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

Quantum Federated & Privacy-Preserving Learning

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

Quantum Finance & Economic Systems

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

Quantum Cybersecurity & Trustworthy AI

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

Quantum Code Generation, LLMs & Automation

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

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.

Quantum Optimization

Explores quantum optimization algorithms such as QAOA and VQE for complex decision problems including scheduling, routing, and finance.

Quantum AI Foundations

Provides conceptual foundations, surveys, and overviews of QML trends, open challenges, and future directions.

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