Quantum key distribution implemented with d-level time-bin entangled photons.

High-dimensional photon states (qudits) are pivotal to enhance the information capacity, noise robustness, and data rates of quantum communications. Time-bin entangled qudits are promising candidates for implementing high-dimensional quantum communications over optical fiber networks with processing rates approaching those of classical telecommunications. However, their use is hindered by phase instability, timing inaccuracy, and low scalability of interferometric schemes needed for time-bin processing.

Optics-Enabled Highly Scalable Inverter for Multi-Valued Logic.

The rapid advancements in machine learning have exacerbated the interconnect bottleneck inherent in binary logic-based computing architectures. An interesting approach to tackle this problem involves increasing the information density per interconnect, i.e.

Agile manipulation of the time-frequency distribution of high-speed electromagnetic waves.

Controlling the temporal evolution of an electromagnetic (EM) wave's frequency components, the so-called time-frequency (TF) distribution, in a versatile and real-time fashion remains very challenging, especially at the high speeds (> GHz regime) required in contemporary communication, imaging, and sensing applications. We propose a general framework for manipulating the TF properties of high-speed EM waves. Specifically, the TF distribution is continuously mapped along the time domain through phase-only processing, enabling its user-defined manipulation via widely-available temporal modulation techniques.

Fiber-optic spectrum monitoring of wavelength-division-multiplexed telecommunication signals with MHz update rates.

We propose a novel (to our knowledge) and simple real-time optical monitoring (RTOM) system for dynamic spectral analysis of telecommunication signals, involving electro-optic (EO) temporal sampling followed by dispersion-induced frequency-to-time mapping and high-speed photodetection. This system enables tracking of the presence and relative intensity of multiple wavelength-division-multiplexed (WDM) data streams that span over a broad frequency band with high resolution, accuracy, and fast measurement update rates. We derive the design conditions and trade-offs of the proposed scheme and report proof-of-concept experiments and a numerical result that demonstrate successful spectral monitoring of dense-WDM signals with different modulation formats and bit rates, over the full C-band, with the needed resolution to discern channels separated by a few tens of GHz, and with an unprecedented fast measurement update rate in the MHz range.

Compact optical convolution processing unit based on multimode interference.

Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing has been demonstrated to enable significant improvements in terms of processing speeds and energy efficiency. However, most present optical computing schemes are hardly scalable since the number of optical elements typically increases quadratically with the computational matrix size.