The paper proposes an improved quantum associative algorithm with distributed query based on model proposed by Ezhov et al.. We introduce two modifications of the query that optimized data retrieval of correct multipatterns simultaneously for any rate of the number of the recognition pattern on the total patterns. Simulation results are given.
This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow fever and dengue) which have several similar signs and symptoms. The memory can distinguish a single infection from a polyinfection. Our model is a combination of the improved versions of the original linear quantum retrieving algorithm proposed by Ventura and the non-linear quantum search algorithm of Abrams and Lloyd. From the given simulation results, it appears that the efficiency of recognition is good when particular signs and symptoms of a disease are inserted given that the linear algorithm is the main algorithm. The non-linear algorithm helps confirm or correct the diagnosis or give some advice to the medical staff for the treatment. So, our QAMDiagnos that has a friendly graphical user interface for desktop and smart-phone is a sensitive and a low-cost diagnostic tool that enables rapid and accurate diagnosis of four tropical diseases.
The model of quantum associative memories (QAM) we propose here consists in simplifying and generalizing that of Rigui Zhou et al. [1] who uses the quantum matrix with the binary decision diagram put forth by David Rosenbaumand [2] and the Abrams and Lloyd's nonlinear search algorithm [3]. Our model gives the possibility to retrieve one of the sought states in multi-values retrieving scheme when a measure on the first register is done in O(c − r) time complexity. It is better than Grover's algorithm and its modified form which need O( 2 n m ) steps when they are used as the retrieval algorithm. n is the number of qubit of the first register and m the number of values x for which f (x) = 1. As the nonlinearity makes the system highly susceptible to noise, an analysis of the influence of the single qubit noise channels on the Nonlinear Search Algorithm of our model of QAM, shows a fidelity of about 0.7 whatever the number of qubits existing in the first register.
In this paper, we investigate the effects of three single-qubit quantum noise channels on the quantum nonlinear search algorithm proposed by Abrams and Lloyd. We focus on the gates of the nonlinear part of the algorithm and consider the case where the quantum noise channel only affects the qubit on which the gate is proceeding. Assuming that quantum noise effects occur after using the gates of the nonlinear part of the algorithm, we find that the quantum nonlinear search algorithm appears to be very resilient to the quantum noise when the desired state does not exist in the first register and is subject to the phase damping channel or the depolarising channel.
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