2 research outputs found
A simple quantum algorithm to efficiently prepare sparse states
State preparation is a fundamental routine in quantum computation, for which
many algorithms have been proposed. Among them, perhaps the simplest one is the
Grover-Rudolph algorithm. In this paper, we analyse the performance of this
algorithm when the state to prepare is sparse. We show that the gate complexity
is linear in the number of non-zero amplitudes in the state and quadratic in
the number of qubits. We then introduce a simple modification of the algorithm,
which makes the dependence on the number of qubits also linear. This is
competitive with the best known algorithms for sparse state preparatio
A quantum algorithm for the solution of the 0-1 Knapsack problem
Here we present two novel contributions for achieving quantum advantage in
solving difficult optimisation problems, both in theory and foreseeable
practice. (1) We introduce the ''Quantum Tree Generator'', an approach to
generate in superposition all feasible solutions of a given instance, yielding
together with amplitude amplification the optimal solutions for
--Knapsack problems. The QTG offers exponential memory savings and
enables competitive runtimes compared to the state-of-the-art Knapsack solver
COMBO for instances involving as few as 600 variables. (2) By introducing a
high-level simulation strategy that exploits logging data from COMBO, we can
predict the runtime of our method way beyond the range of existing quantum
platforms and simulators, for various benchmark instances with up to 1600
variables. Combining both of these innovations, we demonstrate the QTG's
potential advantage for large-scale problems, indicating an effective approach
for combinatorial optimisation problems.Comment: 6+9 pages, 7 figure