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Realizing Error Suppression in Partially Fault-Tolerant Quantum Simulations with IBM Quantum Computers

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abstract

Quantum error-detecting codes offer a near-term path for improving the performance of quantum simulations on noisy hardware. Using IBM's superconducting quantum computer ibm_boston, we show that partially fault-tolerant encoded quantum simulations of the Ising model in 1+1D and 2+1D outperform their unencoded counterparts in estimating local observables. To represent 42 logical qubits on the heavy-hex quantum processor, 21 blocks of the [[4, 2, 2]] Iceberg code and up to 136 physical qubits are used. By pairing fault-tolerant syndrome extraction with non-fault-tolerant logical operations, this scheme preserves many of the benefits of error detection while avoiding the overhead typically required for a fully fault-tolerant logical gate set. The encoding's square logical connectivity, together with the freedom to place logical qubits within each block, enables simulations of a 2D spatial lattice with lower circuit depth than the unencoded implementation requires. We introduce Observable-Ranked Postselection, a selective-filtering technique based on syndrome correlations that recovers reliable results without the prohibitive shot loss of full syndrome postselection. Under the cumulative effect of device errors, this encoding improves local-observable accuracy over the unencoded baseline by 2-6% at intermediate times in 1+1D simulations, growing with circuit depth to over 200% in 2+1D at the latest times studied.

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representative citing papers

The Utility of Sparse Error Detection in Quantum Simulations

quant-ph · 2026-08-03 · conditional · novelty 6.0

Sparse error detection in small Iceberg codes reduces systematic errors in simulated Schwinger-model observables under depolarizing noise, with diminishing returns after a few detection layers.

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  • The Utility of Sparse Error Detection in Quantum Simulations quant-ph · 2026-08-03 · conditional · none · ref 94 · internal anchor

    Sparse error detection in small Iceberg codes reduces systematic errors in simulated Schwinger-model observables under depolarizing noise, with diminishing returns after a few detection layers.