In recent decades, THz metamaterials have emerged as a promising technology for biosensing by extracting useful information (composition, structure and dynamics) of biological samples from the interaction between the THz wave and the biological samples. Advantages of biosensing with THz metamaterials include label-free and non-invasive detection with high sensitivity. In this review, we first summarize different THz sensing principles modulated by the metamaterial for bio-analyte detection. Then, we compare various resonance modes induced in the THz range for biosensing enhancement. In addition, non-conventional materials used in the THz metamaterial to improve the biosensing performance are evaluated. We categorize and review different types of bio-analyte detection using THz metamaterials. Finally, we discuss the future perspective of THz metamaterial in biosensing.

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.

The current outbreak of the coronavirus disease‐19 (COVID‐19) pandemic worldwide has caused millions of fatalities and imposed a severe impact on our daily lives. Thus, the global healthcare system urgently calls for rapid, affordable, and reliable detection toolkits. Although the gold‐standard nucleic acid amplification tests have been widely accepted and utilized, they are time‐consuming and labor‐intensive, which exceedingly hinder the mass detection in low‐income populations, especially in developing countries. Recently, due to the blooming development of photonics, various optical chips have been developed to detect single viruses with the advantages of fast, label‐free, affordable, and point of care deployment. Herein, optical approaches especially in three perspectives, e.g., flow‐free optical methods, optofluidics, and surface‐modification‐assisted approaches, are summarized. The future development of on‐chip optical‐detection methods in the wave of emerging new ideas in nanophotonics is also briefly discussed.

Imaging flow cytometry has been widely adopted in numerous applications such as optical sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The system, with the assistance of machine learning, shows unprecedented advantages in automated image analysis, thus enabling high‐throughput measurement, identification, and sorting of biological entities. Recently, with the burgeoning developments of machine learning algorithms, deep learning has taken over most of data analysis and promised tremendous performance in intelligent imaging flow cytometry. Herein, an overview of the basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied to assist intelligent imaging flow cytometry is provided. Perspectives of emerging machine learning algorithms in implementing future intelligent imaging flow cytometry are also discussed.