Curator's Take
AI Commentary
This article shows that even coherent spatial disorder—tiny random variations in two‑qubit gate angles—can drive a seemingly hard IQP sampling task into regimes that are efficiently classically simulable, revealing a previously underappreciated route by which near‑term quantum advantage can be lost. By combining large‑scale tensor‑network simulations with finite‑size scaling, the authors map out precise disorder and dephasing thresholds that delineate the “hard” versus “easy” phases, providing concrete error‑budget guidelines for experimentalists building square‑lattice processors. The work complements recent studies on decoherence‑induced fragility of random‑circuit supremacy and underscores that controlling coherent imperfections will be as critical as suppressing noise to sustain quantum advantage.
— Mark Eatherly
Summary
While decoherence is known to erode classical hardness in quantum random sampling, the impact of coherent spatial disorder remains an open question. We study a square-lattice instantaneous quantum polynomial-time (IQP) architecture subject to two-qubit gate-angle disorder and single-qubit dephasing using exact tensor-network simulations up to 576 qubits. For finite systems without dephasing, increasing disorder drives two consecutive crossovers toward classical simulability: the output distribution first loses anticoncentration, and then the tensor-network simulation cost drops from exponential to polynomial as entanglement is suppressed. The finite-size scaling collapses are consistent with continuous transitions in the large-system limit. Dephasing further reduces the complexity. We characterize the computationally hard regime through scaling laws that provide quantitative error-budget bounds for realistic near-term devices.