Vine codes generalize directional codes to open planar boundaries, delivering up to 28% fewer data/measure qubits at circuit distance 7 and better simulated performance than the surface code at 10^{-3} noise while using fewer total qubits.
The XZZX surface code.Nature communications, 12(1):2172
4 Pith papers cite this work, alongside 247 external citations. Polarity classification is still indexing.
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quant-ph 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
Protocol learns single-qubit Z-twirled MCM instrument parameters from three repeated measurements on mixed input, yielding ~100x better Pauli-observable prediction than confusion-matrix models on IBM processors.
Clifford-deformed zero-rate LDPC codes achieve code-capacity thresholds approaching 50% under i.i.d. pure dephasing when the number of biased logical operators scales slower than distance or overlaps satisfy stated conditions, with new examples from tile codes.
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.
citing papers explorer
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Vine Codes: Low-Overhead Quantum LDPC Codes on a Planar Square Grid
Vine codes generalize directional codes to open planar boundaries, delivering up to 28% fewer data/measure qubits at circuit distance 7 and better simulated performance than the surface code at 10^{-3} noise while using fewer total qubits.
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Learning Mid-circuit Measurement Backaction from Three Repeated Measurements
Protocol learns single-qubit Z-twirled MCM instrument parameters from three repeated measurements on mixed input, yielding ~100x better Pauli-observable prediction than confusion-matrix models on IBM processors.
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Clifford-deformed zero-rate LDPC codes with 50% biased noise thresholds
Clifford-deformed zero-rate LDPC codes achieve code-capacity thresholds approaching 50% under i.i.d. pure dephasing when the number of biased logical operators scales slower than distance or overlaps satisfy stated conditions, with new examples from tile codes.
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Maximum Likelihood Decoding of Quantum Error Correction Codes
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.