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Trapped Ion Quantum Computing
Far from Perfect: Quantum Error Correction with (Hyperinvariant) Evenbly Codes
arXiv
Authors: Matthew Steinberg, Junyu Fan, Robert J. Harris, David Elkouss, Sebastian Feld, Alexander Jahn
Year
2024
Paper ID
65316
Status
Preprint
Abstract Read
~2 min
Abstract Words
215
Citations
N/A
Abstract
We introduce a new class of qubit codes that we call Evenbly codes, building on a previous proposal of hyperinvariant tensor networks. Its tensor network description consists of local, non-perfect tensors describing CSS codes interspersed with Hadamard gates, placed on a hyperbolic \{p,q\} geometry with even qgeq 4, yielding an infinitely large class of subsystem codes. We construct an example for a \{5,4\} manifold and describe strategies of logical gauge fixing that lead to different rates k/n and distances d, which we calculate analytically, finding distances which range from d=2 to d sim n2/3. Investigating threshold performance under erasure, depolarizing, and pure Pauli noise channels, we find that the code exhibits a depolarizing noise threshold of about 19.1% in the code-capacity model and 50% for pure Pauli and erasure channels under suitable gauges. We also test a constant-rate version with k/n = 0.125, finding excellent error resilience (about 40%) under the erasure channel. Recovery rates for these and other settings are studied both under an optimal decoder as well as a more efficient but non-optimal greedy decoder. We also consider generalizations beyond the CSS tensor construction, compute error rates and thresholds for other hyperbolic geometries, and discuss the relationship to holographic bulk/boundary dualities. Our work indicates that Evenbly codes may show promise for practical quantum computing applications.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- We introduce a new class of qubit codes that we call Evenbly codes, building on a previous proposal of hyperinvariant tensor networks.
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