A quantum approximate optimization algorithm for solving Hamilton path problem

Gong, Changqing; Wang, Ting; He, Wanying; Qi, Han

doi:10.1007/s11227-022-04462-y

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Subsequently, we set the time step to 4, where for each time step t, we used the data features from t-1, t-2, and t-5 as inputs. Ultimately, the input data size for each timestep became [4,3].…”

Section: Methodsmentioning

confidence: 99%

“…Quantum computing can harness special properties, including superposition and entanglement, to execute these tasks even at exponential speeds. Research and applications in quantum computing have achieved significant success, including quantum simulation, quantum approximate optimization algorithm [3], quantum machine learning [4], among other technologies. As researchers delve deeper, models such as quantum reinforcement learning [5], quantum support vector machines [6], quantum neural networks [7] have emerged successively.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Quantum Long Short-Term Memory By Using Bidirectional Ring Variational Quantum Circuit

Qi,

Lv,

Gong

et al. 2024

Preprint

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With the rapid development of quantum machine learning, Quantum Long Short-Term Memory (QLSTM) have demonstrated quantum advantages in time series forecasting problems. Currently, it has found applications in fields such as financial market analysis, natural language processing, and weather forecasting, but its accuracy in regression problem prediction still shows deficiencies. To improve the prediction accuracy of the model, enhanced quantum long short-term memory by using bidirectional ring variational quantum circuit (EQLSTM) is proposed. We reconsidered and optimized the types, quantities, and arrangements of quantum gates, designing a bidirectional ring variational quantum circuit composed of CRX gates (Bi-VQC) to enhance its expressibility. Bi-VQC can more accurately describe and encode complex quantum state features, enabling the EQLSTM to better learn and represent the characteristics of the input data. In addition, Bi-VQC reduces the number of optimizable parameters and lowers the circuit load of the EQLSTM model. To validate the effectiveness of the proposed model, experiments are conducted to test and evaluate the EQLSTM using the Aggregated traffic in the United Kingdom academic network backbone. The experimental results confirm that the proposed EQLSTM model improves from 90.56\% to 97.57\% compared to the QLSTM model.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Quantum Long Short-Term Memory By Using Bidirectional Ring Variational Quantum Circuit

Qi,

Lv,

Gong

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Power system fault diagnosis with quantum computing and efficient gate decomposition

Fei,

Zhao,

Zhou

et al. 2024

Sci Rep

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Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from significant time-consuming, memory overhead, and computational complexity issues as the scale of the power system concerned increases. With rapid development of quantum computing technology, the combinatorial optimization method based on quantum computing has shown certain advantages in computational time over existing methods. Given this background, this paper proposes a quantum computing based power system fault diagnosis method with the quantum approximate optimization algorithm. The proposed method reformulates the fault diagnosis problem as a Hamiltonian by using Ising model, which completely preserves the coupling relationship between faulty components and various operations of protective relays and circuit breakers. Additionally, to enhance problem-solving efficiency under current equipment limitations, the symmetric equivalent decomposition method of multi-z-rotation gate is utilized. Furthermore, the small probability characteristics of power system events is utilized to reduce the number of qubits. Simulation results based on the test system show that the proposed methods can achieve the same optimal results with a faster speed compared with the classical higher-order solver provided by D-Wave.

show abstract

A quantum approximate optimization algorithm for solving Hamilton path problem

Cited by 2 publications

References 15 publications

Enhanced Quantum Long Short-Term Memory By Using Bidirectional Ring Variational Quantum Circuit

Enhanced Quantum Long Short-Term Memory By Using Bidirectional Ring Variational Quantum Circuit

Power system fault diagnosis with quantum computing and efficient gate decomposition

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