Representing signals with sparse vectors has a wide range of applications that from image and video coding to shape representation health monitoring. In many real-time requirements, or deal high-dimensional signals, the computational complexity encoder finds plays an important role. Quantum computing recently shown promising speed-ups in learning tasks. this work, we propose quantum version wel...

Mean Centering (MC) is an important data preprocessing technique, which has a wide range of applications in mining, machine learning, and multivariate statistical analysis. When the set large, this process will be time-consuming. In paper, we propose efficient quantum MC algorithm based on block-encoding enables existing algorithms can get rid assumption that original been classically mean-cent...

Ordinary approach to quantum algorithm is based on quantum Turing machine or quantum circuits. It is known that this approach is not powerful enough to solve NP-complete problems. In this paper we study a new approach to quantum algorithm which is a combination of the ordinary quantum algorithm with a chaotic dynamical system. We consider the satisfiability problem as an example of NP-complete ...

We present a quantum adiabatic algorithm for set of 2-satisfiability (Q2SAT) problem, which is generalization (2SAT) problem. For Q2SAT we construct the Hamiltonian similar to that Heisenberg chain. All solutions given problem span subspace degenerate ground states. The adiabatically evolved so system stays in subspace. Our numerical results suggest time complexity our $O(n^{3.9})$ yielding non...

Ohya and Volovich have proposed a new quantum computation model with chaotic amplification to solve the SAT problem, which went beyond usual quantum algorithm. In this paper, we generalize quantum Turing machine by rewriting usual quantum Turing machine in terms of channel transformation. Moreover, we define some computational classes of generalized quantum Turing machine and show that we can t...

The quantum approximate optimization algorithm (QAOA) is a prospective near-term due to its modest circuit depth and promising benchmarks. However, an external parameter required in QAOA could become performance bottleneck. This motivates studies of the landscape search for heuristic ways initialization. In this work we visualize applied MaxCut problem on random graphs, demonstrating that initi...