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As part of the Theory team, you will implement numerical simulations for selected directional codes and benchmark several decoders on these codes under a relevant noise model. The internship will provide a comparative study: logical error rates vs. physical error rates (and runtime) and allow for the investigation of the possibility of increasing the encoding rates with increased connectivity.
Job Responsibility:
Understand the basics concepts of Quantum Error Correction (QEC), e.g. data qubits, measure qubits, parity checks, logical qubits, circuit level noise, decoding algorithms
Understand the construction of directional codes
Select a few candidates for benchmarking, with around 60 data qubits
Implement their parity check matrices in Python, generate their syndrome extraction circuit using Stim and insert errors to the circuit (using Callisto-inspired noise model)
Compare their logical error rates via Monte Carlo simulations, using a few decoders (eg.BP+OSD,Tesseract,Relay-BP)
Explore machine learning based code discovery to extend the construction of these codes
Requirements:
You speak English fluently
You have strong background in quantum physics and linear algebra
You have completed courses in quantum information or quantum computing
You have Python programming skills
You are curious to learn more about quantum error correction, and work in a start-up environment