A fundamental bottleneck across modern computational imaging and high-dimensional sensing is the conventional decoupled acquisition-reconstruction hierarchy, which subjects high-dimensional spatial sensing to the classical shot-noise limit and intense computational overhead. As a prominent manifestation of this limitation, single-pixel imaging (SPI) suffers severely from this paradigm. We reinvent this paradigm by introducing a quantum-operator-based SPI theoretical framework driven by coherent signal processing. Within this architecture, the spatial inverse problem is analytically mapped into the eigenvalue spectrum of a quantum operator via tailored light-matter interactions. By analytically synthesizing non-linear reconstruction operators via ultra-shallow quantum architectures, we theoretically demonstrate an exponential decay of spatial approximation errors, completely bypassing traditional linear solvers. This operator-space embedding not only shields reconstruction from noise via a strategic error-saturation zone but also bridges the gap from classical shot-noise scaling $\mathcal{O}(1/\sqrt{N_{\text{ph}}})$ to the ultimate Heisenberg limit $\mathcal{O}(1/N_{\text{ph}})$ . Crucially, while formulated within SPI, this coherent operator paradigm fundamentally extends to general photon-starved, high-dimensional imaging modalities. This work establishes a universal theoretical blueprint for next-generation quantum-enhanced sensing, shifting the paradigm from iterative optimization to coherent operator-space evolution.A fundamental bottleneck across modern computational imaging and high-dimensional sensing is the conventional decoupled acquisition-reconstruction hierarchy, which subjects high-dimensional spatial sensing to the classical shot-noise limit and intense computational overhead. As a prominent manifestation of this limitation, single-pixel imaging (SPI) suffers severely from this paradigm. We reinvent this paradigm by introducing a quantum-operator-based SPI theoretical framework driven by coherent signal processing. Within this architecture, the spatial inverse problem is analytically mapped into the eigenvalue spectrum of a quantum operator via tailored light-matter interactions. By analytically synthesizing non-linear reconstruction operators via ultra-shallow quantum architectures, we theoretically demonstrate an exponential decay of spatial approximation errors, completely bypassing traditional linear solvers. This operator-space embedding not only shields reconstruction from noise via a strategic error-saturation zone but also bridges the gap from classical shot-noise scaling $\mathcal{O}(1/\sqrt{N_{\text{ph}}})$ to the ultimate Heisenberg limit $\mathcal{O}(1/N_{\text{ph}})$. Crucially, while formulated within SPI, this coherent operator paradigm fundamentally extends to general photon-starved, high-dimensional imaging modalities. This work establishes a universal theoretical blueprint for next-generation quantum-enhanced sensing, shifting the paradigm from iterative optimization to coherent operator-space evolution.

quant-pharXiv:2608.21783Aug 25, 20260 citations

Reconstructions of quantum theory: methodology and the role of axiomatization

Jessica Oddan

European Journal for Philosophy of ScienceApr 30, 20241 citation

Perspectives in quantum theory

Wolfgang Yourgrau, Alwyn van der Merwe, Alfred Landé

Dover Publications eBooksJan 1, 1971247 citations

On the Theory of Quantum and Towards Practical Computation

Robert Kudelić

Quantum computing exposes the brilliance of quantum mechanics through computer science and, as such, gives oneself a marvelous and exhilarating journey to go through. This article leads along that journey with a historical and current outlook on quantum computation that is geared toward computer experts but also to experts from other disciplines as well. It is an article that will bridge the vast gap between classical and quantum computation and open an entering wedge through which one will be able to both bring himself up to speed on quantum computation and, intrinsically, in a straightforward manner, become acquainted with it. We are indeed in luck to be living in an age where computing is being reinvented, and not only seeing history in the making firsthand but, in fact, having the opportunity to be the ones who are reinventing--and that is quite a thought.Quantum computing exposes the brilliance of quantum mechanics through computer science and, as such, gives oneself a marvelous and exhilarating journey to go through. This article leads along that journey with a historical and current outlook on quantum computation that is geared toward computer experts but also to experts from other disciplines as well. It is an article that will bridge the vast gap between classical and quantum computation and open an entering wedge through which one will be able to both bring himself up to speed on quantum computation and, intrinsically, in a straightforward manner, become acquainted with it. We are indeed in luck to be living in an age where computing is being reinvented, and not only seeing history in the making firsthand but, in fact, having the opportunity to be the ones who are reinventing--and that is quite a thought.

arXiv:2403.09682Feb 7, 20243 citations

\mathcal{O}(1/\sqrt{N_{\text{ph}}})

to the ultimate Heisenberg limit

\mathcal{O}(1/N_{\text{ph}})

. Crucially, while formulated within SPI, this coherent operator paradigm fundamentally extends to general photon-starved, high-dimensional imaging modalities. This work establishes a universal theoretical blueprint for next-generation quantum-enhanced sensing, shifting the paradigm from iterative optimization to coherent operator-space evolution.