Quantum Associative Memory with Improved Distributed Queries

Njafa, Jean Pierre Tchapet; Engo, S. G. Nana; Woafo, P.

doi:10.1007/s10773-012-1237-0

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Further modification [27] to the model of a distributed query is carried out by merging the concept of the memory state Oracle with the binomial function based Oracle. This is depicted in Figure 4.…”

Section: Quantum Associative Searchmentioning

confidence: 99%

QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment

et al. 2021

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With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, which uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover’s search algorithm in two ways, allowing: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX Simulator framework. Our implementation represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design.

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Section: Quantum Associative Searchmentioning

confidence: 99%

QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment

et al. 2021

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“…However, their model still produces non‐negligible probability of irrelevant classification. We have recently put forth an improved model of QAM with distributed query that reduces the probability of this irrelevant classification .…”

Section: Introductionmentioning

confidence: 99%

Concise quantum associative memories with nonlinear search algorithm

Njafa

Engo

2016

Fortschr. Phys.

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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.

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“…New situations like Quantum Hopfield Networks and Quantum Associative memory opened the doors for the development of Quantum Neural Networks (QNN) which are Artificial Neural Networks (ANN) functioning according to quantum laws . Recently, an important model of QuAM has been put forward [11] with distributed query that reduces the probability of irrelevant classification. Rigui et al have recently proposed a model of Ventura's associative memory for multivalued retrieval [12].…”

Section: Introductionmentioning

confidence: 99%

Applications of Singh-Rajput Mes in Recall Operations of Quantum Associative Memory for a Two- Qubit System

Singh

Rajput

2015

Int J Theor Phys

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Recall operations of quantum associative memory (QuAM) have been conducted separately through evolutionary as well as non-evolutionary processes in terms of unitary and non-unitary operators respectively by separately choosing our recently derived maximally entangled states (Singh-Rajput MES) and Bell's MES as memory states for various queries and it has been shown that in each case the choices of Singh-Rajput MES as valid memory states are much more suitable than those of Bell's MES. it has been demonstrated that in both the types of recall processes the first and the fourth states of Singh-Rajput MES are most suitable choices as memory states for the queries '11' and '00' respectively while none of the Bell's MES is a suitable choice as valid memory state in these recall processes. It has been demonstrated that all the four states of Singh-Rajput MES are suitable choice as valid memory states for the queries '1?', '?1', '?0' and '0?' while none of the Bell's MES is suitable choice as the valid memory state for these queries also.

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Quantum Associative Memory with Improved Distributed Queries

Cited by 8 publications

References 10 publications

QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment

QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment

Concise quantum associative memories with nonlinear search algorithm

Applications of Singh-Rajput Mes in Recall Operations of Quantum Associative Memory for a Two- Qubit System

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