CAREER: Photonic Quantum Machine Learning: From Architecture to Applications

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The formulation of quantum mechanics in the 20th century shifted the landscape of science, giving birth to a plethora of revolutionary technologies that empower the information age. Humankind is now on the verge of a second quantum revolution fueled by quantum information science (QIS), which is envisioned to enable disruptive communication, sensing, and computing applications. Despite the tremendous prospects promised by QIS, building large-scale and robust quantum information processing systems remains an outstanding challenge due to the fragility of quantum information in the present noisy intermediate-scale quantum (NISQ) hardware. To unlock the power of NISQ devices and systems, hybrid quantum-classical protocols have become a focus of recent QIS studies, in which state-of-the-art classical data science tools are leveraged to steer NISQ hardware towards solving specific data-processing problems. This CAREER project will develop a new photonic quantum machine-learning architecture that combines mature, classical machine-learning tools and NISQ platforms to endow unprecedented communication, sensing, and data processing capabilities. Compared with other NISQ platforms, quantum photonics feature room-temperature operations, mass productivity, and compatibility with the existing telecommunication and sensing infrastructures. The project will advance basic knowledge for the NISQ era and the interdisciplinary areas of QIS, machine learning, and NSF’s 10 Big Ideas Harnessing the Data Revolution and the Quantum Leap. A critical ingredient for a sustainable QIS ecosystem is to develop the next-generation quantum workforce. To this end, this CAREER project will encompass activities for: 1) QIS teaching laboratories for undergraduate students; 2) a QIS training program for industry workforce development; and 3) outreach to engage K-12 STEM students early in QIS.The research activities of this CAREER project will encompass both: 1) a photonic quantum machine-learning architecture based on a classical machine-learning framework and photonic quantum information-processing hardware, including reconfigurable entanglement sources and adaptive quantum receivers; and 2) photonic quantum machine-learning applications for long-haul optical communications, multi-domain sensing, and quantum-enhanced data processing. The new photonic quantum machine-learning architecture will effectively use cutting-edge classical machine-learning tools to configure variational photonic quantum circuits, as a powerful means to generate, process, and measure quantum information. Although photons interact only weakly with each other to hinder the use of large-scale photonic entanglement, the proposed photonic quantum machine-learning architecture will overcome this barrier by leveraging quantum photonics that offer deterministic generation, the processing of large-scale entanglement, and suitability for sensing and communication applications. By combining enhancements from machine learning and quantum coherence, the expected project outcomes will enhance various sensing- and communication-related tasks, including pattern recognition, deep-space signal detection, and efficient data compression. Ultimately, broadly sharing the new knowledge grown out of these research activities should spark collaborations between academia, National Laboratories, and the U.S. healthcare, aerospace, environmental protection, and chemical engineering industries. By working with industrial partners, the CAREER project will connect with new quantum technologies that transform U.S. industries. The project research facilities and workforce development activities will help to prepare the U.S. technology industry workforce for the future quantum edge and establish comfortable and personally relevant quantum foundations for students across university to high-school settings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.