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This research presents a scalable learning framework that characterizes noisy n-qubit circuits with logarithmic-depth complexity, overcoming the exponential resource barriers of traditional quantum tomography. By offering an input-agnostic approach for both unital and non-unital noise channels, the work provides a critical methodological advance for validating large-scale quantum processors.
New method characterizes noisy qubits even in deep circuits
Characterizing large quantum systems with noisy qubits is difficult, but this work introduces a learning framework for n-qubit circuits under single-qubit noise.
Researchers from Beijing Normal University, The University of Western Australia, and Peking University have developed a new learning framework to characterize noisy quantum circuits with a complexity that scales far more efficiently than existing methods. The work introduces a structure-agnostic approach for analyzing n-qubit quantum circuits experiencing single-qubit noise, achieving efficient learnable representations in logarithmic-depth circuits, a significant departure from the exponential resource demands of conventional quantum tomography. This framework applies to both unital and non-unital noisy channels without assuming any specific input quantum state, offering a versatile tool for characterizing large-scale noisy quantum devices. The results provide a scalable route toward addressing a key bottleneck in the development of quantum technologies. Quantum states experiencing unital noise channels now possess a demonstrably efficient learnable representation, a finding that eases a critical bottleneck in characterizing larger quantum systems. The work establishes that…
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