Shadow Engineering of Quantum Processes

• URL: http://arxiv.org/abs/2606.12035v1
• Date: Wed, 10 Jun 2026 12:57:15 GMT
• Title: Shadow Engineering of Quantum Processes
• Abstract summary: Characterizing quantum processes is essential for benchmarking, error diagnosis, and algorithm verification.<n>We introduce shadow engineering, a framework encoding the classical shadows of processes into sparse transfer matrices.<n>We demonstrate the framework's effectiveness and practicality on a superconducting quantum processor for typical applications.
• Score: 4.77219085316665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Characterizing quantum processes is essential for hardware benchmarking, error diagnosis, and algorithm verification. While recent work [PRX QUANTUM \textbf{4}, 040337 (2023)] extended classical shadows from quantum state to quantum process, enabling efficient single-channel $\mathcal{E}$ property prediction, its applicability to composite processes $f(\mathcal{E}_1, \mathcal{E}_2,\cdots, \mathcal{E}_k)$ remains unexplored. We introduce shadow engineering, a framework encoding the classical shadows of processes into sparse transfer matrices to predict $f(\mathcal{E}_1, \mathcal{E}_2,\cdots, \mathcal{E}_k)$ properties with proven polynomial sample complexity, matching single-channel efficiency while exponentially lower than quantum process tomography. Crucially, this approach repurposes existing $\mathcal{E}_m$-shadow data without physical execution of $f(\mathcal{E}_1, \mathcal{E}_2,\cdots, \mathcal{E}_k)$, enabling flexible quantum process characterization with minimal hardware overhead. We demonstrate the framework's effectiveness and practicality on a superconducting quantum processor for typical applications such as error mitigation and Hamiltonian dynamical simulation. This framework unlocks new capabilities for predicting complex quantum behaviors without physical re-execution, with immediate applications in near-term device calibration and quantum simulation.

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