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Information causality and its tests for quantum communications. I- Ching Yu Host : Prof. Chi-Yee  Cheung Collaborators: Prof. Feng -Li Lin (NTNU) Prof. Li-Yi Hsu (CYCU). Outline-I. Information Causality (IC) and quantum correlations

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Information causality and its tests for quantum communications

Information causality and its tests for quantumcommunications

I- ChingYu

Host : Prof. Chi-Yee Cheung

Collaborators: Prof. Feng-Li Lin (NTNU)

Prof. Li-Yi Hsu (CYCU)


Outline i
Outline-I

  • Information Causality (IC) and quantum correlations

    • Quantum non-locality

    • No-signaling theory

    • Information Causality (IC)

  • IC and the signal decay theorem

    • IC and signal decay theorem

    • The generalized Tsirelson-type inequality


Outline ii
Outline-II

  • Testing IC for general quantum communication protocols

    • Feasibility for maximizing mutual information by convex optimization?

    • Solutions

    • Solution (i):

    • Solution (ii):

  • The conclusion


Quantum non locality i the violation of the bell type inequality
Quantum non-locality-IThe violation of the Bell-type inequality

The CHSH inequality

The measurement scenario


Quantum non locality ii
Quantum non-locality-II

  • The local hidden variable theory:

If A0, A1, B0, B1=1, -1, then Cxy=<Ax By>;

CHSH=(A0 + A1) B0 + (A0 − A1) B1; |<CHSH>| ≤ 2


Quantum non locality iii
Quantum non-locality-III

  • The maximal amount of quantum violation- Tsirelsonbound

    Since

Why Quantum mechanics cannot be more nonlocal?


No signaling theory i
No-signaling theory-I

  • The speed of the propagating information cannot be faster than the light speed

  • To be specific, despite of any non-local correlations previously shared between Alice and Bob, Alice cannot signal to the distant Bob by her choice of inputs due to the no-signaling theory.


No signaling theory i i
No-signaling theory-I I

  • Does the no-signaling theory limit the quantum non-locality?

  • The PR-box:


Information causality i
Information Causality-I

  • What is Information Causality (IC)?

    In the communication protocol, the information gain cannot exceed the amount of classical communication.


Information causality ii
Information Causality-II

The Random Access Code (RAC) protocol

  • Alice prepares a data base { } in secret.

  • She sends Bob a bit

  • Bob decode Alice’s bit ay by

  • Bob is successful only if

  • i.e.,

IC says total mutual information between Bob’s guess bit β and Alice’s database is bounded by 1, i.e.,

IC is violated!


Information causality iii
Information Causality-III

For binary quantum system

with two measurement settings per side

  • IC is satisfied by quantum mechanics.

  • IC is violated by PR-box.

  • The Tsirelsonbound is consistent with IC.

IC could be the physical principle to distinguish quantum correlations from the non-quantum (non-local) correlations.


Information causality and signal decay theory
Information Causality and signal decay theory


Multi setting rac protocol

Alice encodes her database by x(i+1)=a0+ai, i=0,…,k-1.

Bob encodes his given bit b as a k-1 bits string y.

Multi-setting RAC protocol


Noise parameter
noise parameter

  • Bob’s success probability to guess is

  • Define the coding noise parameter to be

  • The result:

  • The noise parameter and the CHSH inequality


Ic and signal propagation
IC and signal propagation

The signal decay theorem

Binary symmetric channel

Binary symmetric channel

Y

Z

IC yields:

] W. Evans and L. J. Schulman, Proceedings of the 34th Annual Symposium on Foundations of Computer Science, 594 (1993).


Generalized tsirelson type inequalities
Generalized Tsirelson-type inequalities

Using the Cauchy-Schwarz inequality, we can obtain

Multi-setting Tsirelson-type inequalities

  • When k=2,

The Tsirelson inequality


Checking the bound by semidefinite programing sdp
Checking the bound by semidefiniteprograming (SDP)

  • SDP can solve the problem of optimizing a linear function which subject to the constraint that the combination of symmetric matrices is positive semidefinite.

  • We use the same method proposed by Stephanie Wehner to calculate the quantum bound .

It is consistent with the bound from IC !!

S. Wehner, Phys. Rev. A 73, 022110 (2006).



More general quantum communication protocols and ic
More general quantum communication protocols and IC

  • For multi-level quantum communication protocols, IC is satisfied by quantum correlation? saturated?


Feasibility for maximizing mutual information by convex optimization
Feasibility for maximizing mutual information by convex optimization?

  • We use the convex optimization to maximize the mutual information (I) over Alice’s input probabilities and quantum joint probability from NS-box.

  • Minimizing a convex function with the equality or inequality constraints is called convex optimization.


The solutions
The solutions optimization?

  • We could find a concave function: the Bell-type functionwhich is monotonically increasing to the mutual information (I) and maximize it over all quantum joint probability of NS-box and Alice’s input probabilities.

  • Brutal force : without relying on convex optimization. We have to pick up all the sets of quantum and input marginal probability and then evaluate the corresponding mutual information (I).


The bell type function i
The optimization?Bell-type function-I

Multi-level RAC protocol

The signal decay theorem for di-nary channel

If , Bob can guess perfectly.

  • The symmetric channel


The bell type function i i
The optimization?Bell-type function-I I

  • Using the Cauchy-Schwarz inequality, we can obtain

  • If , and is uniform, we then prove the mutual information (I) is monotonically increasing with the noise parameter .


Finding the quantum bound and maximal mutual information
Finding the quantum bound and Maximal mutual information optimization?

  • Using the quantum constraints of the joint probabilities of NS-box proposed by

    One can write down the constraints in convex optimization problem and find the quantum bound of the Bell-type function.

    Moreover one can calculate the associated mutual information.

    Result: The associated maximal mutual information is less than the bound from information causality.

Quantum mechanics satisfies IC


The solutions1
The solutions optimization?

  • We could find a concave function which is monotonically increasing to the mutual information (I) and then evaluate the corresponding mutual information (I).

    For example: the object of quantum non-locality.

  • Brutal force : without relying on convex optimization. We have to pick up all the sets of quantum correlation and input marginal probability and then evaluate the corresponding mutual information (I).


Testing ic for different cases
Testing IC for different optimization?cases

  • Fixed input: symmetric channels with i.i.d. and uniform input marginal probabilities

  • Fixed joint probability: with non-uniform input marginal probabilities.

  • The most general case.


The condition for d 2 k 2 quantum correlations
The condition for d=2 k=2 quantum correlations optimization?

  • The necessary and sufficient condition for correlation functions


Symmetric channels with i i d and uniform input marginal probabilities
Symmetric optimization?channels with i.i.d. and uniform input marginal probabilities

  • The non-locality is characterized by the CHSH function.

  • The red part can be achieved also by sharing the local correlation.

  • The maximal mutual information for the local or quantum correlations is bound by 1. IC is saturated.

  • When

  • The mutual information is not monotonically related to the quantum non-locality.

  • The more quantum non-locality may not always yield the more mutual information.

Non-locality


Case with non uniform input marginal probabilities

Symmetric channels optimization?

Asymmetric channels

Case with non-uniform input marginal probabilities


The most general channels
The most general channels optimization?

  • By partitioning the defining domains of the probabilities into 100 points.

  • We find

  • IC is saturated.


The conclusion
The conclusion optimization?

  • We combine IC and the signal decay theorem and then obtain a series of Tsirelson-type inequalities for two-level and bi-partite quantum systems.

  • For the quantum communication protocols discussed in our work, the IC is never violated. Thus, IC is supported and could be treated as a physical principle to single out quantum mechanics.

  • We also find that the IC is saturated not for the case with the associated Tsirelsonbound but for the case saturating the local bound of the CHSH inequality. Sharing more non-local correlation does not imply the better performance in our communication protocols.



The hierarchical quantum constraints
The hierarchical quantum constraints optimization?

How to know the given joint probabilities

could be reproduced by quantum system?

A: Unless they satisfy a hierarchical quantum constraints.

The quantum constraints of joint probabilities come from the property of projection operators.

  • Hermiticity:

  • Orthogonality:

  • Completeness:

  • Commutativity:


The hierarchical quantum constraints1
The hierarchical quantum constraints optimization?

  • The constraint becomes stronger than the previous step of the hierarchical constraint.

Q3

Q2

Q1


From noisy communication to noisy computation
From noisy communication to noisy computation optimization?

  • von Neumann suggested that the error of the computation should be treated by thermodynamical method as the treatment for the communication in Shannon's work. This means that, for the noisy computation, one should use some information-theoretic methods related to the noisy communication.


Finding the quantum bound and maximal mutual information1
Finding the quantum bound and Maximal mutual information optimization?

The first step of the hierarchical constraints

The second step of the hierarchical constraints

Quantum correlations satisfy IC


Symmetric channels with i i d and uniform input marginal probabilities1
Symmetric channels with optimization?i.i.d. and uniform input marginal probabilities

The top region

Non-locality

Non-locality


Case with non uniform input marginal probabilities1
Case optimization?with non-uniform input marginal probabilities

Symmetric

Symmetric and


Case with non uniform input marginal probabilities asymmetric channel
Case optimization?with non-uniform input marginal probabilities- asymmetric channel


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