1 research outputs found
Quantum Adiabatic Evolution for Global Optimization in Big Data
Big Data is characterized by Volume, Velocity, Veracity and Complexity. The
interaction between this huge data is complex with an associated free will
having dynamic and non linear nature. We reduced big data based on its
characteristics, conceptually driven by quantum field theory and utilizing the
physics of condensed matter theory in a complex nonlinear dynamic system:
Quantum Topological Field Theory of Data. The model is formulated from the
dynamics and evolution of single datum, eventually defining the global
properties and evolution of collective data space via action, partition
function, green propagators in almost polynomially solvable O(nlogn)
complexity. The simulated results show that the time complexity of our
algorithm for global optimization via quantum adiabatic evolution is almost in
O(logn) Our algorithm first mines the space via greedy approach and makes a
list of all ground state Hamiltonians, then utilizing the tunnelling property
of quantum mechanics optimizes the algorithm unlike up hill and iterative
techniques and doesnot let algorithm to get localized in local minima or sharp
valley due to adiabatic evolution of the system. The loss in quantumness, non
realizable, no clone, noise, decoherence, splitting of energy states due to
electric and magnetic fields, variant to perturbations and less lifetime makes
it inefficient for practical implementation. The inefficiencies of qubit can be
overcome via property that remains invariant to perturbation and Cartesian
independent having well defined mathematical structure. It can be well
addressed via topological field theory of data.Comment: :118 Pages: 2 figures:5 graphs:Conferences 2:Journal Papers 3 under
review. arXiv admin note: text overlap with arXiv:1506.08978,
arXiv:1511.03010, arXiv:0811.2519 by other author