We present a conceptual design for a medical imaging device capable of scanningthe entire human body (0.07 m3) at isotropic cellular resolution (10 μm) in under onesecond (0.22 s), enabling classical scan–transmit–reconstruct teleportation of macro-scopic organisms. The device combines: (i) an 11.7 T superconducting magnet with a0.9 m bore; (ii) a squeezed-state radio-frequency source (10 dB squeezing at 500 MHzvia Josephson Parametric Amplifier); (iii) a 581 GS/s arbitrary waveform generatorencoding 1.28 × 1011 compressed sensing projections; (iv) a quantum-limited homodynedetector array (I/Q, 581 GS/s); (v) a compressed sensing reconstruction engine process-ing 1010 sparse parameters; and (vi) a 10.2 Pb/s fiber-optic data link. No componentviolates known physics; the principal barriers are engineering: a 2× gap in AWG/ADCspeed, a 3×105× gap in real-time CS reconstruction throughput, and scaling Josephsonparametric amplification to 500 MHz. Total estimated cost is $1B with a 25–40 yeardevelopment timeline
We present a conceptual design for a medical imaging device capable of scanningthe entire human body (0.07 m3) at isotropic cellular resolution (10 μm) in under onesecond (0.22 s), enabling classical scan–transmit–reconstruct teleportation of macro-scopic organisms. The device combines: (i) an 11.7 T superconducting magnet with a0.9 m bore; (ii) a squeezed-state radio-frequency source (10 dB squeezing at 500 MHzvia Josephson Parametric Amplifier); (iii) a 581 GS/s arbitrary waveform generatorencoding 1.28 × 1011 compressed sensing projections; (iv) a quantum-limited homodynedetector array (I/Q, 581 GS/s); (v) a compressed sensing reconstruction engine process-ing 1010 sparse parameters; and (vi) a 10.2 Pb/s fiber-optic data link. No componentviolates known physics; the principal barriers are engineering: a 2× gap in AWG/ADCspeed, a 3×105× gap in real-time CS reconstruction throughput, and scaling Josephsonparametric amplification to 500 MHz. Total estimated cost is $1B with a 25–40 yeardevelopment timeline
Computed tomography (CT) is a non-destructive technique for observing internal images and has proven highly valuable in medical diagnostics. Recent advances in quantum computing have begun to influence tomographic reconstruction techniques. The quantum tomographic reconstruction algorithm is less affected by artifacts or noise than classical algorithms by using the square function of the difference between pixels obtained by projecting CT images in quantum superposition states and pixels obtained from experimental data. In particular, by using quantum linear systems, a fast quadratic unconstrained binary optimization (QUBO) model formulation for quantum tomographic reconstruction is possible. In this paper, we formulate the QUBO model for quantum compressed sensing tomographic reconstruction, which is a linear combination of the QUBO model for quantum tomographic reconstruction and the QUBO model for total variation in quantum superposition-state CT images. In our experiments, we used sinograms obtained by using the Radon transform of Shepp-Logan images and body CT images. We evaluate the performance of the new algorithm by reconstructing CT images using a hybrid solver with the QUBO model computed from each sinogram. The new algorithm was able to obtain a solution within 5 projection images for 30 × 30 image samples and within 6 projection images for 60 × 60 image samples, reconstructing error-free CT images. We anticipate that quantum compressed sensing tomographic reconstruction algorithms could significantly reduce the total radiation dose when quantum computing performance advances.
How many measurements are fundamentally required to capture a signal. Shannon's information theory established the bedrock of this question in 1948, the Nyquist Shannon theorem set the first answer, and compressed sensing (CS) rewrote it in 2006 by reducing the required measurement number to M = O(Klog(N/K)) for a K sparse signal. Here, we propose quantum compressed sensing (QCS), a paradigm that reframes signal acquisition as a unitary quantum evolution. By encoding high dimensional signal information into a single quantum probe state, then introducing domain-alignment evolution,a physically realizable unitary transformation that maps the sparse basis directly onto the measurement basis. QCS executes the support-set search at the quantum level without consuming measurement trials. The logarithmic penalty vanishes, compressing the required measurement number from the classical bound to M =O(K) and reducing reconstruction from ill posed optimization to linear estimation. We experimentally validate QCS using frequency and time domain sparse signals, confirming that the measurement number scales linearly with sparsity and decouples entirely from the signal dimension. Our work provides a physical pathway toward ultimate information acquisition efficiency, with broad implications for sensing, imaging, and communication.
Quantum physics has brought enhanced capability in various sensing applications. Despite challenges from noise and loss in the radio-frequency (RF) domain, [Phys. Rev. Lett. 124, 150502 (2020)] demonstrates a route for enhanced RF-receiver empowered by quantum squeezing and entanglement. In this work, we further explore the quantum advantage of imaging in the weak coupling scenario of the RF-photonic receiver. The proposed imaging receiver applies transducer to upconvert the RF signal to optical to enable high-efficiency connection via low-loss fiber networks. The efficient connection therefore increases the synthetic aperture and improves the resolution of the distributed imaging system. To overcome the challenge from low transduction efficiency in existing devices limited by weak photon interaction, we propose the use of squeezed-state optical sources to suppress the noise. We numerically evaluate the quantum advantage in synthetic aperture radar imaging, where the images are generated from the standard resolution test chart via a Gaussian point spread function with added Gaussian noise. We apply the Wiener filter on the images to restore the objects and find that stronger squeezing significantly improves the quality of the restored image. Our findings push quantum squeezing advantage to real-world applications.
Quantum imaging can be defined as an area of quantum optics that investigates the ultimate performance limits of optical imaging allowed by the quantum nature of light. Quantum Imaging techniques possess a high potential for improving the performance in recording, storage, and readout of optical images beyond the limits set by the standard quantum level of fluctuations known as the shot noise. This talk aims at giving an overview of the fundamentals of Quantum Imaging as well as its most important directions. We shall discuss generation of the spatially multimode squeezed states of light be means of a travelling-wave optical parametric amplifier. We shall demonstrate that this kind of light allows us to reduce the spatial fluctuations of the photocurrent in a properly chosen homodyne detection scheme with a highly efficient CCD camera. It will be shown that using the amplified quadrature of the light wave in a travelling-wave optical parametric amplifier, one can perform noiseless amplification of optical images. We shall provide recent experimental results demonstrating a single-shot noiseless image amplification by a pulsed optical parametric amplifier. One of important experimental achievements of Quantum Imaging, coined in the literature as quantum laser pointer, is a precise measurement of position and transverse displacement of a laser beam with resolution beyond the limit imposed by the shot noise. We shall describe briefly the idea of the experiment in which the transverse displacement of a laser beam was measured with resolution of the order of Angstrom. The problem of precise measurement of transverse displacement of a light beam brings us to a more general question about the quantum limits of optical resolution. The classical resolution criterion, derived in the nineteen century by Abbe and Rayleigh, states that the resolution of an optical instrument is limited by diffraction and is related to the wavelength of the light used in the imaging scheme. However, it was for a long time recognised in the literature that in some cases when one has some a priori information about the object, one can improve the resolution beyond the Rayleigh limit using the so-called super-resolution techniques. As a final topic in this talk, we shall discuss the quantum limits of optical super-resolution. In particular, we shall formulate the standard quantum limit of super-resolution and demonstrate how one can go beyond this limit using specially designed multimode squeezed light.
Real-time sensing of ultra-wideband radio-frequency signal with high frequency resolution is challenging, which is confined by the sampling rate of electronic analog-to-digital converter and the capability of digital signal processing. By combining quantum mechanics with compressed sensing, quantum compressed sensing is proposed for wideband radio-frequency signal frequency measurement. By using an electro-optical crystal as a sensor which modulates the wave function of the coherent photons with the signal to be measured. The frequency spectrum could be recovered by detecting the modulated sparse photons with a low time-jitter single-photon detector and a time-to-digital converter. More than 50 GHz real-time analysis bandwidth is demonstrated with the Fourier transform limit resolution. The further simulation shows it can be extended to more than 300 GHz with the present technologies.
We introduce a new generation of 3D imaging devices based on quantum plenoptic imaging. Position-momentum entanglement and photon number correlations are exploited to provide a scan-free 3D image after post-processing of the collected light intensity signal. We explore the steps toward designing and implementing quantum plenop- tic cameras with dramatically improved performances, unattainable in standard plenoptic cameras, such as diffraction-limited resolution, large depth of focus, and ultra-low noise. However, to make these new types of devices attractive to end-users, two main challenges need to be tackled: the reduction of the acquisition times, that for the commercially available high-resolution cameras would be from tens of seconds to a few minutes, and a speed-up in processing the large amount of data that are acquired, in order to retrieve 3D reconstructions or refocused 2D images. To address these challenges, we are employing high-resolution SPAD (single photon avalanche diode) arrays and high-performance low-level programming of ultra-fast electronics, combined with compressive sensing and quantum tomography algorithms, with the aim of reducing both the acquisition and the elaboration time by one or possibly two orders of magnitude. Moreover, in order to achieve the quantum limit and further increase the volumetric resolution beyond the Rayleigh diffraction limit, we explored dedicated pro- tocols based on quantum Fisher information. Finally, we discuss how this new generation of quantum plenoptic devices could be exploited in different fields of research, such as 3D microscopy and space imaging.