Machine Vision and Magnification at the Limits of Quantum Mechanics

Next generation of machine vision sensors that deliver ultra-sharp imaging, high inference speed, and strong energy efficiency.

Ultra-High Magnification Using Quantum Sensors

Imaging sensors used by cameras in cellphones, autonomous vehicles, and robotics are based on CMOS or CCD technology to capture images.

A key challenge of these sensors is low magnification and capturing speed, limited by a physical boundary, the diffraction limit. Furthermore, digital sensors lose information due to shot noise when digitally capturing the image.

Using breakthroughs in quantum technology developed for NASA to discover life on other planets, we are creating minituarizable quantum sensors that significantly increase magnification and capturing speed.

Light-Speed Imaging AI

Object detection, image classification, and facial recognition at light-speed.

Our sensors provide incredibly fast vision AI by combining them with Optical Neural Networks. Optical Neural Networks are physical optical devices that can fully replicate a digital neural network for AI vision. Optical Neural Networks can provide classifications with an incredible increase in processing speed.

Our quantum sensors are fully integrated with Optical Neural Networks, allowing AI Vision to be executed with light at the Edge (at the device). This allows an incredible increase in speed for critical time-sensitive tasks. Like breaking a vehicle to save a life.

Modern AI systems are inefficient and are consuming an escalating amount of energy, contributing to a growing share of the tech sector's carbon footprint, which accounts for 2-3% of global emissions.

Our sensors have an ultra-low energy consumption many orders of magnitude less than a classical CMOS sensor and GPU combination. This makes our sensors perfect for autonomous vehicles and low-energy environments.

Since imaging is a highly critical and significant part of AI tasks, our sensors and chips can make a significant impact to reduction of carbon emissions.

Ultra-Low Energy Consumption

Our Tech  

Our technology was developed in partnership with DARPA and NASA for high-precision space situational awareness. It is intended to fit into the Habitable Worlds Observatory, the successor to the James Webb and Hubble space observatories, dedicated to finding exoplanets that can support life.

We use modal imaging at the quantum limit of resolution, for passive imaging with incoherent light. We attain the fundamental quantum limits in the regimes when conventional imaging techniques fail, using pre-detection spatial mode sorting, including adaptive techniques.

Who We Are

We are an early stage deeptech startup with roots in MIT, Harvard, University of Maryland, and University of Arizona.

Our Headquarters are in Boston, with lab space in College Park, Maryland.

  • Johannes Galatsanos

    CO-FOUNDER, CEO

    Johannes has spent 15 years in the Data, AI, Quantum Tech, and Intrapreneurship space. Prior to Diffraqtion, he was an Executive Director at Novartis, heading the largest Data & AI transformation in the industry. He was also a researcher at MIT and the Center for Quantum Networks, a lecturer in AI, and a managing consultant at a Big Four.

    He’s an MIT Sloan Fellows MBA, Oxford, and Uni Frankfurt graduate with a focus on AI, Quantum Technologies and entrepreneurship for deeptech commercialization.

  • Co-Founder

    Dr. Christine Yi-Ting Wang

    CO-FOUNDER, CTO

    Christine has more than 20 years of R&D experience in the field of optics and photonics, especially quantum electronics, imaging systems and photonic integrated circuits. Prior to Diffraqtion, she was the Director of Optics and Photonics at Riverside Research, and prior to that a Principal Scientist at The Charles Stark Draper Laboratory. 

    She received her Ph.D. in Physics from Harvard University, and was a Marie Sklodowska-Curie Postdoctoral Fellow at Max-Planck Institute of Quantum Optics and EPFL in Lausanne.

  • Prof. Saikat Guha

    CO-FOUNDER, CHIEF SCIENTIFIC ADVISOR

    Saikat is the Clark Distinguished Chair Professor of Electrical and Computer Engineering at the University of Maryland and Director of the NSF Center for Quantum Networks. Saikat has over 10,000 citations and more than a hundred published papers and patents, with a focus on Quantum Sensing and Optical Neural Networks.

    He received his Ph.D. in Electrical Engineering and Computer Science from MIT, where he is also an adjunct faculty member.