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Planar quantum low-density parity-check codes with open boundaries
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abstract
Although high-threshold and low-overhead quantum low-density parity-check (qLDPC) codes, such as bivariate bicycle (BB) codes, can reduce the physical-qubit cost by an order of magnitude compared to the Kitaev toric code, their torus layout remains difficult for physical implementation. In this work, we introduce the first systematic procedure to convert BB codes into fully planar, open-boundary qLDPC codes, preserving their performance. We present planar code families with logical dimensions $6 \leq k\leq13$, e.g., $[[78, 6, 6]]$, $[[107, 7, 7]]$, $[[268, 8, 12]]$, $[[405, 9, 15]]$, $[[348, 10, 13]]$, $[[450, 11, 15]]$, $[[386, 12, 12]]$, $[[362, 13, 11]]$, all with geometrically local weight-6 stabilizers. Allowing weight-8 stabilizers produces a $[[282,12,14]]$ code, exhibiting an efficiency metric ($kd^2/n$) an order of magnitude higher than the surface code. The construction combines boundary anyon condensation with the ``lattice grafting'' optimization, yielding high-performance qLDPC codes natively compatible with planar hardware architectures. It also uncovers Sierpinski-type fractal logical operators whose distance scales with the fractal area on finite lattices. These planar qLDPC codes provide an implementable route to resource-efficient, high-threshold fault tolerance and a flexible framework for future code design on realistic two-dimensional hardware.
Forward citations
Cited by 3 Pith papers
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A quantum bus connects many logical qubits through a gauge-code strip, with a claimed factor O(d) reduction in qubit overhead for long-range logical interactions.
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Louvre: Relaxing Hardware Requirements of Quantum LDPC Codes by Routing with Expanded Quantum Instruction Set
Louvre cuts the qubit connectivity degree of generalized bicycle codes by up to one-third using iSWAP-based routing, achieving comparable simulated logical error rates.
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Conjoining only bit-flip and phase-flip repetition codes can generate any CSS code, and an iterative algorithm grows sparse subsystem codes with kd^2=O(n) worst-case scaling.
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