Power System Fault Diagnosis with Quantum Computing and Efficient Gate Decomposition

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 (QAOA). 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 proposed. 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

Reversibly tuning the insulating and superconducting state in KxFe2-ySe2 crystals by post-annealing

Since the discovery of superconductivity at 26 K in oxy-pnictide LaFeAsO1-xFx, enormous interests have been stimulated in the field of condensed matter physics and material sciences. Among the many kind of structures in the iron pnictide superconductors, FeSe with the PbO structure has received special attention since there is not poisonous pnictogen element in chemical composition and its structure is the simplest one. However, the superconducting transition temperature (Tc) in iron chalcogenide compounds is not enhanced as high as other iron pnictide superconductors under ambient pressure until the superconductivity at above 30 K in potassium intercalated iron selenide KxFe2-ySe2 was discovered. The insulating and the superconducting state are both observed in KxFe2-ySe2 with different stoichiometries and some groups have tuned the system from insulating to superconducting state by varying the ratio of starting materials[10, 11]. The recent data from neutron scattering suggest that the superconductivity may be built upon an ordered state of Fe vacancies as well as the antiferromagnetic state with a very strong ordered magnetic moment 3.4 B. Here we show that the superconductivity can actually be tuned on a single sample directly from an insulating state by post-annealing and fast quenching. Upon waiting for some days at room temperatures, the superconductivity will disappear and the resistivity exhibits an insulating behavior again. The spatial distribution of the compositions of the as-grown sample and the post-annealed-quenched one was analyzed by the Energy Dispersive X-ray Spectrum (EDXS) and found to be very close to each other. Therefore it is tempting to conclude that the superconductivity is achieved when the Fe-vacancies are in a random (disordered) state. Once they arrange in an ordered state by relaxation or slow cooling, the system turns out to be an insulator.Comment: 12 pages,5 figure

Increased brain iron in patients with thyroid-associated ophthalmopathy: a whole-brain analysis

BackgroundTo investigate the whole-brain iron deposition alternations in patients with thyroid-associated ophthalmopathy (TAO) using quantitative susceptibility mapping (QSM).MethodsForty-eight patients with TAO and 33 healthy controls (HCs) were enrolled. All participants underwent brain magnetic resonance imaging scans and clinical scale assessments. QSM values were calculated and compared between TAO and HCs groups using a voxel-based analysis. A support vector machine (SVM) analysis was performed to evaluate the performance of QSM values in differentiating patients with TAO from HCs.ResultsCompared with HCs, patients with TAO showed significantly increased QSM values in the bilateral caudate nucleus (CN), left thalamus (TH), left cuneus, left precuneus, right insula and right middle frontal gyrus. In TAO group, QSM values in left TH were positively correlated with Hamilton Depression Rating Scale (HDRS) scores (r = 0.414, p = 0.005). The QSM values in right CN were negatively correlated with Montreal Cognitive Assessment (MoCA) scores (r = -0.342, p = 0.021). Besides that, a nearly negative correlation was found between QSM values in left CN and MoCA scores (r = -0.286, p = 0.057). The SVM model showed a good performance in distinguishing patients with TAO from the HCs (area under the curve, 0.958; average accuracy, 90.1%).ConclusionPatients with TAO had significantly increased iron deposition in brain regions corresponding to known visual, emotional and cognitive deficits. QSM values could serve as potential neuroimaging markers of TAO