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This paper establishes rigorous information-theoretic lower bounds for quantum compressed sensing, demonstrating that while quantum parallelism can reduce effective measurement complexity compared to classical limits, it does not eliminate the fundamental sampling requirements for exact support recovery. The analysis clarifies the specific roles of quantum resources in measurement science, showing that multimode detection improves experimental efficiency but cannot bypass the core complexity dictated by the sparse signal structure. These findings are critical for understanding the true potential and limitations of quantum advantages in sensing applications beyond general-purpose computing.
Measurement Complexity of Quantum Compressed Sensing
arXiv:2610.08234v1 Announce Type: new Abstract: Conventional compressed sensing (CS) has a measurement lower bound of M = {\Omega}(K log(N/K)) under non-adaptive measurements. Recent experiments on quantum compressed sensing (QCS) have reported numbers of measurements below this classical lower bound. In this work, we establish lower bounds on the measurement complexity of QCS from an information-theoretic and quantum-physical perspective. QCS exploits quantum parallelism, enabling a unitary domain-alignment evolution to act on a superposition of all N basis states and map the sparse basis onto the measurement basis in a one-to-one manner. Consequently, the locations of nonzero components are explicitly carried by measurement-index labels. If only basic statistical information about the K nonzero components is required, the total number of effective index samples satisfies the coarse necessary condition Ms = {\Omega}(K). Under independent single-index projective measurements, when the support is unknown, the nonzero probabilities satisfy pn = {\Theta}(1/K), and exact support recovery is required with a fixed success probability, the sampling-coverage requirement leads to Ms = {\Theta}(K ln K). If each nonzero amplitude coefficient is further required to achieve a coordinate-wise relative root-mean-square error {\epsilon}, the optimal effective index sample complexity is Ms = {\Theta}(K ln K + K/{\epsilon}2), which reduces to {\Theta}(K ln K) for fixed {\epsilon}. Multimode photon-number-resolving detection can reduce the number of experimental rounds but not the total effective index sample complexity. These results show that quantum parallelism is not only a core resource for quantum computing but can also play a transformative role in measurement science.
Abstract:Conventional compressed sensing (CS) has a measurement lower bound of M = {\Omega}(K log(N/K)) under non-adaptive measurements. Recent experiments on quantum compressed sensing (QCS) have reported numbers of measurements below this classical lower bound. In this work, we establish lower bounds on the measurement complexity of QCS from an information-theoretic and quantum-physical perspective. QCS exploits quantum parallelism, enabling a unitary domain-alignment evolution to act on a superposition of all N basis states and map the sparse basis onto the measurement basis in a one-to-one manner. Consequently, the locations of nonzero components are explicitly carried by measurement-index labels. If only basic statistical information about the K nonzero components is required, the total number of effective index samples satisfies the coarse necessary condition Ms = {\Omega}(K). Under independent single-index projective measurements, when the support is unknown, the nonzero probabilities satisfy pn = {\Theta}(1/K), and exact support recovery is required with a fixed success probability, the sampling-coverage requirement leads to Ms = {\Theta}(K ln K). If each nonzero amplitude coefficient is further required to achieve a coordinate-wise relative root-mean-square error {\epsilon}, the optimal effective index sample complexity is Ms = {\Theta}(K ln K + K/{\epsilon}2), which reduces to {\Theta}(K ln K) for fixed {\epsilon}. Multimode photon-number-resolving detection can reduce the number of experimental rounds but not the total effective index sample complexity. These results show that quantum parallelism is not only a core resource for quantum computing but can also play a transformative role in measurement science.
arXiv:2610.08234 [quant-ph]
(or arXiv:2610.08234v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.08234 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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