Experimental Quantum Homomorphic Encryption Using a Quantum Photonic Chip.

A fully homomorphic encryption system enables computation on encrypted data without the necessity for prior decryption. This facilitates the seamless establishment of a secure quantum channel, bridging the server and client components, and thereby providing the client with secure access to the server's substantial computational capacity for executing quantum operations. However, traditional homomorphic encryption systems lack scalability, programmability, and stability. In this Letter, we experimentally demonstrate a proof-of-concept implementation of a homomorphic encryption scheme on a compact quantum chip, verifying the feasibility of using photonic chips for quantum homomorphic encryption. Our work not only provides a solution for circuit expansion, addressing the longstanding challenge of scalability while significantly reducing the size of quantum network infrastructure, but also lays the groundwork for the development of highly sophisticated quantum fully homomorphic encryption systems.

mmWave Zero Order Resonant Antenna with Patch-Like Radiation Fed by a Butler Matrix for Passive Beamforming.

A small zero-order resonant antenna based on the composite right-left-handed (CRLH) principle is designed and fabricated without metallic vias at 30 GHz to have patch-like radiation. The mirror images of two CRLH structures are connected to design the antenna without via holes. The equivalent circuit, parameter extraction, and dispersion diagram are studied to analyze the characteristics of the CRLH antenna. The antenna was fabricated and experimentally verified. The measured realized gain of the antenna is 5.35 dBi at 30 GHz. The designed antenna is free of spurious resonance over a band width of 10 GHz. A passive beamforming array is designed using the proposed CRLH antenna and the Butler matrix. A substrate integrated waveguide is used to implement the Butler matrix. The CRLH antennas are connected to four outputs of a 4×4 Butler matrix. The scanning angles are 12∘, -68∘, 64∘, and -11∘ for excitations from port 1 to port 4 of the 4×4 Butler matrix feeding the CRLH antenna.

mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning.

A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R2 value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%.

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor.

In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3-10 GHz. The radar captures the reflected signals that are post processed to determine the soil moisture which is mapped to the input features extracted from the reflected signals for the training of the machine learning models. In addition, the results are compared and analyzed with a contact-based Vernier soil sensor. Different machine learning models trained using neural network, support vector machine, linear regression and k-nearest neighbor are evaluated and presented in this work. The efficiency of the model is computed using root mean square error, co-efficient of determination and mean absolute error. The RMSE and MAE values of KNN, SVM and Linear Regression are 11.51 and 9.27, 15.20 and 12.74, 3.94 and 3.54, respectively. It is observed that the neural network gives the best results with an R2 value of 0.9894. This research work has been carried out with an intention to develop cost-effective solutions for common users such as agriculturists to monitor the soil moisture conditions with improved accuracy.

Large-scale photonic network with squeezed vacuum states for molecular vibronic spectroscopy.

Although molecular vibronic spectra generation is pivotal for chemical analysis, tackling such exponentially complex tasks on classical computers remains inefficient. Quantum simulation, though theoretically promising, faces technological challenges in experimentally extracting vibronic spectra for molecules with multiple modes. Here, we propose a nontrivial algorithm to generate the vibronic spectra using states with zero displacements (squeezed vacuum states) coupled to a linear optical network, offering ease of experimental implementation. We also fabricate an integrated quantum photonic microprocessor chip as a versatile simulation platform containing 16 modes of single-mode squeezed vacuum states and a fully programmable interferometer network. Molecular vibronic spectra of formic acid and thymine under the Condon approximation are simulated using the quantum microprocessor chip with high reconstructed fidelity ( > 92%). Furthermore, vibronic spectra of naphthalene, phenanthrene, and benzene under the non-Condon approximation are also experimentally simulated. Such demonstrations could pave the way for solving complicated quantum chemistry problems involving vibronic spectra and computational tasks beyond the reach of classical computers.

Recent progress in quantum photonic chips for quantum communication and internet.

Recent years have witnessed significant progress in quantum communication and quantum internet with the emerging quantum photonic chips, whose characteristics of scalability, stability, and low cost, flourish and open up new possibilities in miniaturized footprints. Here, we provide an overview of the advances in quantum photonic chips for quantum communication, beginning with a summary of the prevalent photonic integrated fabrication platforms and key components for integrated quantum communication systems. We then discuss a range of quantum communication applications, such as quantum key distribution and quantum teleportation. Finally, the review culminates with a perspective on challenges towards high-performance chip-based quantum communication, as well as a glimpse into future opportunities for integrated quantum networks.

Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder.

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking.

Pangolin-Inspired Stretchable, Microwave-Invisible Metascale.

Microwave-invisible devices are emerging as a valuable technology in various applications, including soft robotics, shape-morphing structures, and textural camouflages, especially in electronic countermeasures. Unfortunately, conventional microwave-absorbing metastructures and bulk absorbers are stretching confined, limiting their application in deformable or special-shaped targets. To overcome such limitations, a conceptually novel soft-rigid-connection strategy, inspired by the pangolin, is proposed. Pangolin-inspired metascale (PIMS), which is a kind of stretchable metamaterial consisting of an electromagnetic dissipative scale (EMD-scale) and elastomer, is rationally designed. Such a device exhibits robust microwave-absorbing capacity under the interference of 50% stretching. Besides, profiting from the covering effect and size-confined effect of EMD-scale, the out-of-plane indentation failure force of PIMS is at least 5 times larger than conventional device. As a proof of concept, the proposed device is conformally pasted on nondevelopable surfaces. For a spherical dome surface, the maximum radar cross-section (RCS) reduction of PIMS is 6.3 dB larger than that of a conventional device, while for a saddle surface, the bandwidth of 10 dB RCS reduction exhibits an increase of 83%. In short, this work provides a conceptually novel platform to develop stretchable, nondevelopable surface conformable functional devices.

Parametric Excitation of Optomechanical Resonators by Periodical Modulation.

Optical excitation of mechanical resonators has long been a research interest, since it has great applications in the physical and engineering field. Previous optomechanical methods rely on the wavelength-dependent, optical anti-damping effects, with the working range limited to the blue-detuning range. In this study, we experimentally demonstrated the excitation of optomechanical resonators by periodical modulation. The wavelength working range was extended from the blue-detuning to red-detuning range. This demonstration will provide a new way to excite mechanical resonators and benefit practical applications, such as optical mass sensors and gyroscopes with an extended working range.

0.2 λ0 Thick Adaptive Retroreflector Made of Spin-Locked Metasurface.

The metasurface concept is employed to planarize retroflectors by stacking two metasurfaces with separation that is two orders larger than the wavelength. Here, a retroreflective metasurface using subwavelength-thick reconfigurable C-shaped resonators (RCRs) is reported, which reduces the overall thickness from the previous record of 590 λ0 down to only 0.2 λ0 . The geometry of RCRs could be in situ controlled to realize equal amplitude and phase modulation onto transverse magnetic (TM)-polarized and transverse electric (TE)-polarized incidences. With the phase gradient being engineered, an in-plane momentum could be imparted to the incident wave, guaranteeing the spin state of the retro-reflected wave identical to that of the incident light. Such spin-locked metasurface is natively adaptive toward different incident angles to realize retroreflection by mechanically altering the geometry of RCRs. As a proof of concept, an ultrathin retroreflective metasurface is validated at 15 GHz, under various illumination angles at 10°, 12°, 15°, and 20°. Such adaptive spin-locked metasurface could find promising applications in spin-based optical devices, communication systems, remote sensing, RCS enhancement, and so on.