Quantum Computing
The quantum field is now moving from theory into practice with very limited research existing on the practical aspect. Key issues addressed by the Quantum Computing research group include handling noise and coping with a limited number of qubits. Quantum is now changing all aspects of computer systems including data management and communication. With the tremendous processing power of quantum computing, real-world grand challenges like climate change modelling and forecasting can be tackled.
The team:
- Dr. Ahmed El-Mahdy [Chair]
- Dr. Marwa Sorour
- Dr. Norhan Elsayed
- Eng. Mustafa Fathy [MSc Student]
- Eng. Ahmed Jamal [MSc Student]
- Yusuf Alsawah [BSc Student]
- Mohammed Abdulsami [BSc Student]
- Omar Abdelrasoul [BSc Student]
- Youssef AbdElWahab [BSc Student]
Collaborators:
- Prof. Walid Gomaa, E-JUST
- Prof. Kazunori Ueda, Waseda University
- Prof. Keiji Kimura, Waseda University
- Prof. Yasutaka Wada, Mesi University
- Dr. Bassem Mokhtar, UAE University
- Eng. Mohamed Mourad, UAE University
We now have a new undergraduate track on quantum computing which includes the following courses:
- CSCI 361: Introduction to Quantum Computing
- CSCI 461: Quantum Computer Architecture
- AIS407: Quantum Machine Learning
- CSCI 462: Distributed Quantum Computing Systems
- CSCI 463: Quantum Implementation Technologies
- CSEC 451: Cybersecurity for Quantum Computing
- CSCI 464: Introduction to Quantum Information Systems
- CSCI 465: Introduction to Adiabatic Quantum Computing
- CSCI 466: Selected Topics in Quantum Computing
Accepted paper:
- Amer, Norhan Elsayed, Walid Gomaa, Keiji Kimura, Kazunori Ueda, and Ahmed El-Mahdy. "On the optimality of quantum circuit initial mapping using reinforcement learning." EPJ Quantum Technology 11, no. 1 (2024): 19.
- Norhan Elsayed Amer, Walid Gomaa, Keiji Kimura, Kazunori Ueda and Ahmed El-Mahdy, “On the Learnability of quantum state fidelity, EPJ Quantum Technology,” Springer, https://doi.org/10.1140/epjqt/s40507-022-00149-8, Accepted Nov 2022.
- Norhan Elsayed, Ahmed El-Mahdy, On the Predictability of Quantum Circuit Fidelity Using Machine Learning, 35th IEEE International Parallel & Distributed Processing Symposium (IPDPS’21), 2021.
Current Research Projects
Practical Quantum Neural Network Computation on Noisy, Intermediate Scale-Quantum Computers
Practical Quantum Neural Network Computation on Noisy, Intermediate Scale-Quantum Computers PI of the project: Prof. Ahmed Hazem El-Mahdy Funded by: STDF E-JUST Waseda Highlights: Fidelity Estimation: Estimating the Fidelity is a very complex task, requiring an exponential number of experiments We showed that we can learn the fidelity using CNN with polynomial complexity Improving Fidelity: The way quantum circuits are mapped into physical qubits significantly affects their fidelity We used reinforcement learning to reach a ‘good’ map We are comparable by the state-of-the-art but our approach