Implementing Surface Codes on IBM Brussels: A New Perspective
Surface codes are currently the most promising candidate for fault-tolerant quantum computing due to their high error threshold.
Recently, I've been experimenting with the d=3 surface code. The logical qubit is encoded in a grid of physical qubits where the stabilizers are:
Zp=i∈p∏Zi,Xp=i∈p∏Xi
We're seeing a significant improvement in logical error rates as we scale, but the decoders (matching algorithms) are still a bottleneck. What's everyone's favorite MWPM implementation?
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I'm skeptical about the 'non-Markovian' claims. Most noise we see in transmons is dominated by T1 relaxation which is fairly Markovian at the scale of a single gate. Have you subtracted the pulse distortion effects?
Great writeup! The Bell state formulation is spot on. One thing to add: entanglement doesn't allow faster-than-light communication because you can't control which state is measured — only correlations are preserved.
From a hardware perspective, the wiring density for a surface code is a nightmare. How does the 0.4% threshold change if we account for the heat load of the additional microwave lines?
I've been looking at the syndrome extraction cycles for the distance-3 surface code. Is there a specific reason you didn't use the rotated layout to save on qubit count?
PyMatching (specifically the sparse blossom implementation in PyMatching 2) stands out as my favorite for MWPM due to its extreme speed, Python integration, and avoidance of all-to-all Dijkstra searches.