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International Conference on Micro- and Nano-Electronics (ICMNE)

Multi-qubit controlled NOT gates for artificial intelligence natural languages processing

Alexander Vasiliev, Farid Mansurovich Ablayev, Sergey N. Andrianov, Natalia S. Andrianova, А. А. Калачев

Published Mar 15, 2019• 0 citations
Publisher

Abstract

Natural language processing is efficient using quantum neural networks including multiqubit controlled NOT gates with multiple control qubits and a single target qubit. We propose here a photonic miltiqubit controlled NOT gate based on a multi-wave mixing process in a cavity. Theory of such a multiqubit gate is constructed using input-output formalism. Parameters matching condition is found that must be fulfilled for successful gate operation. Recommendations are given for the construction of quantum neural networks that are able to solve various practical problems of natural language processing.

DOI
10.1117/12.2522447

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Natural language processing is efficient using quantum neural networks including multiqubit controlled NOT gates with multiple control qubits and a single target qubit. We propose here a photonic miltiqubit controlled NOT gate based on a multi-wave mixing process in a cavity. Theory of such a multiqubit gate is constructed using input-output formalism. Parameters matching condition is found that must be fulfilled for successful gate operation. Recommendations are given for the construction of quantum neural networks that are able to solve various practical problems of natural language processing.
The approximation ability of neural networks plays an important role to the result when one uses neural networks to resolve problems.Unfortunately there is no ideal way to construct proper networks with strong approximation ability.A novel construction approach based on the multi-qubits controller-not gates is proposed for neural networks model in this paper.The proposed model consists of three layers where the hidden nodes are the quantum neurons and the output nodes are the common neurons.The quantum neuron consists of the quantum rotation gates and the multi-qubits controllednot gates.The overall memory of input sequences is captured from information feedback of target qubit from output to input in the multi-qubits controlled-not gate.The output of quantum neuron is obtained from the controlled relationship of the multi-qubits controlled-not gate.The L-M learning algorithms are designed in detail based on the basic principles of quantum computation.The features of input sequences can be effectively obtained in two ways of breadth and depth.The experimental results of solitaire forecast show that,when the number of input nodes is close to the length of sequences,the training set recognition rate of the proposed model increases about 8% than the common neural networks,which reveals the effectiveness of the quantum computation for enhancing the approximation capability of the common neural networks.
In recent years, quantum state engineering and quantum information processing using microwave fields and photons have received increasing attention. In addition, multiqubit gates play an important role in quantum information processing. In this work, we propose to encode a photonic qubit via two arbitrary orthogonal eigenstates (with eigenvalues $\ifmmode\pm\else\textpm\fi{}1$, respectively) of the photon-number parity operator. With such encoding, we then present a single-step method to realize a multi-target-qubit controlled-phase gate with one photonic qubit simultaneously controlling $n\ensuremath{-}1$ target photonic qubits, by employing nnn microwave cavities coupled to one superconducting flux qutrit. This proposal can be applied not only to implement nonhybrid multi-target-qubit controlled-phase gates using photonic qubits with various encodings, but also to realize hybrid multi-target-qubit controlled-phase gates using photonic qubits with different encodings. The gate realization requires only a single-step operation. The gate operation time does not increase with the number of target qubits. Because the qutrit remains in the ground state during the entire operation, decoherence from the qutrit is greatly suppressed. As an application, we show how to apply this gate to generate a multicavity Greenberger-Horne-Zeilinger (GHZ) entangled state with general expression. Depending on the specific encodings, we further discuss the preparation of several nonhybrid and hybrid GHZ entangled states of multiple cavities. We numerically investigate the circuit-QED experimental feasibility of creating a three-cavity spin-coherent hybrid GHZ state. This proposal can be extended to accomplish the same tasks in a wide range of physical systems, such as multiple microwave or optical cavities coupled to a three-level natural or artificial atom.