Neural Architecture Search for Quantum Error Correction

Abstract

Mesklin Research Labs evaluates learned code selection for quantum error correction and shows how neural search can identify low-overhead candidates faster than manual tuning.

Key Highlights

• Reduced qubit overhead by discovering codes with more efficient parity structures
• Search loops converged faster when guided by benchmark-aware reward shaping
• Results were strongest on noisy intermediate-scale processor simulations

Methodology

1. 1Used neural architecture search to evaluate candidate error-correcting codes
2. 2Benchmarked against standard surface-code baselines under varying noise models
3. 3Validated against simulated fidelity and recovery-cost metrics

Findings

• Learned policies found efficient code families in far fewer iterations than manual sweeps
• The best candidates balanced recovery cost with resilience under correlated noise
• The approach is well suited for research teams exploring early-stage quantum systems