Projects
List of previous and current projects:
EMRLD Chain: Hybrid Layered Encryption Framework with PQC and QKD
To address the threats to classical cyrptographic systems by quantum computing, the authors propose a hybrid encryption framework for quantum-classical networks, integrating classical and quantum-inspired cryptography. The first layer, Enhanced Multi-Round Layered Diffusion (EMRLD) Chain, derived a 2048-bit key using PBKDF2 from a user-supplied password and applied eight rounds of encryption with distinct sub-keys, significantly increasing resistance to quantum-assisted brute-force attacks. The second layer introduces a simulated Quantum Key Distribution mechanism, generating a quantum-inspired key through qubit-based randomness and privacy amplification via SHA-256. This key masked the ciphertext using a bitwise XOR operation, emulating a one-time pad. A proof-of-concept implementation demonstrates that this layered approach effectively mitigates quantum-era cryptographic risks while maintaining computational efficiency. The results highlight the potential of hybrid cryptographic frameworks as transitional security solutions in the evolving quantum landscape.
Quantum Code Synthesis: Leveraging LLMs and Retrieval-Augmented Generation
In recent years, significant advancements have been observed in quantum computing. However, translating and encoding problems from classical computing into the quantum realm remains a focal area of research. This process requires substantial energy and dedication from scientists and experts who aim to formulate specific problems into quantum computing algorithms. Existing quantum algorithms often function as proofs of concept or face limitations regarding scalability, noise tolerance, and adaptability. This study investigates how Large Language Models (LLMs) can facilitate formulating problems within quantum computing. Our research thoroughly examines the potential of developing Hamiltonians by leveraging the capabilities of Large Language Models (LLMs). We compare prominent models OpenAI GPT, AWS Claude, and Meta Llama developed and evaluate the Retrieval Augmented Generation (RAG) technique. Finally, we enhance our outcome by using the reward function value to assess the generated result. This approach enhances the accuracy and precision of the quantum solution.
Dependable Classical-Quantum Computing Systems Engineering
Increasing evidence suggests quantum computing (QC) complements traditional High-Performance Computing (HPC) by leveraging its unique capabilities, leading to the emergence of a new, hybrid paradigm, QHPC. However, this integration introduces new challenges, with dependability—defined by reproducibility, resiliency, and security and privacy—emerging as a central concern for building trustworthy systems that provide an advantage to the users. This paper proposes a framework for dependable QHPC system design, organized around these three pillars. We identify integration challenges, anticipate roadblocks, and highlight productive synergies across QC, HPC, cloud platforms, and network security. Drawing from both classical computing principles and quantum-specific insights, we present a roadmap for co-design that supports robust hybrid architectures. Our approach offers concrete metrics for assessing dependability, provides design guidance for engineers working at the QC-HPC interface, and surfaces new engineering questions around complexity, scale, and fault tolerance. Ultimately, designing for dependability is key to realizing practical, scalable QHPC systems and accelerating the broader quantum ecosystem capable of translating quantum promises into actual application delivery.