Quantum Bayesian Networks

July 1, 2018

Quantum Edward, Quantum Computing Software for Medical Diagnosis and GAN (Generative Adversarial Networks)

Filed under: Uncategorized — rrtucci @ 7:28 am

Quantum Edward at this point is just a small library of Python tools for doing classical supervised learning by Quantum Neural Networks (QNNs). The basic idea behind QEdward is pretty simple: In conventional ANN (Artificial Neural Nets), one has layers of activation functions. What if we replace each of those layers by a quantum gate or a sequence of quantum gates and call the whole thing a quantum computer circuit? The replacement quantum gates are selected in a very natural way based on the chain rule of probabilities. We take that idea and run with it.

As the initial author of Quantum Edward, I am often asked to justify its existence by giving some possible use cases. After all, I work for a startup company artiste-qb.net, so the effort spent on Quantum Edward will not be justified in the eyes of our investors if it is a pure academic exercise with no real-world uses. So let me propose two potential uses.

(1) Medical Diagnosis

It is interesting that the Bayesian Variational Inference method that Quantum Edward currently uses was first used in 1999 by Michael Jordan (Berkeley Univ. prof with same name as the famous basketball player) to do medical diagnosis using Bayesian Networks. So the use of B Nets for Medical Diagnosis has been in the plans of b net fans for at least 20 years.

https://arxiv.org/abs/1105.5462

More recently, my friends Johann Marquez (COO of Connexa) and Tao Yin (CTO of artiste-qb.net) have pointed out to me the following very exciting news article:

This AI Just Beat Human Doctors On A Clinical Exam (Forbes, June 28, 2018, by Parmy Olson)

It took 2 years to train the Babylon Health AI, but the investment has begun to pay off. Currently, their AI can diagnose a disease correctly 82% of the time (and that will improve as it continues to learn from each case it considers) while human doctors are correct only 72% of the time on average. Babylon provides an AI chatbot in combination with a remote force of 250 work-from-home human doctors.

Excerpts:

The startup’s charismatic founder, Ali Parsa, has called it a world first and a major step towards his ambitious goal of putting accessible healthcare in the hands of everyone on the planet.

Parsa’s most important customer till now has been Britain’s state-run NHS, which since last year has allowed 26,000 citizens in London to switch from its physical GP clinics to Babylon’s service instead. Another 20,000 are on a waiting list to join.

Parsa isn’t shy about his transatlantic ambitions: “I think the U.S. will be our biggest market shortly,” he adds.

Will quantum computers (using quantum AI like Quantum Edward) ever be able to do medical diagnosis more effectively than classical computers? It’s an open question, but I have high hopes that they will.

(2) Generative Adversarial Networks (GAN)

GANs (Wikipedia link) have been much in the news ever since they were invented just 4 years ago, for their ability to make amazingly accurate predictions with very little human aid. For instance, they can generate pictures of human faces that humans have a hard time distinguishing from the real thing, and generate 360 degree views of rooms from only a few single, fixed perspective photos of the room.

Dusting Tran’s Edward (on which Quantum Edward is based) implements inference algorithms of two types, Variational and Monte Carlo. With Edward, one can build classical neural networks that do classification via the so called Black Box Variational Inference (BBVI) algorithm. Can BBVI also be used to do GAN classically? Yes! Check out the following 4 month old paper:

Graphical Generative Adversarial Networks, by Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang https://arxiv.org/abs/1804.03429 (see footnote)

Can this be generalized to quantum mechanics, i.e. can one use BBVI to do classification and GAN on a quantum computer? Probably yes. Quantum Edward already does classification. It should be possible to extend the techniques already in use in Quantum Edward so as to do GAN too. After all, GAN is just 2 neural nets, either classical or quantum, competing against each other.

(footnote) It is interesting to note that 3 out the four authors of this exciting GAN paper work at Tsinghua Univ in Beijing. Their leader is Prof. Jun Zhu (PhD from Tsinghua Univ, post-doc for 4 yrs at Carnegie Mellon), a rising star in the AI and Bayesian Networks community. He is the main architect of the software ZhuSuan. ZhuSuan is available at GitHub under the MIT license. It is a nice alternative to Dustin Tran’s Edward. Like Edward, it implements Bayesian Networks and Hierarchical Models on top of TensorFlow. The above GAN paper and the ZhuSuan software illustrate how advanced China is in AI.

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1 Comment »

  1. This paper was published in ArXiv on July 3. It gives an example of Quantum GAN for a small number (4) of qubits

    https://arxiv.org/abs/1807.01235

    Comment by rrtucci — July 4, 2018 @ 3:13 am


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