Choi representation of completely positive maps in brief
- URL: http://arxiv.org/abs/2402.12944v2
- Date: Sun, 26 Jan 2025 07:39:30 GMT
- Title: Choi representation of completely positive maps in brief
- Authors: G. Homa, A. Ortega, M. Koniorczyk,
- Abstract summary: The Choi representation of completely positive (CP) maps is often used in the context of quantum information and computation.<n>It is a correspondence between CP maps and quantum states also termed as the Choi-Jamiol kowski isomorphism.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Choi representation of completely positive (CP) maps, i.e. quantum channels is often used in the context of quantum information and computation as it is easy to work with. It is a correspondence between CP maps and quantum states also termed as the Choi-Jamio\l kowski isomorphism. It is especially useful if a parametrization of the set of CP maps is needed in order to consider a general map or optimize over the set of these. Here we provide a brief introduction to this topic, focusing on certain useful calculational techniques which are presented in full detail.
Related papers
- A class of Schwarz qubit maps with diagonal unitary and orthogonal symmetries [0.0]
A class of unital qubit maps displaying diagonal unitary and symmetries is analyzed.
We provide a complete characterization of this class of maps showing intricate relation between positivity, operator Schwarz inequality, and complete positivity.
Our analysis leads to generalization of seminal Fujiwara-Algoet conditions for Pauli quantum channels.
arXiv Detail & Related papers (2024-04-16T20:37:16Z) - Genuine entanglement detection via projection map in multipartite systems [0.0]
We present a formalism to detect genuine multipartite entanglement by considering projection map which is a positive but not completely positive map.
We construct a suitable witness operator based on projection map to certify genuine tripartite entanglement.
arXiv Detail & Related papers (2024-01-05T20:06:42Z) - On the Extremality of the Tensor Product of Quantum Channels [0.0]
We investigate the preservation of extremality under the tensor product.
We prove that extremality is preserved for CPT or UCP maps, but for UCPT it is not always preserved.
arXiv Detail & Related papers (2023-05-09T23:00:48Z) - Unsupervised Learning of Robust Spectral Shape Matching [12.740151710302397]
We propose a novel learning-based approach for robust 3D shape matching.
Our method builds upon deep functional maps and can be trained in a fully unsupervised manner.
arXiv Detail & Related papers (2023-04-27T02:12:47Z) - SegPrompt: Using Segmentation Map as a Better Prompt to Finetune Deep
Models for Kidney Stone Classification [62.403510793388705]
Deep learning has produced encouraging results for kidney stone classification using endoscope images.
The shortage of annotated training data poses a severe problem in improving the performance and generalization ability of the trained model.
We propose SegPrompt to alleviate the data shortage problems by exploiting segmentation maps from two aspects.
arXiv Detail & Related papers (2023-03-15T01:30:48Z) - The tilted CHSH games: an operator algebraic classification [77.34726150561087]
This article introduces a general systematic procedure for solving any binary-input binary-output game.
We then illustrate on the prominent class of tilted CHSH games.
We derive for those an entire characterisation on the region exhibiting some quantum advantage.
arXiv Detail & Related papers (2023-02-16T18:33:59Z) - Entanglement Breaking Rank via Complementary Channels and Multiplicative
Domains [4.588028371034406]
We introduce a new technique to determine if a channel is entanglement breaking and to evaluate entanglement breaking rank.
We show the entanglement breaking and Choi ranks of such channels are equal.
arXiv Detail & Related papers (2022-11-21T23:33:10Z) - Bayesian Learning for Neural Networks: an algorithmic survey [95.42181254494287]
This self-contained survey engages and introduces readers to the principles and algorithms of Bayesian Learning for Neural Networks.
It provides an introduction to the topic from an accessible, practical-algorithmic perspective.
arXiv Detail & Related papers (2022-11-21T21:36:58Z) - Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy
Optimization [46.30376601157526]
We introduce pointwise map smoothness via the Dirichlet energy into the functional map pipeline.
We propose an algorithm for optimizing it efficiently, which leads to high-quality results in challenging settings.
arXiv Detail & Related papers (2022-10-05T14:07:17Z) - Learning Implicit Feature Alignment Function for Semantic Segmentation [51.36809814890326]
Implicit Feature Alignment function (IFA) is inspired by the rapidly expanding topic of implicit neural representations.
We show that IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
Our method can be combined with improvement on various architectures, and it achieves state-of-the-art accuracy trade-off on common benchmarks.
arXiv Detail & Related papers (2022-06-17T09:40:14Z) - Order preserving maps on quantum measurements [1.2891210250935143]
We study the equivalence classes of quantum measurements endowed with the post-processing partial order.
We map this set into a simpler partially ordered set using an order preserving map and investigating the resulting image.
arXiv Detail & Related papers (2022-02-01T19:50:47Z) - Multiway Non-rigid Point Cloud Registration via Learned Functional Map
Synchronization [105.14877281665011]
We present SyNoRiM, a novel way to register multiple non-rigid shapes by synchronizing the maps relating learned functions defined on the point clouds.
We demonstrate via extensive experiments that our method achieves a state-of-the-art performance in registration accuracy.
arXiv Detail & Related papers (2021-11-25T02:37:59Z) - What Image Features Boost Housing Market Predictions? [81.32205133298254]
We propose a set of techniques for the extraction of visual features for efficient numerical inclusion in predictive algorithms.
We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks.
The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors.
arXiv Detail & Related papers (2021-07-15T06:32:10Z) - Optimal radial basis for density-based atomic representations [58.720142291102135]
We discuss how to build an adaptive, optimal numerical basis that is chosen to represent most efficiently the structural diversity of the dataset at hand.
For each training dataset, this optimal basis is unique, and can be computed at no additional cost with respect to the primitive basis.
We demonstrate that this construction yields representations that are accurate and computationally efficient.
arXiv Detail & Related papers (2021-05-18T17:57:08Z) - Guaranteeing Completely Positive Quantum Evolution [2.578242050187029]
We transform an initial NCP map to a CP map through composition with the asymmetric depolarizing map.
We prove that the composition can always be made CP without completely depolarizing in any direction.
We show that asymmetric depolarization has many advantages over SPA in preserving the structure of the original NCP map.
arXiv Detail & Related papers (2021-04-30T20:53:19Z) - Revisiting The Evaluation of Class Activation Mapping for
Explainability: A Novel Metric and Experimental Analysis [54.94682858474711]
Class Activation Mapping (CAM) approaches provide an effective visualization by taking weighted averages of the activation maps.
We propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches.
arXiv Detail & Related papers (2021-04-20T21:34:24Z) - Gravitational Models Explain Shifts on Human Visual Attention [80.76475913429357]
Visual attention refers to the human brain's ability to select relevant sensory information for preferential processing.
Various methods to estimate saliency have been proposed in the last three decades.
We propose a gravitational model (GRAV) to describe the attentional shifts.
arXiv Detail & Related papers (2020-09-15T10:12:41Z) - Relevant OTOC operators: footprints of the classical dynamics [68.8204255655161]
The OTOC-RE theorem relates the OTOCs summed over a complete base of operators to the second Renyi entropy.
We show that the sum over a small set of relevant operators, is enough in order to obtain a very good approximation for the entropy.
In turn, this provides with an alternative natural indicator of complexity, i.e. the scaling of the number of relevant operators with time.
arXiv Detail & Related papers (2020-07-31T19:23:26Z) - Decomposable Pauli diagonal maps and Tensor Squares of Qubit Maps [91.3755431537592]
We show that any positive product of a qubit map with itself is decomposable.
We characterize the cone of decomposable ququart Pauli diagonal maps.
arXiv Detail & Related papers (2020-06-25T16:39:32Z) - Practical construction of positive maps which are not completely
positive [0.0]
This article introduces PnCP, a toolbox for constructing positive maps which are not completely positive.
We show how this package can be applied to the problem of classifying entanglement in quantum states.
arXiv Detail & Related papers (2020-01-05T07:39:44Z)
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.