Quantum Bayesian Networks

June 27, 2020

My Pinned Tweet at Twitter

Filed under: Uncategorized — rrtucci @ 9:28 pm

This is the pinned Tweet on my company’s (www.ar-tiste.xyz) Twitter account

September 16, 2020

Time travel & the importance of being Causal AI Earnest

Filed under: Uncategorized — rrtucci @ 3:41 pm

September 10, 2020

Amazon Braket, ka-ching, ka-ching

Filed under: Uncategorized — rrtucci @ 8:55 pm

About a month ago, Amazon announced the opening of their much anticipated quantum cloud service called Braket. It’s quite funny. Amazon has hired a bunch of greedy, selfish, narcissistic, dishonest, amoral quantum physicists from Caltech, who know nothing about programming or business, to make their quantum service profitable. Good luck with them, Jeff Bezos!

In a previous blog post about 9 months ago, I listed 17 quantum clouds (including the new ones by Google and Microsoft, and the longstanding one by IBM). By now there are probably a few more. So the quantum cloud field is super-saturated already.

Add to that the fact that proxy-quantum clouds like Amazon’s and Microsoft’s introduce a middle-man in the exchange of information. Hence, they are certain to slow things down at the user end compared with non-proxy, native quantum clouds like IBM’s and Google’s.

If I wanted to use a proxy quantum cloud, I would install my own private one. It would be cheaper, more flexible and private. There is already available, excellent, well maintained, free, open-source software produced by Berkeley Univ. that allows a computer ignoramus to install, in minutes, a private, Kubernetes driven, highly scalable, proxy quantum cloud.

Oh, and one more thing… user fees.

At Amazon-Braket, they do give you quantum simulator usage with <25 qubits for free, but you can do that on your own computer. Furthermore, AWS cloud fees seem to be charged separately from AWS-Braket fees. AWS cloud fees begin to be charged after your free tier year is over, and these can be quite substantial compared to using your own computer for free.

At Amazon-Braket, none of the usage of the qc hardware is for free.

  • 30 cents per task plus a per shot fee. ka-ching.
  • IonQ feels it costs 50 times more than D-Wave per shot. IonQ wants to charge 1 penny per shot. ka-ching, ka-ching  Good luck, guys! IBM salesman Dario Gil claims (*) that in the Quantum Challenge that IBM had in May, they had 10^9 shots per day. IonQ/AWS would have charged users $10^7/day for that affair.

AWS-Braket is going to be an interesting quantum physics experiment. It will answer the age-old physics question: How much qc hobbyists that are not being funded by a company are willing to pay for their vice? I suspect the answer is: not much. As to a large number of companies paying for qc services, I doubt that will happen either, because qc technology won’t yield a quantum over classical advantage for many years. What I believe is going to happen is that a lot of people will join out of curiosity, but will leave after they spend their first $10. I just don’t think that Amazon-Braket has any stickiness. We’ll see if I’m right.

Brought to you by http://www.ar-tiste.xyz

(*)Quote from Dario Gil article in Scientific American: “In early May, during IBM’s Digital Think conference, nearly 2,000 people from 45 countries took part in our Quantum Challenge—and using 18 IBM Quantum systems through the IBM Cloud, ran more than a billion circuits a day on real quantum hardware.”

September 8, 2020

The plagiarist J. Ignacio Cirac (El plagiario J. Ignacio Cirac)

Filed under: Uncategorized — rrtucci @ 9:51 pm

The following paper has been called to my attention today. Somehow, I had totally missed it:

From probabilistic graphical models to generalized tensor networks for supervised learning, by Ivan Glasser, Nicola Pancotti, J. Ignacio Cirac

J. Ignacio Cirac (the senior author and a very famous man in quantum computing) and his unethical associates  first used Tensor Networks in 2005. I first used Bayesian Networks in my 1995 paper entitled “Quantum Bayesian Nets” and I’ve written 50 papers in arXiv, this 12 year old blog called “Quantum Bayesian Networks”, several patents, and a ton of open source software about quantum Bayesian Networks. And yet they write a paper in 2019 about quantum Bayesian networks and never mention my work. Do they claim they missed 20 years of my work, and 40 years of work by Judea Pearl, who won the Turing Award, widely considered the Nobel Prize of CS, for his work on Bayesian Networks (not tensor networks)? It would be very funny if they did. The truth is that they are unethical plagiarists; there is no other possible explanation. 

P.S.: Note. I am not claiming that Cirac and co-thiefs copied part of my work verbatim. What I am claiming is that: it is clear, beyond a doubt, that sleazy Cirac and co-workers intentionally failed to mention highly relevant, widely available, and copious prior art. This is considered unethical and illegal in Science and in the patent office too.

The shaking table caused the vase to break!

Filed under: Uncategorized — rrtucci @ 12:54 am

I love this picture. It makes the importance of Causal AI clear to me. The importance of being Causal AI Earnest 🙂 And it touches on so many topics: Bayesian networks, Pearl d-separation, Pearl causality, and squashed quantum entanglement. It comes from the wonderful book:

Learning Bayesian Networks, by Richard E. Neapolitan

September 6, 2020

Neven’s talk at 2020 Summer Symposium

Filed under: Uncategorized — rrtucci @ 9:59 pm

Check out this talk that Hartmut Neven gave 2 days ago


So, Neven’s future plans for Google Quantum AI are 2 fold-

  1. New Google quantum cloud
  2. 10 year plan to build an error corrected machine.

It would be interesting to hear Martinis’ opinion about 2. I suspect that Neven’s plan for 2 is not very feasible, and that Martinis’ plan for 2 was quite different, and that is a large part of why he left. Martinis said as much as he was leaving Google. He said that his plans for wiring an error corrected qc were very different from those of Neven and Neven’s trusted advisors.

So whose opinion do you believe, Neven’s or Martinis’s?

This picture, taken from the above video, is Neven’s plan for an error corrected qc. Does it look like it would work to you? It doesn’t to me. To me, it looks like a picture taken from a superheroes comic book, of a very large turbine, or rather, half of it, a turbine which is definitely not at micro-kelvin temperatures. Miniaturization and refrigeration do not seem to be part of Neven’s folly.

Martinis has 40 years of experience building superconductive devices, and he has an impeccable track record of delivering what he promises. As far as hands-on experience building superconductive devices, Neven has zero. As far as intuition for what physical theories will and will not ultimately work, Neven has almost zero: He has a firm belief in the nonsense, pseudo-scientific Everett Multi-World interpretation of quantum mechanics and that qc’s work in many world’s at the same time, a firm early belief in the unrealistic DWave and Geordie Rose, who promised us Rose’s Law (qc’s “faster than the universe” by 2015), a firm belief in the utterly unrealistic, surreal, Neven double exponential law, … Some will say, but Bob, Neven has a staff of 100 bright people working for him. Well, yes, but I think Neven runs that outfit like a king, surrounded by 100 handpicked yes-men. He has the final word about everything. He ultimately rebelled against sharing power with Martinis, thus proving, to my mind, that he doesn’t share power gladly.

Good News, Xanadu AI jumps the shark

Filed under: Uncategorized — rrtucci @ 4:30 pm

Check out Xanadu AI’s latest press release:

Xanadu Releases World’s First Photonic Quantum Computer in the Cloud

Some very bold claims are made by Xanadu in that article.  Here are some excerpts:

Photonics based quantum computers have many advantages over older platforms. Xanadu’s quantum processors operate at room temperature. They can easily integrate into existing fiber optic-based telecommunication infrastructure, enabling a future where quantum computers are networked.

“We believe that photonics offers the most viable approach towards universal fault-tolerant quantum computing with Xanadu’s ability to network a large number of quantum processors together. “

“We believe we can roughly double the number of qubits in our cloud systems every six months,”

“In addition to the computing market, the company is also targeting secure communication and quantum networking, an area that photonics is poised to dominate. “We are laying the groundwork for our vision of the future: a global array of photonic quantum computers, networked over a quantum internet.”

Just because Xanadu’s pseudo-qc is photonic does not mean that it can be networked any better than  a non-photonic qc, with other computers, over the existing networks. So that is a misrepresentation. Besides, the quantum internet is a boondoggle that is highly unlikely to ever be built.

Up to now, Xanadu has provided little or no experimental evidence to support any of the claims of that article. Here is what Scott Aaronson had to say about Xanadu 9 months ago, and note that no one from Xanadu defended the company in the comments; they totally ignored Scott’s criticism.


I think that this time, Elizabeth Holmes, or whatever is the name of Xanadu’s CEO, has jumped the shark. This is good news for all truth loving people, because it makes it easier to expose Xanadu’s false promises now that those promises are clearly stated and now that the performance of their pseudo-qc can be analyzed by impartial observers. Let’s see how the quality of their qubits and error correction compares with that of other qc’s such as IBM’s. Let’s see if they can really double the number of **high quality** qubits every 6 months (low quality qubits are practically useless. DWave has 5,600 low quality qubits already and yet it is probably on the verge of bankruptcy after 20 profitless years.)

I can’t wait to see what the people at IBM, Google, Microsoft, Intel,  Rigetti, DWave, IonQ, Honeywell & PsiQuantum have to say about this. If they don’t say anything, it’s as if they were conceding that they’ve lost the qc race, and investors will flock en masse to Xanadu. Is funding of qc, a zero-sum game? I suspect it is.

Recently, I read another press release where the CEO of Xanadu was quoted as saying that he and his Olmers accomplices are seeking $100 million for Xanadu’s next funding round, on top of about $40 million received on previous rounds!! Investors beware of this Canadian Ponzi scheme! Xanadu used to refer on press releases to ex MIT professor Seth Lloyd as their “main scientific advisor”. Now that Seth Lloyd has been put on leave by MIT for accepting almost $300,000 from pedophile and owner of a child prostitution ring, Jeffrey Epstein, you would think that investors would have some misgivings about Xanadu’s credibility and ability to judge character. They say that birds of a feather, like Xanadu and Lloyd, hang together.

Xanadu AI CEO, Elizabeth Holmes (aka Christian Weedbrook) and Xanadu’s main scientific advisor and pedophile enabler, Seth Lloyd.

September 1, 2020

Belief Propagation (Message Passing) for Classical and Quantum Bayesian Networks

Filed under: Uncategorized — rrtucci @ 7:22 pm

My FREE book about Bayesian Networks, Bayesuvius, continues to grow. It currently has 33 chapters. The purpose of this blog post is to announce the release of a new Bayesuvius chapter on Belief Propagation (BP).

Belief Propagation (BP) (aka Message Passing) was first proposed in 1982 by Judea Pearl to simplify the exact evaluation of probability marginals of Bayesian Networks (bnets). It gives exact results for trees and polytrees (i.e. for bnets with a single connected component and no acyclic loops). For bnets with loops, it gives approximate results (loopy belief propagation), and it has been generalized to the junction tree (JT) algorithm which gives exact results for general bnets with loops.

The JT algo starts by clustering the loops of a bnet into bigger nodes so as to transform the bnet into a tree bnet. Then it applies BP to the ensuing tree. The first breakthrough paper to achieve this agenda in full was by Lauritzen, and Spiegelhalter (LS) in 1988. When it first came out, the LS algorithm caused quite a stir, and led to the creation of many bnet companies, many of which continue to exist and flourish today.

So why is BP important?

BP yields a huge reduction in the number of operations (additions and subtractions) necessary to calculate the marginals P(x_i)=\sum_{x_j: j\neq i}P(x_1, x_2, \ldots, x_N) of the probability distribution P(x_1, x_2, \ldots, x_N) associated with a classical bnet with N nodes. BP also works for quantum bnets. In the quantum case, quantum bnets have a complex probability amplitude A(x_1, x_2, \ldots, x_N) associated with them, and one seeks to calculate coherent sums A(x_i)=\sum_{x_j: j\neq i}A(x_1, x_2, \ldots, x_N).

My open source program Quantum Fog has implemented BP for both classical and quantum bnets since its first release at github in Dec. 2015. Quantum Fog implements the junction tree algorithm (which uses BP) for both probabilities and probability amplitudes.

It is also possible, but so far Quantum Fog doesn’t do it, to implement BP directly from the message passing equations invented by Judea Pearl, and then to use that approach to do loopy belief propagation for both classical and quantum bnets. This should be feasible because the message passing recursive equations of BP do not care if the messages being passed are complex valued (quantum bnet case) or real valued (classical bnet case). They only care about the graph structure of the bnet.

I won’t encumber the reader of this blog post with an exact statement of those recursive equations. For that level of technicality, I refer the reader to Bayesuvius’s chapter on BP. What I will do is to show the graphic that I give in that chapter to motivate those equations. Here it is, with its caption, which reads like a short story:

The yellow node is a gossip monger. It receives messages from all the green nodes, and then it relays a joint message to the red node. Union of green nodes and the red node = full neighborhood of yellow node. There are two possible cases: the red node is either a parent or a child of the yellow one. As usual, we use arrows with dashed (resp., dotted) shafts for downstream (resp., upstream) messages.

Examples of Causal Thinking, from Judea Pearl’s “The Book of Why”

Filed under: Uncategorized — rrtucci @ 2:37 am

BayesiaLab is putting out an excellent series of blog posts featuring examples of Causal AI taken directly from Judea Pearl’s “The Book of Why”. Check it out! Extraordinarily beautiful stuff. 4 examples so far (Breast Cancer, Firing Squad, Smallpox Vaccine, Tea House).

Brought to you by http://www.ar-tiste.xyz, a COOP of purveyors of high quality and low cost Bayesian Network Software & Services

August 30, 2020


Filed under: Uncategorized — rrtucci @ 2:44 pm

The unfortunate 900 Indian academicians are going to be killed by exposing them to the toxic language Qsharp, which is so foul that nobody uses it unless MS pays them to do so.

August 25, 2020

Causal AI, the other AI meat, the impossible AI burger

Filed under: Uncategorized — rrtucci @ 1:46 am



August 24, 2020

On Cloud 9

Filed under: Uncategorized — rrtucci @ 3:24 pm

Today, I am on cloud 9. Do you know the origins of the idiom “on cloud nine”? According to this reference

The origin of sense 1 (“a state of bliss”) is uncertain; however, the following etymology has been suggested:

The first edition of the International Cloud Atlas (1896),[1] which defined ten types of cloud, described the ninth type as the cumulonimbus which rises to 10 km (6.2 miles), the highest a cloud can be.

I’m on cloud 9 because I received an acknowledgement on Twitter from one of my heroes

August 15, 2020

Decision Trees & Bayesian Networks

Filed under: Uncategorized — rrtucci @ 9:51 pm


Decision Trees (DTs) are certainly cool and it is not my intention to belittle them here. Compared to Bayesian Networks (bnets), they seem easier to construct. In fact, I just wrote a poem dedicated to Decision Trees. It starts “I think that I shall never see an AI as lovely as a decision tree”. DTs do have some drawbacks, but, like I said, the purpose of this blog post is not to criticize them.

The real purpose of this post is to point out that there is a secret romance going on between Decision Trees and Bayesian Networks. Say you have a simple binary DT with YES and NO branches. Then you can construct an equivalent bnet with exactly the same tree graph. You turn the branches into arrows pointing down from the apex root node. Each fork in the tree becomes a node of the bnet. However, the nodes will have to have three states instead of two: NO, YES and NULL. This third state called NULL is a small overhead cost, a small price to pay. In return, you get to keep the tree structure in the equivalent bnet.

I explain all this more precisely in my FREE book Bayesuvius, where I describe this technique in 2 new chapters, entitled:

  1. Decision Trees
  2. Binary Decision Diagrams

Brought to you by http://www.ar-tiste.xyz, purveyors of high quality and low cost Bayesian Network software & services. A COOP.

How complicated projects like building a skyscraper or a rocket or a bridge manage so many details?

Filed under: Uncategorized — rrtucci @ 6:33 pm

It’s a question I often wondered about as a child, as I marveled at NYC skyscrapers during my first visit there, or when I visited Cape Canaveral or the Golden Gate Bridge. I don’t claim I will provide a full answer to this question in this short post. However, recently, I’ve been studying two types of tools that are very useful in handling the mindbogglingly numerous details of a complicated project. And I would like to tell you about those 2 tools here. So this is going to be a very limited answer to the question posed in the title, but still, I hope it will be an interesting answer.

The 2 tools I am referring to are:

  1. PERT diagrams.
    PERT diagrams are used for scheduling a project consisting of a series of interdependent activities and estimating how long it will take to finish the project. PERT diagrams were invented by the US NAVY in 1958 to manage a submarine project. Nowadays they are taught in many business and management courses.
  2. Reliability Box Diagrams (and the closely related Fault Tree Diagrams)
    Complicated devices with a large number of components such as cars or airplanes or submarines or aircraft carriers or rockets can fail in many ways. If their performance depends on some components working in series and one of the components in the series fails, this may lead to catastrophic failure. To avert such disasters, engineers use equivalent components connected in parallel instead of in series, thus providing multiple backup systems. They analyze the device to find its weak points and add backup capabilities there. They also estimate the average time to failure for the device.

Standard presentations of these 2 tools do not use Bayesian Networks. In the last week, I added 2 new chapters to my FREE book Bayesuvius, one chapter for each of the above 2 tools. What I did was to translate both of those tools into Bayesian Networks. So now you can do PERT analysis and Reliability/Failure Analysis without leaving your B net comfort zone, using only B Nets.

Brought to you by http://www.ar-tiste.xyz, purveyors of high quality and low cost Bayesian Network software & services. A COOP.

July 30, 2020

Prion Computer Viruses For China, With Love

Filed under: Uncategorized — rrtucci @ 4:28 am

Check out:
When China Sees All, Chinese AI Is Creating an Axis of Autocracy, by Ross Andersen (The Atlantic, Sept 2020 print edition)
And Sheena Greitens analysis of it.

This article made me think that the US Defense agencies and freedom fighter hackers could be working on cheap devices to detect and disable surveillance cameras from afar(*), and on “prion” computer viruses(**). Such liberating technology could be put into the hands of AI oppressed people. I think China’s use of AI for nefarious purposes would not be super difficult to foil because it relies on very fragile, networked devices.

(*) One could, for instance, tap into the surveillance camera network and inject a virus through there.
(**) By prion computer viruses I mean computer viruses specifically designed to surreptitiously degrade the behavior of artificial neural networks (ANNs) or other AI software. ANNs are very finicky and fragile devices that take a long time to train; certain subtle anomalies in that training will cause the ANN’s efficiency to plummet, resulting in the loss of days or weeks of training. I’ve heard of software designed to mitigate biases and blind spots in ANNs by subtly altering the final weights of the ANN. Couldn’t similar ideas be used to introduce biases and blind spots into ANN software? If done right, the virus could corrupt an ANN’s training or final weights and disappear once its job is done, unbeknownst to the ANN owner.


July 29, 2020

Ar-tiste.xyz, 1st COOP of Bayesian Network programmers

Filed under: Uncategorized — rrtucci @ 5:50 pm

At Ar-tiste, we don’t really “hire” programmers. We are loosely organized as a “coop” of top Bayesian Network programmers. More here.

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