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Gong-De Guo

Publications and source records attributed to Gong-De Guo.

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Authenticated Multiparty Quantum Key Agreement for Optical-Ring Quantum Communication Networks

Quantum communication networks are connected by various devices to achieve communication or distributed computing for users in remote locations. In order to solve the problem of generating temporary session key for secure communication in optical-ring quantum networks, a quantum key agreement protocol is proposed. In the key agreement protocols, an attacker can impersonate a legal user to participate in the negotiation process and eavesdrop the agreement key easily. This is often overlooked in most quantum key agreement protocols, which makes them insecure in practical implementation. Considering this problem, the function of authenticating the user's identity is added in the proposed protocol. Combining classical hash function with identity information, we design the authentication operation conforming to the characteristics of quantum search algorithm. In the security analysis of the proposed protocol, quantum state discrimination is utilized to show that the protocol is secure against common attacks and impersonation attack. In addition, only single photons need to be prepared and measured, which makes our protocol feasible with existing technology.

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Quantum Dimensionality Reduction by Linear Discriminant Analysis

Dimensionality reduction (DR) of data is a crucial issue for many machine learning tasks, such as pattern recognition and data classification. In this paper, we present a quantum algorithm and a quantum circuit to efficiently perform linear discriminant analysis (LDA) for dimensionality reduction. Firstly, the presented algorithm improves the existing quantum LDA algorithm to avoid the error caused by the irreversibility of the between-class scatter matrix $S_B$ in the original algorithm. Secondly, a quantum algorithm and quantum circuits are proposed to obtain the target state corresponding to the low-dimensional data. Compared with the best-known classical algorithm, the quantum linear discriminant analysis dimensionality reduction (QLDADR) algorithm has exponential acceleration on the number $M$ of vectors and a quadratic speedup on the dimensionality $D$ of the original data space, when the original dataset is projected onto a polylogarithmic low-dimensional space. Moreover, the target state obtained by our algorithm can be used as a submodule of other quantum machine learning tasks. It has practical application value of make that free from the disaster of dimensionality.

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