Hybrid Quantum-Classical Algorithms & Parameterized Quantum Circuits for High-Dimensional Optimization
Published in Ongoing Research Project, 2026
Quantum Machine Learning (QML) Qiskit & PennyLane Ongoing Research
Research Objectives
As quantum hardware advances through the Noisy Intermediate-Scale Quantum (NISQ) era, combining classical computational resilience with quantum Hilbert-space state representation offers profound opportunities for solving high-dimensional combinatorial and non-convex optimization problems.
- Parameterized Quantum Circuits (PQCs): Engineering shallow-depth ansatz designs resilient to barren plateau phenomena and gate noise.
- Variational Quantum Classifiers (VQCs): Formulating variational quantum algorithms in
PennyLaneandQiskitto benchmark quantum state encoding schemes against classical kernel methods. - Constrained Optimization: Evaluating hybrid quantum-classical optimizers (COBYLA, SPSA, and Adam) on multi-constraint financial and network topology graphs.
Recommended citation: S. M. Mohiuddin Khan Shiam. (2026). "Hybrid Quantum-Classical Algorithms & Parameterized Quantum Circuits for High-Dimensional Optimization." Working Paper / Ongoing Research.