Simulation and Analysis of Quantum Search Algorithms under Noise

Primary supervisor

Charith Jayasekara

Quantum algorithms such as Grover’s Search promise quadratic speed-ups over classical search but are sensitive to hardware noise.
This project will use Qiskit Aer to model realistic noise channels (decoherence, gate and readout errors) and evaluate their impact on algorithmic performance.
By varying circuit depth, qubit count, and noise parameters, the student will identify conditions under which quantum advantage remains achievable and investigate possible error-mitigation strategies.

Aim/outline

  • Implement Grover’s algorithm and selected variants using Qiskit.
  • Introduce controlled noise models to simulate near-term hardware behaviour.
  • Quantify degradation of success probability and circuit fidelity.
  • Evaluate basic mitigation techniques such as measurement error correction or zero-noise extrapolation.
  • Produce an analytical and visual comparison between ideal and noisy executions.

Required knowledge

  • Python programming (NumPy, matplotlib).
  • Basic linear algebra and probability.
  • Introductory quantum computing concepts (gates, qubits, measurement).
  • Familiarity with Qiskit tutorials or willingness to learn.