Quantum Phase Estimation with Arbitrary Constant-precision Phase Shift Operators

While Quantum phase estimation (QPE) is at the core of many quantum algorithms known to date, its physical implementation (algorithms based on quantum Fourier transform (QFT)) is highly constrained by the requirement of high-precision controlled phase shift operators, which remain difficult to realize. In this paper, we introduce an alternative approach to approximately implement QPE with arbitrary constant-precision controlled phase shift operators. The new quantum algorithm bridges the gap between QPE algorithms based on QFT and Kitaev's original approach. For approximating the eigenphase precise to the nth bit, Kitaev's original approach does not require any controlled phase shift operator. In contrast, QPE algorithms based on QFT or approximate QFT require controlled phase shift operators with precision of at least Pi/2n. The new approach fills the gap and requires only arbitrary constant-precision controlled phase shift operators. From a physical implementation viewpoint, the new algorithm outperforms Kitaev's approach.Comment: 14 pages, 6 figures and 1 tabl

Deep Learning Meets Cognitive Radio: Predicting Future Steps

Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot

A Novel Airborne Self-organising Architecture for 5G+ Networks

Network Flying Platforms (NFPs) such as unmanned aerial vehicles, unmanned balloons or drones flying at low/medium/high altitude can be employed to enhance network coverage and capacity by deploying a swarm of flying platforms that implement novel radio resource management techniques. In this paper, we propose a novel layered architecture where NFPs, of various types and flying at low/medium/high layers in a swarm of flying platforms, are considered as an integrated part of the future cellular networks to inject additional capacity and expand the coverage for exceptional scenarios (sports events, concerts, etc.) and hard-to-reach areas (rural or sparsely populated areas). Successful roll-out of the proposed architecture depends on several factors including, but are not limited to: network optimisation for NFP placement and association, safety operations of NFP for network/equipment security, and reliability for NFP transport and control/signaling mechanisms. In this work, we formulate the optimum placement of NFP at a Lower Layer (LL) by exploiting the airborne Self-organising Network (SON) features. Our initial simulations show the NFP-LL can serve more User Equipment (UE)s using this placement technique.Comment: 5 pages, 2 figures, conference paper in IEEE VTC-Fall 2017, in Proceedings IEEE Vehicular Technology Conference (VTC-Fall 2017), Toronto, Canada, Sep. 201

Identification of multiple-input single-output Hammerstein models using Bezier curves and Bernstein polynomials

AbstractThis paper considers the implementation of Bezier–Bernstein polynomials and the Levenberg–Marquart algorithm for identifying multiple-input single-output (MISO) Hammerstein models consisting of nonlinear static functions followed by a linear dynamical subsystem. The nonlinear static functions are approximated by the means of Bezier curves and Bernstein basis functions. The identification method is based on a hybrid scheme including the inverse de Casteljau algorithm, the least squares method, and the Levenberg–Marquart (LM) algorithm. Furthermore, results based on the proposed scheme are given which demonstrate substantial identification performance

The sound of getting rid of coronavirus by RNA interference technology: RNAi against COVID-19

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Efficient quantum processing of ideals in finite rings

Suppose we are given black-box access to a finite ring R, and a list of generators for an ideal I in R. We show how to find an additive basis representation for I in poly(log |R|) time. This generalizes a recent quantum algorithm of Arvind et al. which finds a basis representation for R itself. We then show that our algorithm is a useful primitive allowing quantum computers to rapidly solve a wide variety of problems regarding finite rings. In particular we show how to test whether two ideals are identical, find their intersection, find their quotient, prove whether a given ring element belongs to a given ideal, prove whether a given element is a unit, and if so find its inverse, find the additive and multiplicative identities, compute the order of an ideal, solve linear equations over rings, decide whether an ideal is maximal, find annihilators, and test the injectivity and surjectivity of ring homomorphisms. These problems appear to be hard classically.Comment: 5 page