Physical Implementability of Linear Maps and Its Application in Error
Mitigation
- URL: http://arxiv.org/abs/2012.10959v2
- Date: Thu, 2 Dec 2021 08:38:39 GMT
- Title: Physical Implementability of Linear Maps and Its Application in Error
Mitigation
- Authors: Jiaqing Jiang, Kun Wang, Xin Wang
- Abstract summary: We decompose a target linear map into a linear combination of physically implementable operations.
We show this measure is efficiently computable by semidefinite programs.
We endow this measure with an operational meaning within the quantum error mitigation scenario.
- Score: 12.539795808097063
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Completely positive and trace-preserving maps characterize physically
implementable quantum operations. On the other hand, general linear maps, such
as positive but not completely positive maps, which can not be physically
implemented, are fundamental ingredients in quantum information, both in
theoretical and practical perspectives. This raises the question of how well
one can simulate or approximate the action of a general linear map by
physically implementable operations. In this work, we introduce a systematic
framework to resolve this task using the quasiprobability decomposition
technique. We decompose a target linear map into a linear combination of
physically implementable operations and introduce the physical implementability
measure as the least amount of negative portion that the quasiprobability must
pertain, which directly quantifies the cost of simulating a given map using
physically implementable quantum operations. We show this measure is
efficiently computable by semidefinite programs and prove several properties of
this measure, such as faithfulness, additivity, and unitary invariance. We
derive lower and upper bounds in terms of the Choi operator's trace norm and
obtain analytic expressions for several linear maps of practical interests.
Furthermore, we endow this measure with an operational meaning within the
quantum error mitigation scenario: it establishes the lower bound of the
sampling cost achievable via the quasiprobability decomposition technique. In
particular, for parallel quantum noises, we show that global error mitigation
has no advantage over local error mitigation.
Related papers
- Completely positive trace-preserving maps for higher-order unraveling of Lindblad master equations [0.0]
We perform error analyses for diffusive quantum trajectories based on Kraus operators proposed in the literature.
We show analytically that our proposed operator gives the smallest average trace distance to the exact quantum trajectories.
arXiv Detail & Related papers (2024-08-26T08:47:54Z) - An operator learning perspective on parameter-to-observable maps [0.716879432974126]
This paper introduces the Fourier Neural Mappings framework that is able to accommodate finite-dimensional vector inputs or outputs.
A natural question is whether it is more data-efficient to learn the parameter-to-observable (PtO) map end-to-end or first learn the solution operator and subsequently compute the observable from the full-field solution.
arXiv Detail & Related papers (2024-02-08T20:07:47Z) - Fast Shapley Value Estimation: A Unified Approach [71.92014859992263]
We propose a straightforward and efficient Shapley estimator, SimSHAP, by eliminating redundant techniques.
In our analysis of existing approaches, we observe that estimators can be unified as a linear transformation of randomly summed values from feature subsets.
Our experiments validate the effectiveness of our SimSHAP, which significantly accelerates the computation of accurate Shapley values.
arXiv Detail & Related papers (2023-11-02T06:09:24Z) - Realizing Non-Physical Actions through Hermitian-Preserving Map
Exponentiation [1.0255759863714506]
We introduce the Hermitian-preserving mapiation algorithm, which can effectively realize the action of an arbitrary Hermitian-preserving map by encoding its output into a quantum process.
Our findings present a pathway for systematically and efficiently implementing non-physical actions with quantum devices.
arXiv Detail & Related papers (2023-08-15T18:00:04Z) - Scalable Bayesian Meta-Learning through Generalized Implicit Gradients [64.21628447579772]
Implicit Bayesian meta-learning (iBaML) method broadens the scope of learnable priors, but also quantifies the associated uncertainty.
Analytical error bounds are established to demonstrate the precision and efficiency of the generalized implicit gradient over the explicit one.
arXiv Detail & Related papers (2023-03-31T02:10:30Z) - Kernel-based off-policy estimation without overlap: Instance optimality
beyond semiparametric efficiency [53.90687548731265]
We study optimal procedures for estimating a linear functional based on observational data.
For any convex and symmetric function class $mathcalF$, we derive a non-asymptotic local minimax bound on the mean-squared error.
arXiv Detail & Related papers (2023-01-16T02:57:37Z) - The vacuum provides quantum advantage to otherwise simulatable
architectures [49.1574468325115]
We consider a computational model composed of ideal Gottesman-Kitaev-Preskill stabilizer states.
We provide an algorithm to calculate the probability density function of the measurement outcomes.
arXiv Detail & Related papers (2022-05-19T18:03:17Z) - Quantum computation of nonlinear maps [0.0]
We compute a general differentiable invertible nonlinear map on a quantum computer using only linear unitary operations.
More iterations produce spurious echos, which are unavoidable in any finite unitary emulation of generic non-conservative dynamics.
arXiv Detail & Related papers (2021-05-15T23:57:26Z) - Operational applications of the diamond norm and related measures in
quantifying the non-physicality of quantum maps [0.0]
We study the non-physicality of linear maps based on different ways to approximate a given linear map with quantum channels.
We show that for any trace-preserving map, the quantities both reduce to a fundamental distance measure: the diamond norm.
arXiv Detail & Related papers (2021-02-15T19:00:00Z) - Local optimization on pure Gaussian state manifolds [63.76263875368856]
We exploit insights into the geometry of bosonic and fermionic Gaussian states to develop an efficient local optimization algorithm.
The method is based on notions of descent gradient attuned to the local geometry.
We use the presented methods to collect numerical and analytical evidence for the conjecture that Gaussian purifications are sufficient to compute the entanglement of purification of arbitrary mixed Gaussian states.
arXiv Detail & Related papers (2020-09-24T18:00:36Z) - Eigendecomposition-Free Training of Deep Networks for Linear
Least-Square Problems [107.3868459697569]
We introduce an eigendecomposition-free approach to training a deep network.
We show that our approach is much more robust than explicit differentiation of the eigendecomposition.
Our method has better convergence properties and yields state-of-the-art results.
arXiv Detail & Related papers (2020-04-15T04:29:34Z)
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.