Quantum state dynamics in a community of subjects assigned quantum states modulo a proposition perceived in a social value context

Patent metadata

Field	Value
Publication number	US-10007884-B2
Application number	US-201414324127-A
Country	US
Kind code	B2
Filing date	Jul 4, 2014
Priority date	Jul 4, 2014
Publication date	Jun 26, 2018
Grant date	Jun 26, 2018

Abstract

The present invention is directed to methods and apparatus for predicting the quantum state, including the dynamics of such quantum state in so far as it represents a subject embedded in a community of community subjects. In the quantum representation adopted herein the internal states of all subjects are assigned to quantum subject states defined with respect to an underlying proposition about an item that can be instantiated by an object, a subject or by an experience. Contextualization of the proposition about the item is identified with a basis (eigenbasis of a spectral decomposition) referred to herein as the social value context. The dynamics are obtained from quantum interactions on a graph onto which the quantum states of all the subjects are mapped by a surjective mapping dictated by subject interconnections and subject-related data, including the social graph and information derived from “big data”.

First claim

The invention claimed is:

1. A computer implemented method for predicting a quantum state of a subject modulo an underlying proposition contextualized by a community in a social value context, said method comprising:
a) positing by a creation module community subjects belonging to said community and sharing a community values space;
b) assigning by an assignment module a community subject state |C k in a community state space (C) associated with said community values space to each of said community subjects;
c) performing by said assignment module the steps of:
i) assigning a subject state |S in a subject state space (S) associated with an internal state of said subject related to said underlying proposition;
ii) estimating a measurable indication modulo said underlying proposition from said subject state;
iii)assigning a subject value matrix PR S based on said measurable indication, said subject value matrix PR S representing an estimated subject value context in which said underlying proposition is contextualized by said subject;

d) placing by a graphing module said subject state |S and each said community subject state |C k on a graph according to a surjective mapping; and
e) predicting by a prediction module said quantum state of said subject state |S based on quantum interactions on said graph.

2. The method of claim 1 , further comprising:
a) measuring a mean measurable indication modulo said underlying proposition exhibited by said community;
b) assigning by said assignment module a community value matrix PR C based on said mean measurable indication, said community value matrix PR C representing said social value context in which said underlying proposition is contextualized by said community.

3. The method of claim 2 , wherein said community is a networked community and said step of measuring said mean measurable indication is performed by a network behavior monitoring unit.

4. The method of claim 2 , wherein said mean measurable indication comprises one of at least two mutually exclusive responses a, b with respect to said underlying proposition.

5. The method of claim 4 , wherein said at least two mutually exclusive responses a, b correspond to at least two eigenvalues λ 1 , λ 2 of said community value matrix PR C .

6. The method of claim 2 , wherein said step of estimating further comprises estimating by a mapping module an overlap between said community state space (C) associated with said community values space and said subject state space (S) associated with said internal state of said subject.

7. The method of claim 2 , further comprising estimating by a statistics module a degree of incompatibility between said community value matrix PR C representing said social value context in which said underlying proposition is contextualized by said community and said subject value matrix PR S representing said estimated subject value context in which said underlying proposition is contextualized by said subject.

8. The method of claim 7 , wherein said degree of incompatibility is quantified by a commutator [PR C ,PR S ] between said community value matrix PR C and said subject value matrix PR S .

9. The method of claim 2 , wherein said step of estimating said measurable indication modulo said underlying proposition from said subject comprises collecting a stream of data related to said internal state of said subject and generated by said subject online.

10. The method of claim 2 , wherein said step of estimating said measurable indication modulo said underlying proposition from said subject comprises collecting a stream of data related to said underlying proposition and generated by said subject online.

11. The method of claim 10 , wherein said underlying proposition is associated with at least one item from the group consisting of a test subject, a test object, a test experience and said step of estimating said measurable indication modulo said underlying proposition from said subject comprises collecting a stream of data related to said at least one item.

12. The method of claim 1 , wherein said surjective mapping reflects quantum spin statistics exhibited by said subject state |S and each said community subject state |C k .

13. The method of claim 12 , wherein said quantum spin statistics comprise one of at least one statistic chosen among a consensus statistic B-E and an anti-consensus statistic F-D.

14. The method of claim 1 , wherein said graph comprises vertices and edges, and wherein said subject state |S and each said community subject state |C k are placed on said vertices according to said surjective mapping.

15. The method of claim 14 , wherein said graph comprises a lattice selected from the group consisting of an Ising Model, a Heisenberg Model and a Hubbard Model.

16. The method of claim 15 , wherein said lattice is configured to reflect quantum interactions between nearest neighbor vertices.

17. The method of claim 15 , wherein said lattice is configured to reflect a chemical potential μ.

18. The method of claim 15 , wherein said quantum interactions on said lattice are simulated at a thermodynamic equilibrium by a simulation engine.

19. The method of claim 15 , wherein said lattice is immersed in an external field along a global value axis associated with a global contextualization of said underlying proposition.

20. The method of claim 15 , wherein said lattice supports lattice hopping.

21. The method of claim 14 , wherein said subject and said community subjects are networked and said graph comprises connections between said subject and said community subjects.

22. The method of claim 21 , wherein said subject and said community subjects are members of a social network and said method further comprises monitoring of interactions between said members with a network behavior monitoring unit.

23. The method of claim 1 , wherein at least one of each said community subject state |C k and said subject state |S is represented by a qubit.

24. A computer implemented method for predicting quantum state dynamics of community subjects modulo an underlying proposition contextualized in a social value context, said method comprising:
a) positing by a creation module a community of said community subjects sharing a community values space;
b) assigning by an assignment module a community subject state |C k in a community state space (C) associated with said community values space to each of said community subjects;
c) measuring a mean measurable indication modulo said underlying proposition exhibited by said community;
d) assigning by said assignment module a community value matrix PR C based on said mean measurable indication, said community value matrix PR C representing said social value context in which said underlying proposition is contextualized by said community;
e)placing by a graphing module each said community subject state |C k on a graph according to a surjective mapping; and
f)predicting by a prediction module said quantum state dynamics emerging between a statistically significant number N of said community subjects each assigned subject state |C k , where k=1, 2, . . . , N, based on quantum interactions on said graph.

25. The method of claim 24 , further comprising predicting by a prediction module a maximum degree of incompatibility between said community value matrix PR C representing said social value context in which s

Assignees

• Invent Ly Llc
• Invent Ly

Inventors

• Alboszta Marek
• Brown Stephen J

Classifications

• G06N99/002 Primary

  Physics · mapped topic

• G06N7/01 Primary

  Probabilistic graphical models, e.g. probabilistic networks · CPC title