aiqbench analysis
This study investigates how coherent spatial disorder affects the computational hardness of large-scale IQP architectures, identifying specific thresholds where quantum advantage degrades into classical simulability. By simulating systems up to 576 qubits, researchers establish quantitative scaling laws that define error budgets for maintaining complexity in near-term devices without relying on environmental decoherence. These findings are critical for understanding the robustness of fault-tolerant designs and the limits of practical quantum supremacy demonstrations.
Coherent-disorder-driven complexity transitions in a quantum-advantage architecture
arXiv:2607.18938v1 Announce Type: new Abstract: 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.
Abstract: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.
arXiv:2607.18938 [quant-ph]
(or arXiv:2607.18938v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.18938 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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