Surajit Sen served as a Project Assistant at Siksha ’O’ Anusandhan University, contributing to advanced research in quantum computing and quantum information theory. The program provided rigorous training in theoretical and mathematical physics, with a strong focus on advanced quantum mechanics, quantum information theory, and applied mathematics.
Surajit Sen has undertaken several advanced research projects in quantum computing and quantum information theory during his tenure at Siksha ’O’ Anusandhan University. His research primarily focuses on hybrid quantum-classical models and quantum measurement techniques with applications in image processing and pattern recognition.
Surajit Sen has undertaken several advanced research projects in quantum computing and quantum information theory during his tenure at Siksha ’O’ Anusandhan University. His research primarily focuses on hybrid quantum-classical models and quantum measurement techniques with applications in image processing and pattern recognition.
Unsharp Measurement-Based Hybrid Feature Extraction for Image Classification (2025): In this project, Surajit developed a hybrid quantum-classical model to classify handwritten digits ‘0’ and ‘4’ from the MNIST dataset. The grayscale pixel data was encoded into quantum states, and unsharp measurements were employed to extract feature sets while preserving critical quantum properties. These quantum features were then processed by a classical neural network. The study revealed that factors such as state reuse and measurement unsharpness play a significant role in the accuracy of classification tasks.
Unsharp Measurement-Based Edge-Detection Using a Photonic Quantum Computer (2024): Surajit developed a photonic quantum-inspired framework for image edge detection utilizing beam splitters and unsharp measurements. The model was optimized using the COBYLA numerical optimization algorithm to minimize reconstruction errors, resulting in high-quality image generation as measured by low mean squared error (MSE) and high peak signal-to-noise ratio (PSNR). This work holds potential applications in fields such as medical imaging, cryptography, and pattern recognition.
Analysis of PT-Symmetric Systems with Vanishing Real and Imaginary Components (2024): This project involved studying a parity-time (PT) invariant quantum potential where both real and imaginary parts vanish. Surajit analyzed the conditions necessary for unbroken PT symmetry and the emergence of real eigenvalues. The research identified symmetry-breaking thresholds and developed solvable models applicable to quantum optics and wave mechanics.
Elective Courses
Unsharp Measurement-Based Edge-Detection Using a Photonic Quantum Computer (2024): Surajit developed a photonic quantum-inspired framework for image edge detection utilizing beam splitters and unsharp measurements. The model was optimized using the COBYLA numerical optimization algorithm to minimize reconstruction errors, resulting in high-quality image generation as measured by low mean squared error (MSE) and high peak signal-to-noise ratio (PSNR). This work holds potential applications in fields such as medical imaging, cryptography, and pattern recognition.
Analysis of PT-Symmetric Systems with Vanishing Real and Imaginary Components (2024): This project involved studying a parity-time (PT) invariant quantum potential where both real and imaginary parts vanish. Surajit analyzed the conditions necessary for unbroken PT symmetry and the emergence of real eigenvalues. The research identified symmetry-breaking thresholds and developed solvable models applicable to quantum optics and wave mechanics.
Elective Courses
To complement his research experience, Surajit has completed several elective courses that provide a strong theoretical and computational foundation in quantum computation and related areas:
Introduction to Quantum Computers: Quantum Algorithms and Information Theory (Spring 2024, S’O’A University): This course provided comprehensive coverage of quantum algorithms and the fundamentals of quantum information theory. Topics included quantum computation models, quantum algorithms like Grover’s and Shor’s, and the mathematical underpinnings of quantum information processing. The course enhanced Surajit's understanding of the theoretical frameworks essential for his research projects.
Introduction to Quantum Computers: Quantum Algorithms and Information Theory (Spring 2024, S’O’A University): This course provided comprehensive coverage of quantum algorithms and the fundamentals of quantum information theory. Topics included quantum computation models, quantum algorithms like Grover’s and Shor’s, and the mathematical underpinnings of quantum information processing. The course enhanced Surajit's understanding of the theoretical frameworks essential for his research projects.