Experimental exploration of quantum algorithms for machine learning. Testing hybrid quantum-classical approaches on NISQ devices using Qiskit and PennyLane.
Overview
This experimental project investigates the potential of near-term quantum computers for machine learning tasks. The work focuses on variational quantum algorithms that can be implemented on current noisy intermediate-scale quantum devices.
Research Directions
- Variational quantum eigensolvers for optimization problems
- Quantum neural networks with parameterized circuits
- Hybrid classical-quantum training algorithms
- Noise mitigation techniques for NISQ devices
Results
Experiments have been conducted on various quantum hardware platforms including IBM Quantum, Google Quantum AI, and Rigetti systems. Results suggest promising applications in specific optimization domains while highlighting current limitations of quantum hardware.