Publications

Advanced Quantum Control With Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain

Published in IEEE Access, 2025

This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. This research aims to use the complementary strengths of DQN and PPO algorithms to develop robust and adaptive control policies for noisy and uncertain quantum systems. We comprehensively analyse the proposed ensemble learning, including algorithmic details, implementation specifics, and experimental results. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the ensemble approach in learning control strategies for manipulating quantum systems towards a random target state. The results highlight the potential of ensemble RL techniques in addressing the challenges of quantum control tasks, such as system noise and dynamics. By integrating multiple RL agents within an ensemble framework, We aim to advance current developments in quantum control and create a new path for the development of adaptive control systems for quantum systems. The performance of the ensemble model is assessed against Gradient Ascent Pulse Engineering (GRAPE) and robust Model Predictive Control (MPC) to demonstrate its efficiency in highly challenging and noisy environments.

Download Paper

Enhancing the Security of Classical Communication with Post-Quantum Authenticated-Encryption Schemes for the Quantum Key Distribution

Published in Computers, 2024

This research aims to establish a secure system for key exchange by using post-quantum cryptography (PQC) schemes in the classic channel of quantum key distribution (QKD). Modern cryptography faces significant threats from quantum computers, which can solve classical problems rapidly. PQC schemes address critical security challenges in QKD, particularly in authentication and encryption, to ensure the reliable communication across quantum and classical channels. The other objective of this study is to balance security and communication speed among various PQC algorithms in different security levels, specifically CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon, which are finalists in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography Standardization project. The quantum channel of QKD is simulated with Qiskit, which is a comprehensive and well-supported tool in the field of quantum computing. By providing a detailed analysis of the performance of these three algorithms with Rivest–Shamir–Adleman (RSA), the results will guide companies and organizations in selecting an optimal combination for their QKD systems to achieve a reliable balance between efficiency and security. Our findings demonstrate that the implemented PQC schemes effectively address security challenges posed by quantum computers, while keeping the the performance similar to RSA.

Download Paper

Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS)

Published in IEEE Access, 2024

Network packet sniffing is one of the techniques that is widely used in the network and cyber security fields. However, sniffing can also be used as a malicious technique that allows threat actors to intercept and capture data flow to collect various information within the victim network. Where the wireless network environment can be vulnerable to sniffing vulnerabilities attacks due to the broadcasting function of Wi-Fi network. Wi-Fi access point devices can often be compromised, and critical information is leaked through sniffing attacks. Moreover, since sniffing is usually one of passive attacks, it is very challenging to detect sniffing activity in the network completely. The primary aim of this research is to contribute to enhancing the security of Internet of Things (IoT) home Wi-Fi systems. This is achieved by applying ensemble machine learning technology with sniffing detection methods using a Network Monitoring System (NMS) to effectively identify and mitigate potential sniffing behaviour within the IoT home Wi-Fi environment. Ultimately, this research will prove whether it is possible to precisely detect abnormal sniffing in a smart home Wi-Fi environment using machine learning techniques.

Download Paper

Recent Advances in Quantum Error Correction and Their Impact on the Stability of Quantum Computing

Published in 9th International Conference on Interdisciplinary Studies in Nanotechnology, 2026

Quantum computing is one of the most important emerging technology areas due to its potential to solve complex problems that are inaccessible or time-consuming for classical computers. However, the fragility of quantum states to noise and incoherence is the biggest challenge for this technology. Quantum error correction (QEC) is considered a key solution to achieve fault-tolerant quantum computing. This paper analyzes recent advances in quantum error correction techniques and their direct impact on the stability and scalability of quantum systems…

Prediction of the Behavior of Many-Particle Systems in Quantum Mechanics Using Quantum Neural Networks

Published in 13th International Conference on Applied Research in Basic Sciences, Engineering and Technology, 2026

Predicting the behavior of many-particle systems in quantum mechanics is one of the most difficult computational problems in condensed matter physics and chemistry due to the exponential growth of the dimension of the Hilbert space. The aim of this research is to investigate the ability of quantum neural networks to overcome this challenge and achieve chemical accuracy in predicting the ground-state energy and properties of many-electron systems. In this study, a new architecture of quantum neural networks based on strongly entangling layers, tensor pretraining, and quantum natural gradient optimization was designed and implemented on NISQ superconducting processors. The results showed that these networks can predict the properties of the molecules H₂, LiH, BeH₂, H₂O, and CH₄ with an average error of 0.92 mH (better than the chemical accuracy limit of 1.6 mH) and significantly better than the classical gold-standard method CCSD(T) and the standard VQE algorithm. Even for the N₂ molecule with 24 qubits, an error of 2.11 mH was achieved despite real hardware noise. The required circuit depth was also reduced by 40 to 65 percent, and the barren plateau phenomenon was controlled up to a depth of 42 layers. These findings suggest that quantum neural networks could soon replace costly classical methods in simulating medium- and large-scale many-particle systems, paving the way for the precise design of new materials and drugs.