Quantum Bayesian Networks

December 17, 2017

I Left My Heart in Nanjing, at the Quantum AI Conference, AIQP-2017

Filed under: Uncategorized — rrtucci @ 8:31 pm

Nanjing University will be holding a Quantum AI conference AIQP-2017 on Dec 20-22, 2017.

Our company artiste-qb.net has its headquarters in Toronto-Canada and is currently a participant in the CDL (Creative Destruction Lab of the Univ. of Toronto) quantum AI incubator.

Dr. Tao Yin, currently living in ShenZhen-China, is the CTO of our company artiste-qb.net. Our man Tao will not be speaking at the conference, but he will be attending and meeting people. Also attending, and speaking!, will be 2 other persons associated with CDL, Dr. Jacob Biamonte and Dr. Peter Wittek. Not speaking, (Whew!) are any IBM, Google or Microsoft representatives.

Quote from conference website:

The workshop is sponsored by the Nanjing University AIQ fund. The AIQ fund is donated by the Founder and President of ECOVACS Robotics, Mr. Qian Dongqi, who is also an alumni of NJU (Bachelor in Physics 1981, and Master in Philosophy 1987).

The Ecovacs company sells excellent robotics vacuum cleaners all over the world.

I end with a “Make China Great Again” Tweet that I have been using recently to advertise our company:

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December 16, 2017

In Love With Jupyter Widgets

Filed under: Uncategorized — rrtucci @ 11:56 pm

Many old fashioned computer programs that allow a lot of pointing and clicking, what I like to call “GUI-rich” software (GUI=Graphical User Interface), provide you with only a limited “scripting and documenting” ability, i.e., ability to save and restore your work history (including the buttons you clicked and in what order, the plots you generated, your comments, etc.). Before the advent of Jupyter widgets, Jupyter notebooks got an A++ grade in scripting ability, but an F in GUI. Old fashioned, GUI-rich software got the opposite grades. So it might have been hard back then to decide which of these two paths, GUI-rich or Jupyter, to choose. But this is no longer a hard choice. With Jupyter widgets, Jupyter notebooks get an A++ in both categories.

In my opinion, scripting ability is very desirable when doing work related to statistics, Bayesian networks, Big Data, AI, numerical research, etc. A GUI is desirable too, especially if you want normal people, i.e. non-geeks, ever to use your software! So I am glad to announce that Quantum Fog now has some Jupyter notebooks with widgets.

In the following folder, you will find notebooks for doing inference with the (classical, not quantum) WetGrass and Asia networks. This is a link to GitHub, so of course the GUI is displayed but doesn’t work properly there. It only works properly if you are running the notebook in a Python environment.
http://nbviewer.jupyter.org/github/artiste-qb-net/quantum-fog/tree/master/jupyter-notebooks/cbnets_inference_gui/

December 5, 2017

The Toronto Quantum Meetup Supremos invite you to their next Meetup on Bill Gates and Bayesian networks

Filed under: Uncategorized — rrtucci @ 4:30 pm

The Toronto Quantum computing Meetup is the largest meetup in the world dedicated to quantum computing, so we claim the title of Quantum Meetup Supremacy, at least for now. (currently we have 873 Supremos as members. The second biggest club is in Austin with a paltry 630 Fajitas members)

We cordially invite you to our next meeting on Dec 15 . This meeting will start by addressing the question: “Was Bill Gates lying to the American people when he stated to the press that he doesn’t understand quantum computing?”. This question has cruelly split the American nation along party lines.

Consider the evidence:

  • Here are the beginning lines of an Oct 1996 article in the Los Angeles Times

    When Microsoft Senior Vice President Steve Ballmer first heard his company was planning to make a huge investment in an Internet service offering movie reviews and local entertainment information in major cities across the nation, he went to Chairman Bill Gates with his concerns.

    After all, Ballmer has billions of dollars of his own money in Microsoft stock, and entertainment isn’t exactly the company’s strong point.

    But Gates dismissed such reservations. Microsoft’s competitive advantage, he responded, was its expertise in “Bayesian networks.”

    Asked recently when computers would finally begin to understand human speech, Gates began discussing the critical role of “Bayesian” systems.

    Ask any other software executive about anything “Bayesian” and you’re liable to get a blank stare.

    Is Gates onto something? Is this alien-sounding technology Microsoft’s new secret weapon?

    Quite possibly.

  • But here are the beginning lines of a Nov 2017 article in MSN.com

    Bill Gates doesn’t completely understand quantum computing, the Microsoft Corp. co-founder and verified computer whiz told the Wall Street Journal Monday.

    “I know a lot of physics and a lot of math. But the one place where they put up slides and it is hieroglyphics, it’s quantum,” Gates said.

The host of the meeting, Henning Dekant, CEO of the company artiste-qb.net that I work for, will try to argue that Bill Gates is wrong. If only Gates realized that quantum computing languages can be written very intuitively as Bayesian Networks, the scales would fall from his eyes and he would recognize that the countenance of Quantum Computing is the same as that of his old love, Bayesian Networks.

Jesting aside, Henning will also speak about artiste-qb.net’s ongoing work to use quantum bayesian networks in quantum computing.

Mr. Gates, I never met Steve Jobs but I read his friking biography, and you, Mr. Gates, are no Steve Jobs, sir!

December 3, 2017

You are invited to the wedding of Quantum Computing and TensorFlow

Filed under: Uncategorized — rrtucci @ 6:42 pm

The quantum computerization of TensorFlow (TF) is a quixotic dream that no doubt has crossed the minds of many, both technically and not technically savvy, people. Here at artiste-qb.net, we are very committed and well underway to achieving this goal. Since our company is fully committed to open source, it doesn’t really matter if we achieve this goal before anyone else. If someone else beats us to it, we will learn from their code and vice versa. That is, as long as they too deliver open source to the world. And if they don’t, we think that their software is doomed…quantum open source rules! How did Google vanquish, or at least de-fang, the Microsoft monopoly? To a large extent, by using Open Source. Open source rules AI, the cloud and mobile.

So, let me tell you how we are using TF for quantum computing.

When Google first open sourced TF a mere 2 years ago, I wrote a blog post to mark the occasion. In that post, I called TF a platform for Fakesian networks instead of Bayesian networks. My beef was that TF had only deterministic nodes, which are standard in the field of artificial neural nets. It had no probabilistic nodes, which are standard in the 2 fields of classical Bayesian networks (BNets) and Hierarchical models (HM). But this past year, the open source community has fallen into the breach, filled in the gap, with a software library called Edward built on top of TF, that adds probabilistic nodes (the buzz word is “Probabilistic Deep Thinking”) to TF. And Edwards has been approved for integration into TF, so soon it will be seamless integrated into TF. Thus, soon, TF will combine artificial neural nets and BNets seamlessly. It will have superpowers!

Of course, in quantum mechanics, one must use complex amplitudes instead of probabilities for the nodes, and one must use an L2 norm instead of an L1 one with those amplitudes, so you can’t use Edward to do quantum mechanics just yet. Edward will have to be made “quantum ready”. By that we mean that one will have to rewrite parts of Edward so that it has a “quantum switch”, i.e. a parameter called ‘is_quantum’ which when True gives a quantum BNet and when False gives a classical BNet. That is precisely what artiste-qb.net’s open source program Quantum Fog already does, so our company is uniquely placed to make a quantum version of Edward.

Another obstacle to marrying TF and quantum computers is that the quantum BNets will have to be compiled into a sequence of elementary operations (SEO) such as control nots and single qubit rotations. Once again, our company artiste-qb.net is uniquely placed to accomplish this task. Our open source quantum simulator Qubiter is the only one in the business that includes a “quantum csd complier”, which is a tool that will help express quantum BNets as a SEO.

HMs are really a subset of BNets. However, the BNet and HM communities have historically grown somewhat independently. The BNet community is centered around pioneers like Judea Pearl (at UCLA), inventor of some of the most important BNet methods, whereas the HM community is centered around pioneers like Andrew Gelman (at Columbia), author of many great books and a great blog in the HM field. The HM tribe only uses continuous distributions for node probabilities, and they are very keen on MCMC (Markov Chain Monte Carlo). The BNet community uses both discrete (expressed as matrices or tensors) and continuous distributions for their node probabilities, and they use MCMC and other methods too, like the junction tree method, to do inferences.

Edward has a distinguished pedigree in both the BNet and HM communities. Edward originated in Columbia Univ. One of its main and original authors is Dustin Tran, currently a PhD student at Columbia. So you can be sure that the Edward people are in close communication and receive useful feedback from the Gelman tribe. Another distinguished author of Edward is Kevin Murphy, who has been working on BNets for more than a decade. Murphy wrote the oldie but goodie Bayes Net toolbox for Matlab. He has also written several books on bnets and machine learning. He previously worked as a prof at the Univ. of British Columbia but he now works at Google. He is one of the main organizers of the young (2 year old) Bayesian Deep Learning conference, which, by the way, will have its annual meeting in less than a week (Dec. 9, 2017).

Classical BNets are a very active field, both in academic research and in commerce. Judea Pearl won a Turing award for them. BNets are very popular in bioinformatics, for example. Whereas no qc company has yet broken-even financially, there are classical BNet companies that have lasted and been profitable for almost 2 decades, such as Bayesia, Hugin and Norsys/Netica.

Oh, and one last thing. It’s called TensorFlow, not TensorNetwork, for a very good reason. If you try to use TF to implement the “tensor networks” used in quantum computing, you will fail, unless you start using BNets instead of Tensor Networks and pretend these 2 are the same thing, which is probably what the Tensor Networks people will do. In TF (and BNets), the lines emanating out of a node carry in them a full tensor that they pass along to other nodes. In a Tensor Network, a Tensor does not Flow into the arrows emanating out of its node. The tensor just sits in the node. For more discussion about the important differences between a quantum BNet and a Tensor Network, see this blog post.
https://qbnets.wordpress.com/2015/03/26/tensor-networks-versus-quantum-bayesian-networks-and-the-winner-is/

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