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”.
Please help me write a proper abstract based on the patent claims. CLAIM: 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 |Ck in a community state space (C) associated with said community values space to each of said community subjects; c) assigning by said assignment module a subject state |S in a subject state space (S) associated with an internal state of said subject related to said underlying proposition; d) placing by a graphing module said subject state |S and each said community subject state |Ck 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 PRC based on said mean measurable indication, said community value matrix PRC 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 λ1, λ2 of said community value matrix PRC. 6. The method of claim 2, further comprising: a) estimating a measurable indication modulo said underlying proposition from said subject state |S; b) assigning by said assignment module a subject value matrix PRS based on said measurable indication, said subject value matrix PRS representing an estimated subject value context in which said underlying proposition is contextualized by said subject. 7. The method of claim 6, 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. 8. The method of claim 6, further comprising estimating by a statistics module a degree of incompatibility between said community value matrix PRC representing said social value context in which said underlying proposition is contextualized by said community and said subject value matrix PRS representing said estimated subject value context in which said underlying proposition is contextualized by said subject. 9. The method of claim 8, wherein said degree of incompatibility is quantified by a commutator [PRC, PRS] between said community value matrix PRS and said subject value matrix PRS. 10. The method of claim 6, 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. 11. The method of claim 6, 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. 12. The method of claim 11, 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. 13. 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 |Ck. 14. The method of claim 13, 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. 15. The method of claim 1, wherein said graph comprises vertices and edges, and wherein said subject state |S and each said community subject state |Ck are placed on said vertices according to said surjective mapping. 16. The method of claim 15, wherein said graph comprises a lattice selected from the group consisting of an Ising Model, a Heisenberg Model and a Hubbard Model. 17. The method of claim 16, wherein said lattice is configured to reflect quantum interactions between nearest neighbor vertices. 18. The method of claim 16, wherein said lattice is configured to reflect a chemical potential μ. 19. The method of claim 16, wherein said quantum interactions on said lattice are simulated at a thermodynamic equilibrium by a simulation engine. 20. The method of claim 16, wherein said lattice is immersed in an external field along a global value axis associated with a global contextualization of said underlying proposition. 21. The method of claim 16, wherein said lattice supports lattice hopping. 22. The method of claim 15, wherein said subject and said community subjects are networked and said graph comprises connections between said subject and said community subjects. 23. The method of claim 22, 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. 24. The method of claim 1, wherein at least one of each said community subject state |Ck and said subject state |S is represented by a qubit. 25. 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 |Ck in a community state space (C) associated with said community values space to each of said community subjects; c) placing by a graphing module each said community subject state |Ck on a graph according to a surjective mapping; and d) predicting by a prediction module said quantum state dynamics emerging between a statistically significant number N of said community subjects each assigned subject state |Ck, where k=1, 2, . . . , N, based on quantum interactions on said graph. 26. The method of claim 25, 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 PRC based on said mean measurable indication, said community value matrix PRC representing said social value context in which said underlying proposition is contextualized by said community. 27. The method of claim 26, further comprising predicting by a prediction module a maximum degree of incompatibility between said community value matrix PRC representing said social value context in which said underlying proposition is contextualized by said community and a test subject value matrix PRSt representing an estimated test subject value context in which said underlying proposition is contextualized by said test subject. 28. The method of claim 26, wherein said community is a networked community and said step of measuring said mean measurable indication is performed by a network behavior monitoring unit. 29. The method of claim 25, wherein said surjective mapping reflects quantum spin statistics exhibited by said community subject states |Ck, where k=1, 2, . . . , N. 30. The method of claim 29, 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. 31. The method of claim 25, wherein said graph comprises vertices and edges, and wherein said community subject states |Ck, where k=1, 2, . . . , N, are placed on said vertices according to said surjective mapping. 32. The method of claim 31, wherein said graph comprises a lattice selected from the group consisting of an Ising Model, a Heisenberg Model and a Hubbard Model. 33. The method of claim 32, wherein said lattice is configured to reflect quantum interactions between nearest neighbor vertices. 34. The method of claim 32, wherein said lattice is configured to reflect a chemical potential μ. 35. The method of claim 32, wherein said quantum interactions on said lattice are simulated at a thermodynamic equilibrium by a simulation engine. 36. The method of claim 32, wherein said lattice is immersed in an external field along a global value axis associated with a global contextualization of said underlying proposition. 37. The method of claim 32, wherein said lattice supports lattice hopping. 38. The method of claim 31, wherein said community subjects are networked and said graph comprises edges corresponding to transmit connections, receive connections and transceive connections between said community subject states |Ck representing said community subjects. 39. The method of claim 25, wherein at least one of said community subject states |Ck is represented by a qubit. 40. A computer system for predicting quantum state dynamics of community subjects modulo an underlying proposition contextualized in a social value context, said computer system comprising: a) a creation module for positing a community of said community subjects sharing a community values space; b) an assignment module for assigning a community subject state |Ck in a community state space (C) associated with said community values space to each of said community subjects; c) a graphing module for placing each said community subject state |Ck on a graph according to a surjective mapping; and d) a prediction module for predicting said quantum state dynamics emerging between a statistically significant number N of said community subjects each assigned subject state |Ck, where k=1, 2, . . . , N, based on quantum interactions on said graph. 41. The computer system of claim 40, wherein said graph comprises vertices and edges, whereby each said community subject state |Ck representing said corresponding community subject is placed on said vertices according to said surjective mapping. 42. The computer system of claim 41, wherein said graph comprises a social graph and said community is a networked community. 43. The computer system of claim 41, wherein said graph comprises a lattice selected from the group consisting of an Ising Model, a Heisenberg Model and a Hubbard Model. 44. The computer system of claim 43, further comprising a simulation engine for simulating said quantum interactions on said lattice at a thermodynamic equilibrium. 45. The computer system of claim 43, further comprising an external field simulation module for simulating an external field along a global value axis associated with a global contextualization of said underlying proposition. 46. The computer system of claim 40, wherein said community is a networked community and said computer system further comprises a network behavior monitoring unit for monitoring said networked community. 47. The computer system of claim 40, further comprising a statistics module for estimating said quantum interactions on said graph. 48. The computer system of claim 40, further comprising a prediction module for predicting an outcome of said quantum interactions on said graph. 49. The computer system of claim 40, further comprising a simulation engine for simulating said quantum interactions on said graph. 50. The computer system of claim 40, 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. 51. The computer system of claim 40, further comprising a mapping module for finding said community state space (C) and a subject state space (S) associated with an internal state of a subject related to said underlying proposition. 52. The computer system of claim 40, wherein said creation module, said assignment module, said graphing module and said prediction module are implemented in nodes of a computer cluster. 53. The computer system of claim 40, further comprising a non-volatile memory for storing information about at least one of said community subjects, said assignments of community subject states |Ck, where k=1, 2, . . . , N, said community state space (C) associated with said community values space, said surjective mapping, said graph, said quantum state dynamics and said quantum interactions. 54. The computer system of claim 40, wherein at least one of said community subject states |Ck is represented by a qubit.