Projects | Akshaya Srinivasan

Hybrid Quantum-Classical AI for Industrial Defect Classification

Developing quantum-classical pipelines integrating CNN features with VQLS-enhanced QSVM and variational quantum classifiers for weld defect classification in industrial applications.

Python Qiskit PyTorch Quantum SVM CNN

Quantum vs Classical Approaches for Crack Segmentation

Comparative study of Q-Seg, quantum-inspired techniques, and U-Net for crack image segmentation in industrial applications. Exploring the potential benefits of quantum algorithms over classical approaches.

Python Qiskit TensorFlow Image Processing U-Net

Self-Learning Algorithm for Quantum System Characterization

Developed a self-learning algorithm to characterize the state of quantum systems using Bayesian experimental designs. This approach optimizes measurement strategies for efficient quantum state tomography.

Python Bayesian Methods Quantum Systems Optimization

Nonlinear Dynamics of Bose-Einstein Condensates

Investigated the nonlinear dynamics of a cloud of dilute atomic gas trapped in a harmonic potential with laser and electromagnetic traps. Utilized the Gross-Pitaevskii equation with a Moment theory approach.

MATLAB Gross-Pitaevskii Equation Moment Theory Quantum Physics

Quantum Image Classification Circuit

Developed a Quantum Image Classification Circuit as part of IonQ's Trapped Ion Applications Challenge during IQuHACK 2023. Explored improvements and applications of near-term quantum devices.

Qiskit Quantum Circuits Image Classification IonQ

Quantum Firmware Frontend Development

Created an intuitive user interface for Quantum firmware using Python, Artiq, and PyQt. Developed wrapper functions using pyVisa for seamless remote control of scientific instruments.