Constructing realistic effective spin Hamiltonians with machine learning approaches

Constructing States and Effective Hamiltonians in Lattice QCD

We formulate SU(3) Hamiltonian lattice QCD in terms of the plaquette variables and determine the relevant subspaces of the Hilbert space for the vacuum wave functional and its approximations. We analyze the oneand two-plaquette problems. In an earlier paper [1] we formulated the fully gauge-fixed SU(N) lattice gauge Hamiltonian and studied the one-plaquette problem for several values of N . Thi...

Quantum graphs with spin Hamiltonians

The article surveys quantization schemes for metric graphs with spin. Typically quantum graphs are defined with the Laplace or Schrödinger operator which describe particles whose intrinsic angular momentum (spin) is zero. However, in many applications, for example modeling an electron (which has spin-1/2) on a network of thin wires, it is necessary to consider operators which allow spin-orbit i...

Effective Hamiltonians with Relativistic Corrections

Constructing a Physiologically Realistic Machine Model of Consciousness

There are a set of theoretical bounds on any system which must learn to perform a complex combination of features with limited information handling resources. A cognitive architecture implemented within these bounds has the capability to generate human-like “conscious” behaviours with mechanisms that are physiologically plausible. The Characteristics of a Physiologically Realistic Model of Cons...

Approaches to machine learning

The field of machine learning strives to develop methods and techniques to automate the acquisition of new information, new skills, and new ways of organizing existing information. In this article, we review the major approaches to machine learning in symbolic domains, covering the tasks of learning concepts from examples, learning search methods, conceptual clustering, and language acquisition...