Ph.D., Electrical Engineering, University of Kansas, 2022
B.Sc., Electrical and Electronic Engineering, American International University Bangladesh, 2013
Naveed Mahmud received his Ph.D. from the University of Kansas (KU), USA with honors in the department of Electrical Engineering and Computer Science (EECS). Dr. Mahmud’s research is focused on future and emerging computing architectures such as quantum computing and reconfigurable computing, optimizing quantum algorithms for efficient implementation on near-term quantum devices, developing applications for quantum computing, and investigating hybrid quantum-classical architectures. His publication record includes seven journal articles and thirteen conference papers. Prior to his Ph.D. at KU, he worked for three years as a Design Verification Engineer for Ulkasemi, a top semiconductor services firm in Bangladesh with offshore design centers in the USA. He was responsible for leading the Digital Design Verification team in addition to developing automated, self-checking testbenches and testsuites for ASICs.
ECE 5520: Computer Architecture
Mahmud, N., MacGillivray, A., Chaudhary, M., & El-Araby, E. (2022). Decoherence-Optimized Circuits for Multi-dimensional and Multi-level Decomposable Quantum Wavelet Transform. IEEE Internet Computing (IEEE-IC), Special Issue on Quantum and Post-Moore's Law Computing, vol. 26, issue 1, February 2022. doi: 10.1109/MIC.2021.3133845.
Mahmud, N., Haase-Divine, B., MacGillivray, A., & El-Araby, E. (2022). Quantum Dimension Reduction for Pattern Recognition in High-Resolution Spatio-Spectral Data. IEEE Transactions on Computers (TC), vol. 71, issue 1, pp. 1-12, January 2022. doi: 10.1109/TC.2020.3034883.
Mahmud, N., MacGillivray, A., Rai, A., Patterson, J., Gharaibeh, A., El-Araby, E., Shaw, H., & Cooper, L. (2021). Combining Quantum Key Distribution with Chaotic Systems for Free Space Optical Communications. Quantum Information Processing, Springer, vol. 20, Article 354, October 2021. doi: https://doi.org/10.1007/s11128-021-03299-3.
Mahmud, N., Haase-Divine, B., Kuhnke, A., Rai, A., MacGillivray, A., & El-Araby, E. (2020). Efficient Computation Techniques and Hardware Architectures for Unitary Transformations in Support of Quantum Algorithm Emulation. Journal of Signal Processing Systems (JSPS), vol. 92, issue 9, pp. 1017-1037, June 2020. doi: https://doi.org/10.1007/s11265-020-01569-4. (Invited among Best Papers of ASAP 2019 Conference).
Mahmud, N., El-Araby, E., & Caliga, D. (2019). Scaling reconfigurable emulation of quantum algorithms at high precision and high throughput. Quantum Engineering, Vol. 1, Issue 2, June 2019. doi: https://doi.org/10.1002/que2.19.(Editor-In-Chief choice for Journal Research Highlights).
Mahmud, N., Jeng, M. J., Nobel, M. A. I., Chaudhary, M., Islam, S. I., Levy, D., & El-Araby, E. (2022). Time-Efficient Quantum-to-Classical Data Decoding. The International Conference on Emergent Quantum Technologies (ICEQT 2022), Las Vegas, Nevada, USA, July 2022. To be published in Transactions on Computational Science & Computational Intelligence, Springer Nature - Research Book Series, 2022.
Mahmud, N., & El-Araby, E. Towards Complete Emulation of Quantum Algorithms using High-Performance Reconfigurable Computing. In The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’21)Doctoral Showcase, St. Louis, MO, USA, November 2021.
Mahmud, N., MacGillivray, A., Chaudhary, M., & El-Araby, E. (2021). Optimizing Quantum Circuits for Arbitrary State Synthesis and Initialization. In The 34th IEEE International System-on-Chip Conference (SOCC 2021), Las Vegas, NV, USA, September 2021. doi: 10.1109/SOCC52499.2021.9739614.
Mahmud, N., Haase-Divine, B., Srimoungchanh, B., Blankenau, N., Kuhnke, A., & El-Araby, E. (2019). Emulating Multi-pattern Quantum Grover’s Search on a High-Performance Reconfigurable Computer. In The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’19), pp. 4, Denver, CO, USA, November 2019.
Mahmud, N., Srimoungchanh, B., Haase-Divine, B., Blankenau, N., Kuhnke, A., & El-Araby, E. (2019). Combining Perfect Shuffle and Bitonic Networks for Efficient Quantum Sorting. In The 5th International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC'19), held in conjunction with the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’19), Denver, CO, USA, November 2019.
Mahmud, N., & El-Araby, E. (2019). Dimension Reduction for Efficient Pattern Recognition in High Spatial Resolution Data Using Quantum Algorithms. In The 32nd IEEE International System-on-Chip Conference (SOCC 2019), Singapore, September 2019. (Best Paper Award Nominee).
Mahmud, N., & El-Araby, E. (2019). Improving Emulation of Quantum Algorithms using Space-Efficient Hardware Architectures. In The 30th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP 2019), New York, USA, July 2019. (Conference Best Papers)
Mahmud, N., & El-Araby, E. (2018). Towards Higher Scalability of Quantum Hardware Emulation using Efficient Resource Scheduling. In The 3rd IEEE International Conference on Rebooting Computing (ICRC 2018), Washington, DC, USA, November 2018.
Mahmud, N., & El-Araby, E. (2018). A Scalable High-Precision and High-Throughput Architecture for Emulation of Quantum Algorithms. In The 31st IEEE International System-on-Chip Conference (SOCC 2018), Washington, DC, USA, September 2018.
Mahmud, N., El-Araby, E., Shaw, H., & Cooper, L. (2018). Securing and auto-synchronizing communication over free-space optics using quantum key distribution and chaotic systems. In Quantum Communications and Quantum Imaging XVI, SPIE’18, San Diego, CA, USA, August 2018.
Looking to hire PhD/Masters students:
I am looking for a Ph.D or Master's student who will join my research group from Spring 2023 at Florida Tech. The position offered is a Graduate Student Assistantship.
If you are interested in doing research on Quantum Computing and/or other Emerging Computing Technologies, please email me at firstname.lastname@example.org with your CV and research interest.
Candidates should be skilled in C/C++/Python. Prior experience in machine learning or quantum physics/mechanics will be good to have. Experience with quantum computing platforms (such as IBM Quantum) will also be preferred.
My Current Research:
Quantum Computing and Reconfigurable Computing:
Quantum computing is a promising technology that can potentially demonstrate supremacy over classical computing in solving a specific class of problems. However, currently two of the main challenges facing quantum computing are quantum state decoherence, and low scalability of current quantum devices. Decoherence places constraints on the realistic applicability of quantum algorithms as real-life applications usually require complex equivalent quantum circuits to be realized. For example, encoding classical data on quantum computers for solving I/O and data-intensive applications generally requires quantum circuits that violate decoherence constraints. In addition, current quantum devices are of small-scale having low quantum bit (qubit) counts, and often producing inaccurate or noisy measurements, which also impacts the realistic applicability of real-world quantum algorithms. Consequently, benchmarking of existing quantum algorithms and the investigation of new applications are heavily dependent on classical simulations that use costly, resource-intensive computing platforms.
My main research focus is on developing efficient solutions to meet these challenges in quantum computing. Specifically, I have delivered contributions in the following areas: reducing complexities of classical-to-quantum data encoding quantum circuits, extending and optimizing various quantum algorithms to mitigate decoherence noise effects, developing a cost-effective Field-Programmable-Gate-Array (FPGA) based emulation methodology for quantum algorithms, and investigating and developing new quantum applications. The results of my research work demonstrate that quantum computers and methodologies based on quantum algorithms will be highly useful in realistic data-intensive domains such as remote-sensing hyperspectral imagery and high-energy physics (HEP). In addition to my work on quantum computing and quantum algorithms, I have also worked on research projects investigating efficient quantum-classical hybrid systems for securing Free-Space Optical (FSO) communications. My work has been published in top conferences such as Supercomputing (SC), System-on-Chip Conference (SOCC), International Conference on Rebooting Computing (ICRC), and in top peer-reviewed journals and magazines such as IEEE Transactions on Computers, Quantum Engineering, Quantum Information Processing, and IEEE Internet Computing. My paper in SOCC 2019 won the Best Paper Award and my journal publication in Quantum Engineering was awarded the Editor-in-Chief choice for the Journal Research Highlights. My Doctoral research was selected for the Doctoral Showcase and Poster Presentation at SC 2021.