arXiv subjects

Qing-Le Wang

Publications and source records attributed to Qing-Le Wang.

4 recordsLinked to original sources

arXiv2024

A Quantum Neural Network-Based Approach to Power Quality Disturbances Detection and Recognition

Power quality disturbances (PQDs) significantly impact the stability and reliability of power systems, necessitating accurate and efficient detection and recognition methods. While numerous classical algorithms for PQDs detection and recognition have been extensively studied and applied, related work in the quantum domain is still in its infancy. In this paper, an improved quantum neural networks (QNN) model for PQDs detection and recognition is proposed. Specifically, the model constructs a quantum circuit comprising data qubits and ancilla qubits. Classical data is transformed into quantum data by embedding it into data qubits via the encoding layer. Subsequently, parametric quantum gates are utilized to form the variational layer, which facilitates qubit information transformation, thereby extracting essential feature information for detection and recognition. The expected value is obtained by measuring ancilla qubits, enabling the completion of disturbance classification based on this expected value. An analysis reveals that the runtime and space complexities of the QNN are $O\left ( poly\left ( N \right ) \right )$ and $O\left ( N \right )$, respectively. Extensive experiments validate the feasibility and superiority of the proposed model in PQD detection and recognition. The model achieves accuracies of 99.75\%, 97.85\% and 95.5\% in experiments involving the detection of disturbances, recognition of seven single disturbances, and recognition of ten mixed disturbances, respectively. Additionally, noise simulation and comparative experiments demonstrate that the proposed model exhibits robust anti-noise capabilities, requires few training parameters, and maintains high accuracy.

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arXiv2024

Quantum Secret Sharing Enhanced: Utilizing W States for Anonymous and Secure Communication

Quantum secret sharing (QSS) is the result of merging the principles of quantum mechanics with secret information sharing. It enables a sender to share a secret among receivers, and the receivers can then collectively recover the secret when the need arises. To enhance the practicality of these quantum protocols, an innovative concept of quantum anonymous secret sharing (QASS) is advanced. In this paper, we propose a QASS protocol via W states, which can share secrets while ensuring recover-ability, recover-security, and recover-anonymity. We have rigorously evaluated our protocols, verifying their accuracy and fortifying their security against scenarios involving the active adversary. This includes considerations for dishonest receivers and non-receivers. Moreover, acknowledging the imperfections inherent in real-world communication channels, we have also undertaken an exhaustive analysis of our protocol's security and effectiveness in a quantum network where some form of noise is present. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications.

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arXiv2020

Improved quantum algorithm for A-optimal projection

Dimensionality reduction (DR) algorithms, which reduce the dimensionality of a given data set while preserving the information of the original data set as well as possible, play an important role in machine learning and data mining. Duan \emph{et al}. proposed a quantum version of the A-optimal projection algorithm (AOP) for dimensionality reduction [Phys. Rev. A 99, 032311 (2019)] and claimed that the algorithm has exponential speedups on the dimensionality of the original feature space $n$ and the dimensionality of the reduced feature space $k$ over the classical algorithm. In this paper, we correct the time complexity of Duan \emph{et al}.'s algorithm to $O(\frac{κ^{4s}\sqrt{k^s}} {ε^{s}}\mathrm{polylog}^s (\frac{mn}ε))$, where $κ$ is the condition number of a matrix that related to the original data set, $s$ is the number of iterations, $m$ is the number of data points and $ε$ is the desired precision of the output state. Since the time complexity has an exponential dependence on $s$, the quantum algorithm can only be beneficial for high dimensional problems with a small number of iterations $s$. To get a further speedup, we propose an improved quantum AOP algorithm with time complexity $O(\frac{s κ^6 \sqrt{k}}ε\mathrm{polylog}(\frac{nm}ε) + \frac{s^2 κ^4}ε\mathrm{polylog}(\frac{κk}ε))$ and space complexity $O(\log_2(nk/ε)+s)$. With space complexity slightly worse, our algorithm achieves at least a polynomial speedup compared to Duan \emph{et al}.'s algorithm. Also, our algorithm shows exponential speedups in $n$ and $m$ compared with the classical algorithm when both $κ$, $k$ and $1/ε$ are $O(\mathrm{polylog}(nm))$.

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arXiv2016

Quantum algorithm for association rules mining

Association rules mining is one of the most important problems in knowledge discovery and data mining. The goal of it is to acquire consumption habits of customers by discovering the relationships between items from a transaction database that has a large number of transactions and items. The most compute intensive process for ARM is to find out the frequent 1-itemsets and 2-itemsets. In this paper, we propose a quantum algorithm for finding out the frequent 1-itemsets and 2-itemsets. In our algorithm, to mine the frequent 1-itemsets efficiently, we use the technique of amplitude amplification. To mine the frequent 2-itemsets efficiently, we propose a new tomography scheme, i.e., pure-state-based quantum state tomography. It is shown that our algorithm is potential to achieve exponential speedup in the number of transactions and polynomial speedup in the number of items over the classical algorithm.

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