37,281 research outputs found
Binary search trees for generalized measurement
Generalized quantum measurements (POVMs or POMs) are important for optimally
extracting information for quantum communication and computation. The standard
realization via the Neumark extension requires extensive resources in the form
of operations in an extended Hilbert space. For an arbitrary measurement, we
show how to construct a binary search tree with a depth logarithmic in the
number of possible outcomes. This could be implemented experimentally by
coupling the measured quantum system to a probe qubit which is measured, and
then iterating.Comment: 5 pages, 4 figure
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 , where is the dimension of the
search space, whereas any classical algorithm necessarily scales as . 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 , with
a constant 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 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
Quantum walk speedup of backtracking algorithms
We describe a general method to obtain quantum speedups of classical
algorithms which are based on the technique of backtracking, a standard
approach for solving constraint satisfaction problems (CSPs). Backtracking
algorithms explore a tree whose vertices are partial solutions to a CSP in an
attempt to find a complete solution. Assume there is a classical backtracking
algorithm which finds a solution to a CSP on n variables, or outputs that none
exists, and whose corresponding tree contains T vertices, each vertex
corresponding to a test of a partial solution. Then we show that there is a
bounded-error quantum algorithm which completes the same task using O(sqrt(T)
n^(3/2) log n) tests. In particular, this quantum algorithm can be used to
speed up the DPLL algorithm, which is the basis of many of the most efficient
SAT solvers used in practice. The quantum algorithm is based on the use of a
quantum walk algorithm of Belovs to search in the backtracking tree. We also
discuss how, for certain distributions on the inputs, the algorithm can lead to
an exponential reduction in expected runtime.Comment: 23 pages; v2: minor changes to presentatio
Quantum Robots and Environments
Quantum robots and their interactions with environments of quantum systems
are described and their study justified. A quantum robot is a mobile quantum
system that includes a quantum computer and needed ancillary systems on board.
Quantum robots carry out tasks whose goals include specified changes in the
state of the environment or carrying out measurements on the environment. Each
task is a sequence of alternating computation and action phases. Computation
phase activities include determination of the action to be carried out in the
next phase and possible recording of information on neighborhood environmental
system states. Action phase activities include motion of the quantum robot and
changes of neighborhood environment system states. Models of quantum robots and
their interactions with environments are described using discrete space and
time. To each task is associated a unitary step operator T that gives the
single time step dynamics. T = T_{a}+T_{c} is a sum of action phase and
computation phase step operators. Conditions that T_{a} and T_{c} should
satisfy are given along with a description of the evolution as a sum over paths
of completed phase input and output states. A simple example of a task carrying
out a measurement on a very simple environment is analyzed. A decision tree for
the task is presented and discussed in terms of sums over phase paths. One sees
that no definite times or durations are associated with the phase steps in the
tree and that the tree describes the successive phase steps in each path in the
sum.Comment: 30 Latex pages, 3 Postscript figures, Minor mathematical corrections,
accepted for publication, Phys Rev
New Developments in Quantum Algorithms
In this survey, we describe two recent developments in quantum algorithms.
The first new development is a quantum algorithm for evaluating a Boolean
formula consisting of AND and OR gates of size N in time O(\sqrt{N}). This
provides quantum speedups for any problem that can be expressed via Boolean
formulas. This result can be also extended to span problems, a generalization
of Boolean formulas. This provides an optimal quantum algorithm for any Boolean
function in the black-box query model.
The second new development is a quantum algorithm for solving systems of
linear equations. In contrast with traditional algorithms that run in time
O(N^{2.37...}) where N is the size of the system, the quantum algorithm runs in
time O(\log^c N). It outputs a quantum state describing the solution of the
system.Comment: 11 pages, 1 figure, to appear as an invited survey talk at MFCS'201
- …