This new approach emphasizes noise-engineered quantum fisher information for sensing, suggesting significant performance improvements in NISQ devices.
Quantum Fisher information (QFI) stands at a critical inflection point: transitioning from a theoretical bound to a measurable diagnostic that enables unprecedented sensing capabilities in noisy intermediate-scale quantum (NISQ) devices. Here, we present a paradigm shift where QFI becomes both the metric and mechanism for achieving practical quantum advantage. We introduce three transformative concepts: (i) noise-engineered QFI that exploits environmental correlations rather than fighting them, (ii) adaptive multiparameter sensing using real-time QFI feedback, and (iii) hybrid classical-quantum estimation that scales QFI extraction to thousands of qubits. Through quantitative analysis of emerging platforms, we demonstrate that current experiments are achieving 2-10× classical performance bounds, with a clear pathway to 100× advantage by 2030. This perspective reframes QFI from a passive bound to an active design principle, establishing sensing as quantum computing's first scalable application.
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Volkan Erol (2025) studied this question.
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