Extends n-dimensional topological stabilizer codes to Clifford hierarchy versions corresponding to non-Abelian gauge theories and constructs transversal gates at the (n+1)th Clifford level.
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Sur- face code quantum computing by lattice surgery
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Lattice-surgery scheduling is mapped to 3D path embedding and solved with look-ahead Dijkstra projection, yielding 3.8x lower execution time on quantum phase estimation benchmarks versus greedy scheduling.
A design framework that converts an eight-to-three CCZ distillation protocol into a joint-measurement surface-code layout using smaller patches, claimed to cut spatial cost versus Gidney-Fowler while preserving leading-order error suppression and single-fault detection.
MCMit proposes a constant-latency multi-control branch instruction, transformer and CNN discriminators, plus static MCM elimination and stochastic branching, evaluated on Qubic with QPU traces to cut latency by 70% and logical error rates by up to 9.4x.
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
New combinatorial proofs and circuit designs for quantum error correction reduce physical qubit overhead by up to 10x and time overhead by 2-6x for codes including Steane, Golay, and surface codes.
Three architectural types for fault-tolerant distributed quantum computing exhibit distinct scaling of Bell-pair consumption and generation attempts with code distance in planar surface and toric codes.
A two-level decoder scheduling framework reduces classical processing requirements for quantum error correction by 10-40% on fault-tolerant benchmarks by managing bursty workloads as shared resources.
Speculative decoding for QEC reduces waiting time for prior windows but yields gains only under high speculation accuracy and low decoder latency; non-speculative decoders outperform under other conditions across gate speeds and platforms.
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.
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