Quantum matching pursuit: A quantum algorithm for sparse representations

Matching-pursuit for simulations of quantum processes

The matching-pursuit algorithm is implemented to develop an extension of the split-operator Fourier transform method to a nonorthogonal, nonuniform and dynamically adaptive coherent-state representation. The accuracy and efficiency of the computational approach are demonstrated in simulations of deep tunneling and long time dynamics by comparing our simulation results with the corresponding ben...

Optimizations to the Orthogonal Matching Pursuit Algorithm for Sparse Basis Representations of Photometric Redshift Pdfs

A Quantum Algorithm for Closest Pattern Matching

We propose a quantum algorithm for closest pattern matching which allows us to search for as many distinct patterns as we wish in a given string (database), requiring a query function per symbol of the pattern alphabet. This represents a significant practical advantage when compared to Grover’s search algorithm as well as to other quantum pattern matching methods [7], which rely on building spe...

Quantum tunneling dynamics in multidimensional systems: a matching-pursuit description.

Rigorous simulations of quantum tunneling dynamics in model systems with up to 20 coupled degrees of freedom are reported. The simulations implement an extension of the recently developed matching-pursuit/split-operator Fourier-transform method to complex-valued coherent-state representations. The resulting method recursively applies the time-evolution operator, as defined by the Trotter expans...

Sparse Multipath Channel Estimation Using Compressive Sampling Matching Pursuit Algorithm

Wideband wireless channel is a time dispersive channel and becomes strongly frequency-selective. However, in most cases, the channel is composed of a few dominant taps and a large part of taps is approximately zero or zero. To exploit the sparsity of multi-path channel (MPC), two methods have been proposed. They are, namely, greedy algorithm and convex program. Greedy algorithm is easy to be im...