Spatial search in a honeycomb network

The spatial search problem consists in minimizing the number of steps required to find a given site in a network, under the restriction that only oracle queries or translations to neighboring sites are allowed. In this paper, a quantum algorithm for the spatial search problem on a honeycomb lattice with $N$ sites and torus-like boundary conditions. The search algorithm is based on a modified quantum walk on a hexagonal lattice and the general framework proposed by Ambainis, Kempe and Rivosh is used to show that the time complexity of this quantum search algorithm is $O(\sqrt{N \log N})$.Comment: 10 pages, 2 figures; Minor typos corrected, one Reference added. accepted in Math. Structures in Computer Science, special volume on Quantum Computin

Observation of tunable exchange bias in Sr2_2YbRuO6_6

The double perovskite compound, Sr$_{2}$YbRuO$_{6}$, displays reversal in the orientation of magnetic moments along with negative magnetization due to an underlying magnetic compensation phenomenon. The exchange bias (EB) field below the compensation temperature could be the usual negative or the positive depending on the initial cooling field. This EB attribute has the potential of getting tuned in a preselected manner, as the positive EB field is seen to crossover from positive to negative value above $T_{\mathrm{comp}}$.Comment: 4 Pages, 4 Figure

Spatial quantum search in a triangular network

The spatial search problem consists in minimizing the number of steps required to find a given site in a network, under the restriction that only oracle queries or translations to neighboring sites are allowed. We propose a quantum algorithm for the spatial search problem on a triangular lattice with N sites and torus-like boundary conditions. The proposed algortithm is a special case of the general framework for abstract search proposed by Ambainis, Kempe and Rivosh [AKR05] (AKR) and Tulsi [Tulsi08], applied to a triangular network. The AKR-Tulsi formalism was employed to show that the time complexity of the quantum search on the triangular lattice is O(sqrt(N logN)).Comment: 10 pages, 4 Postscript figures, uses sbc-template.sty, appeared in Annals of WECIQ 2010, III Workshop of Quantum Computation and Quantum Informatio

The quantum correlation between the selection of the problem and that of the solution sheds light on the mechanism of the quantum speed up

In classical problem solving, there is of course correlation between the selection of the problem on the part of Bob (the problem setter) and that of the solution on the part of Alice (the problem solver). In quantum problem solving, this correlation becomes quantum. This means that Alice contributes to selecting 50% of the information that specifies the problem. As the solution is a function of the problem, this gives to Alice advanced knowledge of 50% of the information that specifies the solution. Both the quadratic and exponential speed ups are explained by the fact that quantum algorithms start from this advanced knowledge.Comment: Earlier version submitted to QIP 2011. Further clarified section 1, "Outline of the argument", submitted to Phys Rev A, 16 page

Nested quantum search and NP-complete problems

A quantum algorithm is known that solves an unstructured search problem in a number of iterations of order $\sqrt{d}$, where $d$ is the dimension of the search space, whereas any classical algorithm necessarily scales as $O(d)$. It is shown here that an improved quantum search algorithm can be devised that exploits the structure of a tree search problem by nesting this standard search algorithm. The number of iterations required to find the solution of an average instance of a constraint satisfaction problem scales as $\sqrt{d^\alpha}$, with a constant $\alpha<1$ depending on the nesting depth and the problem considered. When applying a single nesting level to a problem with constraints of size 2 such as the graph coloring problem, this constant $\alpha$ is estimated to be around 0.62 for average instances of maximum difficulty. This corresponds to a square-root speedup over a classical nested search algorithm, of which our presented algorithm is the quantum counterpart.Comment: 18 pages RevTeX, 3 Postscript figure

Policy Initiatives by the Government of India to Accelerate the Growth of Installed Nuclear Power Capacity in the Coming Years

AbstractWhen examined from the point of view of the size of its population and economy, India is not well endowed with energy resources. Studies done by the Department of Atomic Energy indicate that even after exploiting full potential of every available source of energy including nuclear energy, India needs to continue to import energy resources. In this backdrop, an initiative was launched by Government of India to open up international civil nuclear commerce so as to enable India to access natural uranium from international market and to set up nuclear reactors in technical cooperation with other countries. The paper provides details of what has been done so far, ongoing steps and likely growth scenario for nuclear installed capacity in the country