Recoverability for optimized quantum $f$-divergences
- URL: http://arxiv.org/abs/2008.01668v2
- Date: Thu, 30 Sep 2021 22:01:34 GMT
- Title: Recoverability for optimized quantum $f$-divergences
- Authors: Li Gao, Mark M. Wilde
- Abstract summary: We show that the absolute difference between the optimized $f$-divergence and its channel-processed version is an upper bound on how well one can recover a quantum state.
Not only do these results lead to physically meaningful refinements of the data-processing inequality for the sandwiched R'enyi relative entropy, but they also have implications for perfect reversibility.
- Score: 22.04181157631236
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The optimized quantum $f$-divergences form a family of distinguishability
measures that includes the quantum relative entropy and the sandwiched R\'enyi
relative quasi-entropy as special cases. In this paper, we establish physically
meaningful refinements of the data-processing inequality for the optimized
$f$-divergence. In particular, the refinements state that the absolute
difference between the optimized $f$-divergence and its channel-processed
version is an upper bound on how well one can recover a quantum state acted
upon by a quantum channel, whenever the recovery channel is taken to be a
rotated Petz recovery channel. Not only do these results lead to physically
meaningful refinements of the data-processing inequality for the sandwiched
R\'enyi relative entropy, but they also have implications for perfect
reversibility (i.e., quantum sufficiency) of the optimized $f$-divergences.
Along the way, we improve upon previous physically meaningful refinements of
the data-processing inequality for the standard $f$-divergence, as established
in recent work of Carlen and Vershynina [arXiv:1710.02409, arXiv:1710.08080].
Finally, we extend the definition of the optimized $f$-divergence, its
data-processing inequality, and all of our recoverability results to the
general von Neumann algebraic setting, so that all of our results can be
employed in physical settings beyond those confined to the most common
finite-dimensional setting of interest in quantum information theory.
Related papers
- Estimation of Nonlinear Physical Quantities By Measuring Ancillas [0.0]
We present quantum algorithms for estimating von Neumann entropy and Renyi entropy.
Our framework can complete the given task by measuring a small number of ancilla qubits without directly measuring the system.
arXiv Detail & Related papers (2025-02-11T14:15:19Z) - Semidefinite optimization of the quantum relative entropy of channels [3.9134031118910264]
This paper introduces a method for calculating the quantum relative entropy of channels.
It provides efficiently computable upper and lower bounds that sandwich the true value with any desired precision.
arXiv Detail & Related papers (2024-10-21T18:00:01Z) - On the optimal error exponents for classical and quantum antidistinguishability [3.481985817302898]
Antidistinguishability has been used to investigate the reality of quantum states.
We show that the optimal error exponent vanishes to zero for classical and quantum antidistinguishability.
It remains an open problem to obtain an explicit expression for the optimal error exponent for quantum antidistinguishability.
arXiv Detail & Related papers (2023-09-07T14:03:58Z) - Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
We consider a generalization of Shannon entropy from work in statistical decision theory.
We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures.
We then show how alternative choices for the loss yield a flexible family of acquisition functions.
arXiv Detail & Related papers (2022-10-04T04:43:58Z) - Improved Quantum Algorithms for Fidelity Estimation [77.34726150561087]
We develop new and efficient quantum algorithms for fidelity estimation with provable performance guarantees.
Our algorithms use advanced quantum linear algebra techniques, such as the quantum singular value transformation.
We prove that fidelity estimation to any non-trivial constant additive accuracy is hard in general.
arXiv Detail & Related papers (2022-03-30T02:02:16Z) - Tight Exponential Analysis for Smoothing the Max-Relative Entropy and
for Quantum Privacy Amplification [56.61325554836984]
The max-relative entropy together with its smoothed version is a basic tool in quantum information theory.
We derive the exact exponent for the decay of the small modification of the quantum state in smoothing the max-relative entropy based on purified distance.
arXiv Detail & Related papers (2021-11-01T16:35:41Z) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise [51.31435087414348]
It is essential to theoretically guarantee that algorithms provide small objective residual with high probability.
Existing methods for non-smooth convex optimization have complexity bounds with dependence on confidence level.
We propose novel stepsize rules for two methods with gradient clipping.
arXiv Detail & Related papers (2021-06-10T17:54:21Z) - Optimized quantum f-divergences [6.345523830122166]
I introduce the optimized quantum f-divergence as a related generalization of quantum relative entropy.
I prove that it satisfies the data processing inequality, and the method of proof relies upon the operator Jensen inequality.
One benefit of this approach is that there is now a single, unified approach for establishing the data processing inequality for the Petz--Renyi and sandwiched Renyi relative entropies.
arXiv Detail & Related papers (2021-03-31T04:15:52Z) - Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves.
We find both methods provide good estimates of the energy and ground state.
gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
arXiv Detail & Related papers (2020-11-02T19:52:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.