Quantum neural network
However, typical research in quantum neural networks involves combining classical artificial neural network models (which are widely used in machine learning for the important task of pattern recognition) with the advantages of quantum information in order to develop more efficient algorithms.[3][4][5] One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications.This structure is trained on which path to take similar to classical artificial neural networks.[6] Quantum neural network research is still in its infancy, and a conglomeration of proposals and ideas of varying scope and mathematical rigor have been put forward.Most of them are based on the idea of replacing classical binary or McCulloch-Pitts neurons with a qubit (which can be called a “quron”), resulting in neural units that can be in a superposition of the state ‘firing’ and ‘resting’.A lot of proposals attempt to find a quantum equivalent for the perceptron unit from which neural nets are constructed.Interactions between neurons can be controlled quantumly, with unitary gates, or classically, via measurement of the network states.[14] Most learning algorithms follow the classical model of training an artificial neural network to learn the input-output function of a given training set and use classical feedback loops to update parameters of the quantum system until they converge to an optimal configuration.[15] Quantum neural networks can be applied to algorithmic design: given qubits with tunable mutual interactions, one can attempt to learn interactions following the classical backpropagation rule from a training set of desired input-output relations, taken to be the desired output algorithm's behavior.[19][20][21] Both memories can store an exponential (in terms of n qubits) number of patterns but can be used only once due to the no-cloning theorem and their destruction upon measurement.Trugenberger,[20] however, has shown that his proababilistic model of quantum associative memory can be efficiently implemented and re-used multiples times for any polynomial number of stored patterns, a large advantage with respect to classical associative memories.A substantial amount of interest has been given to a “quantum-inspired” model that uses ideas from quantum theory to implement a neural network based on fuzzy logic.For a quantum neural network, the cost function is determined by measuring the fidelity of the outcome state (In the present NISQ era, this is one of the problems that have to be solved if more applications are to be made of the various VQA algorithms, including QNN.
computational neural networkquantum mechanicsSubhash Kakquantum mindartificial neural networkquantum informationbig data applicationsquantum computingquantum parallelisminterferenceentanglementfeed-forwardartificial neural networksMcCulloch-Pitts neuronssuperpositionperceptroncircuit-based model of quantum computationquantum phase estimation algorithmunitarymeasurementphotonicallyreservoir computingtraining setqubitsbackpropagationcircuit-based quantum computerassociative memoryHopfield neural networksGrover-like quantum search algorithmfuzzy logicno-cloning theoremfan-outcomputational basisAncilla bitreversibilityDifferentiable programmingOptical neural networkHolographic associative memoryQuantum cognitionQuantum machine learningBibcodeInternational Journal of Circuit Theory and ApplicationsCiteSeerXQuantum information scienceDiVincenzo's criteriaNISQ eratimelineQuantum programmingQuantum simulationphysical vs. logicalQuantum processorscloud-basedBell'sEastin–KnillGleason'sGottesman–KnillHolevo'sNo-broadcastingNo-cloningNo-communicationNo-deletingNo-hidingNo-teleportationQuantum speed limitThresholdSolovay–KitaevPurificationClassical capacityentanglement-assistedquantum capacityEntanglement distillationMonogamy of entanglementQuantum channelquantum networkQuantum teleportationquantum gate teleportationSuperdense codingQuantum cryptographyPost-quantum cryptographyQuantum coin flippingQuantum moneyQuantum key distributionSARG04other protocolsQuantum secret sharingQuantum algorithmsAmplitude amplificationBernstein–VaziraniBoson samplingDeutsch–JozsaGrover'sHidden subgroupQuantum annealingQuantum countingQuantum Fourier transformQuantum optimizationQuantum phase estimationShor'sSimon'sQuantumcomplexity theoryPostBQPQuantum supremacyQuantum volumeRandomized benchmarkingRelaxation timescomputing modelsAdiabatic quantum computationContinuous-variable quantum informationOne-way quantum computercluster stateQuantum circuitquantum logic gateQuantum Turing machineTopological quantum computerQuantumerror correctionquantum convolutionalstabilizerBacon–ShorSteaneQuantum opticsCavity QEDCircuit QEDLinear optical QCKLM protocolUltracold atomsNeutral atom QC