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Superconducting Quantum Circuits That Learn - Geordie Rose - H+ Summit @ Harvard


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Geordie Rose
D-Wave Systems Inc.
Special purpose superconducting quantum processors for disruptively accelerating machine learning

Any system that could be considered intelligent must be able to learn. Unfortunately teaching machines how to learn in a generalizable way – so-called minimally supervised or unsupervised learning – is an extremely hard problem. While much progress has been made in understanding how we might do this – for example using deep belief networks – all current proposals are extremely computationally intensive. Exercising them in real-world situations is often not possible because of the required computational cost – even for large corporations with access to enormous server farms. Here I present a path to overcoming this problem by running state of the art machine learning algorithms on a revolutionary new processor design, which uses quantum effects to enable a class of algorithms that cannot be run on any conventional processor.

Dr. Geordie Rose is the founder and CTO of D-Wave. He is known as a leading advocate for quantum computing and superconducting processors, and has been invited to speak on these topics in a wide range of venues, including TED, Future in Review and SC.

His innovative and ambitious approach to building quantum computing technology and support infrastructure has received coverage in MIT Technology Review magazine, The Economist, New Scientist, Scientific American and Science magazines, and one of his business strategies was profiled in a Harvard Business School case study.

Dr. Rose holds a Ph.D. in theoretical physics from the University of British Columbia, specializing in quantum effects in materials. While at McMaster University, he graduated first in his class with a B.Eng. in Engineering Physics, specializing in semiconductor engineering.

Published in: Technology
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Superconducting Quantum Circuits That Learn - Geordie Rose - H+ Summit @ Harvard

  1. 1. Building and using superconducting quantum circuits that learn A status report
  2. 2. Learning is the key to AGI Regardless of your definition of intelligence
  3. 3. Is quantum mechanics required for human+ level learning ability? If it is, that would be spectacularly interesting and surprising Let’s assume it’s not Image from
  4. 4. One possibility: Evolution always leads to classical brains Many compelling arguments in favor of this
  5. 5. But… quantum algorithms do exist for machine learning … machines running these algorithms might be superior to any possible evolved brain… Pattern matching Inference Deduction Scheduling Optimization …yes, even termite brains.
  6. 6. Moral of the story If brains use quantum mechanics, we should try to discover how & why If brains don’t use quantum mechanics, we can build machines that are spectacularly better than any possible evolved brain at a wide range of tasks
  7. 7. (Classical) deep neural nets See Itamar Arel’s presentation 5:00pm today! Unsupervised learning with rich sensory input Problem: learning is computationally hard
  8. 8. “Analog” deep neural nets … with quantum mechanical components Also called adiabatic quantum optimization Use uniquely quantum effects to speed learning
  9. 9. The Rainier processor interconnect … each node is a real physical qubit Layer of output neurons/qubits Two layers of hidden neurons/qubits Weighted connections Layer of input neurons/qubits
  10. 10. How a processor is born … superconducting electronics is fun! and expensive! 1 mm
  11. 11. These “quantum neural nets” have learned some things already Used to build best car detector ever built (work done with Google)
  12. 12. Messages to take away (Unsupervised) learning is the key to AGI Deep neural nets might be the way to go Quantum versions (neurons qubits) are being built; early versions exist already!