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APPLICATION DEADLINE: February 13, 2024

Project & Job Description

Overview

Quantum computing and artificial intelligence (AI) have emerged as two of the leading candidates for the future of computing. AI is enabled by deep neural networks (DNNs) inspired by the human brain. DNNs are composed of finite number of discrete layers consisting of many artificial neurons through which data flows. Software implementation of DNNs on digital computers is very inefficient in speed and energy. We have demonstrated that the physics of photonic devices such as lasers and optical resonators mimic neurons and synapses in the brain and, when networked on-chip with silicon waveguides, results in scalable hardware-based neural networks with sub-nanosecond latencies (Shastri et al. Nature Photonics 15 2021). However, the accuracy of machine learning algorithms depends on size of the neural network (number of neurons and layers). Scalability of photonic neural networks remains a grand challenge as integrated photonic devices are large and difficult to control.

Recently, parameterized quantum circuits have been implemented as layers in software neural networks by industry leaders such as Google and Toronto’s Xanadu. Such devices share many of the properties of neural network layers but can leverage quantum superposition and entanglement to access exponentially increasing representational spaces with qubit number. This promises to break the trade-off between hardware scalability and accuracy of machine learning algorithms. The interspacing of classical and quantum networks makes today's noisy intermediate-scale quantum (NISQ) devices with a lack of adequate error correction adequate to physically-implement these models. The objective of this project will be to design a hardware interface between neuromorphic processors and quantum processors enabling hybrid quantum-classical neural network on a single silicon photonic chip. The physical hybridization of machine learning and quantum information stands to advance in both fields—quantum for machine learning, and machine learning for quantum.

Project & Role

A research assistant (RA) is required for a 16-week project to participate in a research program in nanophotonics, machine learning, and quantum information with a long-term objective to demonstrate programmable nanophotonic processors with applications to scientific computing, machine learning, and quantum information processing. Working under the guidance of Prof. Shastri, the candidate will work as part of research team comprised of postdoctoral researchers, graduate students, and collaborators.

The project will involve in part:

1. experimentally testing photonic neural networks that have been fabricated on a silicon photonics platform
2. simulating the photonic neural networks with simple quantum gates to demonstrate hybrid quantum-classical network and show a machine learning algorithm implemented on this network.

The goal of this project will be to design a hardware interface between photonic neural network and photonic quantum processors that allow a hybrid quantum-classical neural network to run directly on hardware. The qualitative output of this project will be a physical hardware interface design that connects the two processors that could be manufactured and tested. The quantitative output of this project will be simulation outputs using circuit or physical level software simulators of each processor to verify the functionality of the interface.

The overall progress of the candidate will be supervised by Prof. Shastri. The candidate will also be supervised by graduate students (who are experts in their individual field) and will provide an invaluable training experience opportunity for graduate students.

Responsibilities

1. Work independently and carry out original research
2. Work collaboratively as part of a team consisting of postdoctoral researchers, graduate students. This will discussions over lab meetings, lab socials, and working in the lab.
3. Set goals with Prof. Shastri and meet deadlines.

Required qualifications

1. Our research has a high bar and positions are competitive. We expect students to be strongly self-motivated with demonstrated ability to work collaboratively.
2. Should have completed at least two years of a physical science or engineering program—with preference in engineering physics, physics, electrical engineering or computing.
3. Have strong background in coding (python).
4. Desired (but not necessarily required) background in quantum mechanics, optics, photonics, or neural networks.

Skills demonstration and development

As a result of this SWEP position, the student will be able to demonstrate and develop these skills:

• Developing creativity and problem solving: The most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka!' but 'That's funny...' – Isaac Asimov. The student will have an opportunity to work at the forefront of the emerging field of Quantum Neuromorphic Photonics and apply their ideas to experiments and potentially say “that’s funny”
   
• Work collaboratively and develop professional skills: Dr. Shastri’s research is driven by highly collaborative and interdisciplinary efforts with a team of academics and industry partners. These opportunities will create a stimulating environment in which the student can enhance technical skills.
   
• Leadership skills: The student will lead the research and work independently (with guidance from Dr. Shastri). This will create a sense of ownership for the project.
   
• Develop project management skills: With the fast-paced, collaborative and multifaceted nature of the research in Dr. Shastri’s lab, the student will develop project management skills.
   
• Enhance communication (presentation and written) skills: Lab meetings, journal clubs, and workshops will serve as a training ground to develop oral communication skills. The student will also have the opportunity to be an author or a co-author on journal articles which will help enhance scientific writing skills.

Opportunities and/or activities

Opportunities and/or activities will be provided to the student to allow them to develop these skills:

• Unique experimental techniques: The student will receive training in nanophotonic including silicon photonic integration technology with cutting edge design, characterization, and experimental techniques. As one of the fastest developing areas, these skills are in high demand in the industry (IBM, Xanadu, Apple, HP Labs) and strategically positions the student for future opportunities.
• Work with multi-disciplinary team: The student will have the opportunity to work intimately with team of postdoctoral researchers and graduate students in physics, electrical engineering, computational neuroscience at Queen’s University, Princeton University, and University of British Columbia.
• Collaborations with industry partners: The student will have the opportunity to work closely with Dr. Shastri’s industry partners. This will enable the student to build professional networks and witness their research evaluated and contextualized.
• Placement: Some of Prof. Shastri’s students are Engineers at Apple; one is a Founder/CTO of Luminous computing (a start-up in Silicon Valley backed by Bill Gates and Uber CEO); other are pursuing PhDs at U. Waterloo, Princeton etc.
• Unique opportunities will the student be able to participate in:
• Work at the forefront of a newly emerging multidisciplinary field of photonics, machine learning, and quantum information.
• Carry out experimental research in a $4.5M facility at Queen’s for designing and testing microsystems and equipped with the necessary equipment (ultrafast lasers, microscopes), hardware (optomechanics, test and measurement) and software (Lumerical, Cadence).
• Contribute as an author or co-author on journal articles
• Work collaboratively with academics and industry partners for potential internships opportunities
• Prepare the student for graduate school.

Please submit

• CV
• Cover letter (explain why you would like to do summer research and why Shastri Lab, and describe your career goals)
• Transcript (unofficial is fine)