I am Jishnu Mahmud, a junior lecturer at BRAC University and a research assistant under Professor Shaikh Anowarul Fattah, PhD, at Bangladesh University of Engineering and Technology (BUET). I completed my Electrical and Electronic Engineering (EEE) undergraduate studies at the Bangladesh University of Engineering and Technology (BUET), majoring in Communication and Signal Processing.
I am seeking PhD opportunities in quantum information and computation for the Fall 2025 session.
I am vastly interested in the domain of quantum sciences, especially quantum information theory, quantum algorithms, quantum error correction, quantum cryptography, and quantum state tomography.
I did my undergraduate thesis on variational quantum computing under the supervision of Dr. Fattah. It has now been published by Quantum Machine Intelligence by Springer Nature. Currently, my research interests aim to learn broader disciplines of quantum information and computation and use them to develop techniques that would take significant steps to harness the power of quantum machines. I am curious about the domain of problems where quantum machines may outperform the classical computer and devising various methods by which we can attain these goals.
I am working on a research project with Sowmitra Das, a visiting researcher at Imperial College London, on using shadow tomography and classical machine learning to facilitate quantum error mitigation on noisy quantum circuits. We believe that shadow tomography offers tremendous possibilities and hope to show that it can be the answer to developing state-of-the-art error mitigation techniques.
Patch-Based End-to-End Quantum Learning Network for Reduction and Classification of Classical Data Mahmud, J., and Fattah, S.A. arxiv pre-print [PDF].
Quantum convolutional neural networks with interaction layers for classification of classical data Mahmud, J., Mashtura, R., Fattah, S.A. and Saquib, M., 2024. Quantum Machine Intelligence, 6(1), p.11. [PDF]
A Parallel Quantum Feature Encoding Scheme for Effective Classical Data Classification in Quantum Convolutional Neural Networks Mashtura, R., Mahmud, J., Fattah, S.A. and Saquib, M., 2023, October. In TENCON 2023-2023 IEEE Region 10 Conference (TENCON) (pp. 1-5) IEEE.
At BRAC University, I have co-designed the CSE 481 (Quantum Computing 1) course and offered it after a year of dormancy. We strive to promote and educate quantum computing 1 to undergraduates from different backgrounds. I am also co-organizing a lecture series on Quantum Information by Dr. Tibra Ali, a weekly open-to-all lecture series with meet and greet of students from all over Bangladesh interested in learning quantum computing. I aim to promote, educate, and teach quantum computation and information to the new generation of bright Bangladeshi Undergraduates. I have co-supervised two undergraduate thesis groups on quantum variational algorithms.
TQC 2024: Attended various talks and participated in poster sessions.
IEEE TENCON 2023: Presented a paper on quantum machine learning.
IUB Summer School 2024: Glimpses into the Quantum World - Information, Technology, and Applications}: Attended the summer school arranged by the Independent University of Bangladesh (IUB) on quantum information and computation. Lectures were provided by Dr. Leong Chuan Kwek (National University of Singapore) and Dr. Subhro Bhattacharjee (International Centre for Theoretical Sciences).
Here are some of the projects I did during my undergrad. Some were course requirements, some collaborated with friends, and others were to learn a particular topic.
Complete (Transient and Steady State) response of variable multinodal circuits: We designed software to visualize the response of any passive network defined by the user using a developed graphical user interface. The code used Laplace and Fourier Transformations to solve RLC electrical circuits.
Speech-emotion detection- Benchmarking the RAVDESS dataset using convolutional neural networks: I designed a convolutional neural network to classify speech data by emotions. The data was preprocessed by several signal processing techniques, which were used to extract a feature matrix fed to the designed CNN structure to attain competitive results.
Intelligent Traffic Control: We designed an intelligent traffic system sensitive to various parameters such as volume, flow, and human emergencies using sequential logical circuits and finite state machines (FSM).
Biometric Fingerprint Sensor Attendance System with Fever Detection—An IOT-based solution (Microprocessor & Embedded system project): We developed a fingerprint attendance system using NodeMCU and Arduino. We further designed a server running hypertext protocols (HTTP) to request to read and write data to the database once the device was connected to a computer.
Investigating the effects of HVDC connection and large industrial loads in IEEE 39-bus networking using PSAF: We investigated the various types of faults occurring under different loading conditions in a multi-bus power system.
Design of a power system to drive a centrifugal pump for agricultural irrigation: We designed a squirrel cage induction motor controlled by intelligent switches serving as a control system for maintaining optimum water levels required for agricultural irrigation.