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arXiv:2604.13177v1 Announce Type: new Abstract: Quantum computational sensing (QCS) combines quantum sensing with quantum computing to extract task-relevant information from the physical world. QCS can in principle achieve an accuracy advantage for specific tasks versus the alternative of raw-signal estimation using conventional quantum sensing followed by task-specific classical postprocessing. Here we report the experimental demonstration of quantum computational displacement sensing (QCDS) wi
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Quantum Physics
[Submitted on 14 Apr 2026]
Quantum computational displacement sensing
Sridhar Prabhu, Saeed A. Khan, Xingrui Song, Mathieu Ouellet, Ryotatsu Yanagimoto, Saswata Roy, Alen Senanian, Logan G. Wright, Valla Fatemi, Peter L. McMahon
Quantum computational sensing (QCS) combines quantum sensing with quantum computing to extract task-relevant information from the physical world. QCS can in principle achieve an accuracy advantage for specific tasks versus the alternative of raw-signal estimation using conventional quantum sensing followed by task-specific classical postprocessing. Here we report the experimental demonstration of quantum computational displacement sensing (QCDS) with a superconducting circuit comprising a qubit coupled to an oscillator. We consider binary classification sensing tasks, where the goal is to predict the class label of a single complex-valued displacement sensed once by the oscillator. Rather than estimating the displacement, our computational-sensing protocol -- using parameterized quantum circuits before and after sensing -- attempts to determine the binary class label using quantum processing and map it onto the ground or excited state of the qubit. A single measurement of the qubit directly outputs the prediction. We implemented circuits with up to 24 entangling gates and 38 free parameters, which were trained in silico. We show that increasing the circuit depth systematically improves expressivity and classification accuracy. We experimentally obtained an accuracy advantage over a suite of protocols that first use conventional quantum sensing to estimate the displacement before using classical postprocessing to perform prediction. For certain tasks, our protocol achieves a 15-percentage-points higher classification accuracy than the best conventional approach considered. Our results establish the feasibility of quantum computational sensing with noisy superconducting hardware and illustrate how integrating quantum computation with quantum sensing can enhance performance when the goal is to estimate a property or function of a signal rather than to estimate the signal.
Subjects: Quantum Physics (quant-ph)
Cite as: arXiv:2604.13177 [quant-ph]
(or arXiv:2604.13177v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2604.13177
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Submission history
From: Sridhar Prabhu [view email]
[v1] Tue, 14 Apr 2026 18:01:26 UTC (25,600 KB)
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