Week 2 – Testing Random Quantum Circuits

04 Jul 2025 - Miles MAL - reinforcement learning, quantum circuit optimisation, qiskit

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 & compared key reference papers to structure my work around	✔
Generated random Quantum Circuit datasets	✔

Next week’s plan

• Begin to generate datasets which match IBM’s hardware constraints (i.e. 1D Chain Topology - allowing connectivity between adjacent qubits)
• Define my environment: states, actions, rewards
• Try a simple reward with just optimised depth count (like in the key paper)
• Convert my circuit generator into a function which feeds circuits into RL environment