Quantum ML Research Areas

QML Architectures & Optimization

Focuses on developing scalable, noise-resilient, and efficient quantum neural network architectures. Explores trainability, barren plateaus, and circuit depth optimization for hybrid and variational models.

Paper 1: QAS-QTNs: Curriculum Reinforcement Learning-Driven Quantum Architecture Search for Quantum Tensor Networks

Paper 2: Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates

Paper 3: FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithm

Quantum Federated & Privacy-Preserving Learning

Develops distributed quantum learning frameworks ensuring data confidentiality, encryption, and privacy through federated and homomorphic approaches.

Paper 1: MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption

Paper 2: FL-QDSNNs: Federated Learning with Quantum Dynamic Spiking Neural Networks

Quantum Finance & Economic Systems

Applies quantum models to financial prediction, fraud detection, portfolio optimization, and loan analysis, combining hybrid QML with decision-support frameworks.

Paper 1: Quantum Portfolio Optimization with Expert Analysis Evaluation

Paper 2: QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction

Paper 3: LEP-QNN: Loan Eligibility Prediction Using Quantum Neural Networks

Quantum Cybersecurity & Trustworthy AI

Investigates quantum-enhanced defense mechanisms, adversarial attack resilience, and trustworthy AI for anomaly detection and secure computation.

Paper 1: AdvQuNN: A Methodology for Analyzing the Adversarial Robustness of Quanvolutional Neural Networks

Paper 2: RobQuNNs: A Methodology for Robust Quanvolutional Neural Networks against Adversarial Attacks

Quantum Code Generation, LLMs & Automation

Builds quantum-focused LLMs, datasets, and benchmarks to automate quantum code generation and improve programming efficiency.

Paper 1: PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation

Paper 2: QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges

Quantum AI for Applications

Develops quantum AI models applied to real-world domains including imaging, healthcare, and autonomous systems with focus on performance and generalization

Paper 1: QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution

Paper 2: Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection