Week 1 – Preprocessing and Paper Reading

27 Jun 2025 - Miles MAL - reinforcement learning, preprocessing, qiskit, quantum computing

What is my project about?

I am building a machine learning model that predicts errors, and using these to optimise quantum circuits.

Why use Quantum Computers, and what problems exist?

• Quantum computers promise revolutionary speedups due to quantum mechanical effects, however its reliability is limited by noisy hardware. My project aims to use AI to increase efficiency and error-awareness in Noisy Intermediate Scale Quantum (NISQ) devices, prior to Quantum Error Correction (QEC).

Overall Goal

• Develop an AI-enhanced quantum circuit optimiser.
• Predict the effects of quantum noise (qubit decoherence, quantum gate and measurement errors)
• Recommend optimisations to improve the efficiency of quantum circuits.

Tasks completed this week

Task	Checklist
Identified Machine Learning Methods for Quantum Circuit Optimisation (including Reinforcement Learning and Graph Neural Networks, amongst other methods)	✔
Practiced making Quantum Circuits using IBM’s Qiskit Software	✔

Next week’s plan

• Implement Quantum Algorithms and Baseline Simulations: Run ideal, noiseless simulations to establish a performance baseline of Grover’s algorithm. Introduce basic quantum noise and compare performance of ideal and noisy simulations.