Use of non-adiabatic geometric phase for quantum computing by nuclear magnetic resonance

Geometric phases have stimulated researchers for its potential applications in many areas of science. One of them is fault-tolerant quantum computation. A preliminary requisite of quantum computation is the implementation of controlled logic gates by controlled dynamics of qubits. In controlled dynamics, one qubit undergoes coherent evolution and acquires appropriate phase, depending on the state of other qubits. If the evolution is geometric, then the phase acquired depend only on the geometry of the path executed, and is robust against certain types of errors. This phenomenon leads to an inherently fault-tolerant quantum computation. Here we suggest a technique of using non-adiabatic geometric phase for quantum computation, using selective excitation. In a two-qubit system, we selectively evolve a suitable subsystem where the control qubit is in state |1>, through a closed circuit. By this evolution, the target qubit gains a phase controlled by the state of the control qubit. Using these geometric phase gates we demonstrate implementation of Deutsch-Jozsa algorithm and Grover's search algorithm in a two-qubit system

Topological Computation without Braiding

We show that universal quantum computation can be performed within the ground state of a topologically ordered quantum system, which is a naturally protected quantum memory. In particular, we show how this can be achieved using brane-net condensates in 3-colexes. The universal set of gates is implemented without selective addressing of physical qubits and, being fully topologically protected, it does not rely on quasiparticle excitations or their braiding.Comment: revtex4, 4 pages, 4 figure

Stable and Reliable Computation of Eigenvectors of Large Profile Matrices

Independent eigenvector computation for a given set of eigenvalues of typical engineering eigenvalue problems still is a big challenge for established subspace solution methods. The inverse vector iteration as the standard solution method often is not capable of reliably computing the eigenvectors of a cluster of bad separated eigenvalues. The following contribution presents a stable and reliable solution method for independent and selective eigenvector computation of large symmetric profile matrices. The method is an extension of the well-known and well-understood QR-method for full matrices thus having all its good numerical properties. The effects of finite arithmetic precision of computer representations of eigenvalue/eigenvector solution methods are analysed and it is shown that the numerical behavior of the new method is superior to subspace solution methods