sample complexity lower bound that depends polynomially on the dimension. Moreover, if circuit descriptions for the relevant states are provided, we show that the task is hard for the complexity class called (honest verifier) quantum statistical zero knowledge via a reduction to a closely related result by Watrous.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Towards Minimax Estimation of High-Order Functionals by Quantum Arguments

    quant-ph 2026-07 accept novelty 8.0

    Quantum-inspired estimators for F_alpha(P) and F_alpha(rho) achieve optimal sample complexity n ~ alpha and minimax MSE rate alpha/n, improving prior O(alpha^2) bounds.

  2. Quantum state isomorphism problems for groups

    quant-ph 2026-05 unverdicted novelty 8.0

    Quantum state isomorphism under group actions is BQP-hard for pure states across nontrivial groups and QSZK-complete for mixed states with finite groups; Pauli group version is BQP-complete and Clifford is GI-hard, ru...

  3. On estimating operator norm distance, with optimal trace distance estimation when one state is pure

    quant-ph 2026-07 accept novelty 7.0

    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.

  4. Performance Guarantees for Quantum Neural Estimation of Entropies

    quant-ph 2025-11 unverdicted novelty 7.0

    Quantum neural estimators achieve minimax-optimal copy complexity O(|Θ(U)| d / ε²) with sub-Gaussian concentration for measured Rényi relative entropies on density pairs with bounded Thompson metric.

  5. QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation

    quant-ph 2024-10 unverdicted novelty 7.0

    QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.

  6. Polynomial time constructive decision algorithm for multivariable quantum signal processing

    quant-ph 2024-10 unverdicted novelty 7.0

    A polynomial-time classical decision algorithm exactly characterizes which multivariable Laurent polynomial pairs are realizable by M-QSP and supplies a constructive implementation when the answer is yes.

  7. Adaptive identification of low-degree polynomials in quantum singular value transformation: application to nonlinear quantum properties estimation

    quant-ph 2026-06 unverdicted novelty 6.0

    A two-stage adaptive algorithm identifies a task-specific spectral cutoff to enable low-degree QSVT polynomials for nonlinear quantum property estimation, lowering cost versus conservative eigenvalue bounds.

  8. A slightly improved upper bound for quantum statistical zero-knowledge

    quant-ph 2025-12 conditional novelty 5.0

    QSZK and its non-interactive variant NIQSZK stay inside QIP(2)∩co-QIP(2), now with an honest prover that runs in quantum linear space and single-exponential time.