Ratun Rahman

Ratun Rahman

he/him/his

Ph.D. Candidate | Quantum Computing · Machine Learning · Distributed AI

Research

My research develops reliable and scalable learning systems for noisy, heterogeneous, and distributed environments. The central theme is learning under uncertainty and system constraints, spanning both quantum and classical machine learning.

1. Noise-Resilient Quantum Machine Learning

I study how noise, finite-shot measurement, barren plateaus, circuit depth, and hardware constraints affect quantum learning systems. My work develops methods that improve robustness and resource efficiency for variational quantum algorithms and quantum neural networks.

Representative topics

  • Quantum neural networks and QCNNs
  • Barren-plateau mitigation
  • Quantum kernels and hybrid quantum-classical learning
  • Resource-efficient quantum learning
  • Bayesian and uncertainty-aware mitigation

Representative work

  • Quantum Convolutional Neural Networks: A Survey on Architectures, Applications, and Future Directions — IEEE TNNLS, 2026
  • Escaping Barren Plateaus in Variational Quantum Algorithms Using Negative Learning Rate in Quantum Internet of Things — IEEE IoT Journal, 2025
  • Readout Noise Mitigation with Bayesian Methods for Quantum Neural Networks — TNNLS, revision under review
  • On Resource-Efficient Quantum Convolutional Neural Networks with Provable Convergence Guarantees — TNNLS, under review

2. Quantum Error Mitigation & Hardware-Aware Learning

I develop adaptive and operation-aware methods for mitigating noise on NISQ hardware. This research includes zero-noise extrapolation, readout mitigation, dynamical decoupling, noise-aware training, and cost-aware mitigation selection.

A key focus is validating methods beyond simulation. I have run real-device experiments on the Rigetti Cepheus-1-108Q superconducting processor through Amazon Braket, including a 240-task adaptive ZNE study.

Representative work

  • Quantum Noise Mitigation with Adaptive Zero-Noise Extrapolation: A Contextual Multi-Armed Bandits Approach — IEEE JSAC, 2026
  • Layer-Resolved Noise Degradation and Adaptive Dynamical Decoupling in Quantum Neural Networks — Physical Review A submission, 2026
  • Operation-Resolved Zero-Noise Extrapolation for Quantum Neural Networks with Qubit Reuse — Quantum Machine Intelligence submission, 2026
  • Language-Model-Aware Reward Shaping for Contextual-Bandit Adaptive Zero-Noise Extrapolation — AAAI 2027 submission

Software: CMAB-ZNE · QuNoise

3. Quantum Federated & Distributed Learning

My research investigates federated learning across quantum clients with heterogeneous data, devices, noise conditions, computational capabilities, and participation patterns. I develop personalized, privacy-aware, and heterogeneity-aware methods for distributed quantum learning.

Research challenges

  • Non-IID data and client personalization
  • QPU and circuit heterogeneity
  • Quantum noise and calibration drift
  • Sporadic client participation
  • Privacy-preserving distributed quantum learning
  • Multimodal quantum federated learning

Representative work

  • Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach — IEEE Transactions on Computers, 2026
  • Towards Personalized Quantum Federated Learning for Anomaly Detection — IEEE TNSE, 2025
  • Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions — IEEE Network, 2025
  • SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization — IEEE QCE, 2025

Software: SimQFL

4. Quantum Sensing & Distributed Quantum Systems

I investigate collaborative and federated quantum sensing under heterogeneous sensing conditions and quantum noise. This work uses metrics such as Fisher Information and the Cramér–Rao Bound to evaluate precision, robustness, and information sharing.

Representative work

  • Toward High-Precision Variational Quantum Sensing via Asynchronous Federated Learning — IEEE QCE, 2026
  • Differential Privacy for Distributed Quantum Sensing via Quantum Noise — IEEE QCE, 2026
  • Toward Optimized Variational Quantum Sensing in Distributed Networks — IEEE Network, under review

Software: QuSenseSim

5. Trustworthy AI, Federated Learning & Structured Reasoning

Beyond quantum computing, I work on personalized and probabilistic federated learning, multimodal learning, structured reasoning, cybersecurity, and distributed AI. This research studies model personalization, uncertainty, data/system heterogeneity, privacy, and communication constraints.

Representative work

  • Rethinking Structured Generation: Can Graph-Based Reasoning Resolve Ambiguity? — NeurIPS 2026
  • Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization — IEEE TETCI, 2026
  • Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning — IEEE IoT Journal, 2025
  • Improved Modulation Recognition Using Personalized Federated Learning — IEEE TVT, 2024
  • Multimodal Federated Learning with Model Personalization — NeurIPS OPT, 2024

Research Infrastructure

My research workflows use Qiskit, Qiskit Aer, PennyLane, Amazon Braket, PyTorch, Python, C++, Linux, CUDA/GPU acceleration, and HPC/distributed execution. I emphasize reproducible benchmarking, ablation studies, statistical evaluation, and real-hardware validation where appropriate.