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Dhananjay Joshi

Publications and source records attributed to Dhananjay Joshi.

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Flip-chip integrated superconducting qubits using electroplated bump bonds

Flip-chip integration offers a promising route toward scalable superconducting quantum processors and hybrid semiconductor-superconductor quantum devices. We develop a three-dimensional transmon architecture using electroplated indium in which the qubit electric field is shared nearly equally between two bump-bonded substrates while maintaining low participation at the indium-bump interface. The resulting geometry is well suited for future hybrid qubits, enabling the integration of distinct material platforms while minimizing sensitivity to bump-interface loss. Using this platform, we evaluate electroplated indium interconnects for superconducting quantum circuits. Flip-chip transmons incorporating electroplated indium bumps exhibit qubit quality factors around $10^6$. In addition, a systematic study of coplanar-waveguide resonators is used to identify losses associated with the electroplating process. In particular, we find that surface losses associated with the gold-layer, used to enable good electric contact with the indium, is likely the primary contributor to the qubit decay rate. These results demonstrate the compatibility of electroplated indium technology with high-coherence superconducting circuits and establish a promising platform for three-dimensional hybrid quantum integration.

quant-ph

CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification

In today's world of health care, brain tumor detection has become common. However, the manual brain tumor classification approach is time-consuming. So Deep Convolutional Neural Network (DCNN) is used by many researchers in the medical field for making accurate diagnoses and aiding in the patient's treatment. The traditional techniques have problems such as overfitting and the inability to extract necessary features. To overcome these problems, we developed the Topological Data Analysis based Improved Persistent Homology (TDA-IPH) and Convolutional Transfer learning and Visual Recurrent learning with Elephant Herding Optimization hyper-parameter tuning (CTVR-EHO) models for brain tumor segmentation and classification. Initially, the Topological Data Analysis based Improved Persistent Homology is designed to segment the brain tumor image. Then, from the segmented image, features are extracted using TL via the AlexNet model and Bidirectional Visual Long Short-Term Memory (Bi-VLSTM). Next, elephant Herding Optimization (EHO) is used to tune the hyperparameters of both networks to get an optimal result. Finally, extracted features are concatenated and classified using the softmax activation layer. The simulation result of this proposed CTVR-EHO and TDA-IPH method is analyzed based on precision, accuracy, recall, loss, and F score metrics. When compared to other existing brain tumor segmentation and classification models, the proposed CTVR-EHO and TDA-IPH approaches show high accuracy (99.8%), high recall (99.23%), high precision (99.67%), and high F score (99.59%).

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