Hybrid GRU-CNN Bilinear Parameters Initialization for Quantum Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm (QAOA), a pivotal paradigm in the realm of variational quantum algorithms (VQAs), offers promising computational advantages for tackling combinatorial optimization problems. Well-defined initial circuit parameters, responsible for preparing a parameterized quantum state encoding the solution, play a key role in optimizing QAOA. However, classical optimization techniques encounter challenges in discerning optimal parameters that align with the optimal solution. In this work, we propose a hybrid optimization approach that integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and a bilinear strategy as an innovative alternative to conventional optimizers for predicting optimal parameters of QAOA circuits. GRU serves to stochastically initialize favorable parameters for depth-1 circuits, while CNN predicts initial parameters for depth-2 circuits based on the optimized parameters of depth-1 circuits. To assess the efficacy of our approach, we conducted a comparative analysis with traditional initialization methods using QAOA on Erd\H{o}s-R\'enyi graph instances, revealing superior optimal approximation ratios. We employ the bilinear strategy to initialize QAOA circuit parameters at greater depths, with reference parameters obtained from GRU-CNN optimization. This approach allows us to forecast parameters for a depth-12 QAOA circuit, yielding a remarkable approximation ratio of 0.998 across 10 qubits, which surpasses that of the random initialization strategy and the PPN2 method at a depth of 10. The proposed hybrid GRU-CNN bilinear optimization method significantly improves the effectiveness and accuracy of parameters initialization, offering a promising iterative framework for QAOA that elevates its performance

The ROP mechanism study in hard formation drilling using local impact method

The low rate of penetration and short lifetime of drilling bit served as the most common problems encountered in hard formation drilling, thus leading to severe restriction of drilling efficiency in oil and gas reservoir. This study developed a new local impact drilling method to enhance hard formation drilling efficiency. The limitation length formulas of radial/lateral cracks under static indentation and dynamic impact are derived based on the experimental research of Marshall D.B considering the mud column pressure and confining pressure. The local impact rock breaking simulation model is conducted to investigate its ROP raising effect. The results demonstrate that the length of radial/lateral cracks will increase as the decrease of mud pressure and confining pressure, and the local impact can result in a damage zone round the impact crater which helps the rock cutting, thus leading to the ROP increase. The numerical results also demonstrate the advantages of local impact method for raising ROP and the vibration reduction of bit in hard formation drilling. This study has shown that the local impact method can help raising the ROP and vibration reduction of bit, and it may be applied in drilling engineering

Motion-induced eddy current thermography for high-speed inspection

This letter proposes a novel motion-induced eddy current based thermography (MIECT) for high-speed inspection. In contrast to conventional eddy current thermography (ECT) based on a time-varying magnetic field created by an AC coil, the motion-induced eddy current is induced by the relative motion between magnetic field and inspected objects. A rotating magnetic field created by three-phase windings is used to investigate the heating principle and feasibility of the proposed method. Firstly, based on Faraday’s law the distribution of MIEC is investigated, which is then validated by numerical simulation. Further, experimental studies are conducted to validate the proposed method by creating rotating magnetic fields at different speeds from 600 rpm to 6000 rpm, and it is verified that rotating speed will increase MIEC intensity and thereafter improve the heating efficiency. The conclusion can be preliminarily drawn that the proposed MIECT is a platform suitable for high-speed inspection