Quantum Bayesian Networks

March 9, 2019

Current Plans for Qubiter and when is walking backwards a good thing to do?

Filed under: Uncategorized — rrtucci @ 6:21 pm

This post is to keep Qubiter fans abreast of my current plans for it.

As I mentioned in a previous post entitled “Welcome to the Era of TensorFlow and PyTorch Enabled Quantum Computer Simulators”, I have recently become a big fan of the program PennyLane and its main architect, Josh Izaak.

Did you know PennyLane has a Discourse? https://discuss.pennylane.ai/ I love Discourse forums. Lots of other great software (like PyMC, for instance) have discourses too.

I am currently working hard to PennyLanate my Qubiter. In other words, I am trying to make Qubiter do what PennyLane already does, to wit: (1)establish a feedback loop between my classical computer and the quantum computer cloud service of Rigetti (2) When it’s my computer’s turn to act in the feedback loop, make it do minimization using the method of back-propagation. A glib way of describing this process is: a feedback loop which does forward-propagation in the Rigetti qc, followed by backwards-propagation in my computer, followed by forward-propagation in the Rigetti qc, and on and on, ad nauseam.

I am proud to report that Qubiter’s implementation of (1) is pretty much finished. The deed is done. See my Qubiter module https://github.com/artiste-qb-net/qubiter/blob/master/adv_applications/MeanHamilMinimizer_rigetti.py This code has not been tested on the Rigetti cloud so it is most probably still buggy and will change a lot, but I think it is close to working

To do (1), I am imitating the wonderful Pennylane Rigetti plugin, available at GitHub. I have even filed an issue at that github repo

So far, Qubiter does not do minimization by back-propagation, which is goal (2). Instead, it does minimization using the scipy function scipy.optimize.minimize(). My future plans are to replace this scipy function by back-propagation. Remember why we ultimately want back-propagation. It’s because, of all the gradient based minimization methods (another one is conjugate gradient), backprop is the easiest to do in a distributed fashion, which takes advantage of GPU, TPU, etc. The first step, the bicycle training wheels step, towards making Qubiter do (2) is to use the wonderful software Autograd. https://github.com/HIPS/autograd Autograd replaces each numpy function by an autograd evil twin or doppelganger. After I teach Qubiter to harness the power of Autograd to do back-prop, I will replace Autograd by the more powerful tools TensorFlow and PyTorch (These also replace each numpy function by an evil twin in order to do minimization by back-propagation. They also do many other things).

In doing back-propagation in a quantum circuit, one has to calculate the derivative of quantum gates. Luckily, it’s mostly one qubit gates so they are 2-dim unitaries that can be parametrized as


where the k ranges over 1, 2, 3 and we are using Einstein summation convention. \theta_0, \theta_1,\theta_2, \theta_3 are all real. \sigma_k are the Pauli matrices. As the PennyLane authors have pointed out, the derivative of U can be calculated exactly. The derivative of U with respect to \theta_0 is obvious, so let us concentrate on the derivatives with respect to the \theta_k.

U = e^{i\sigma_3\theta_3} = C + i\sigma_3 S
S = \sin\theta_3, C = \cos \theta_3.
\frac{dU}{dt} = \dot{\theta}_3(-S + i\sigma_3 C)

More generally, let
U = e^{i\sigma_k\theta_k} = C +  i\sigma_k \frac{\theta_k}{\theta} S
\theta = \sqrt{\theta_k\theta_k}, S =  \sin\theta, C = \cos \theta
Then, if I’ve done my algebra correctly,

\frac{dU}{dt}=-S \frac{\theta_k}{\theta}  \dot{\theta_k}+ i\sigma_k\dot{\theta_r}  \left[\frac{\theta_k\theta_r}{\theta^2} C+   \frac{S}{\theta}(-\frac{\theta_k\theta_r}{\theta^2}    + \delta_{k, r})\right]

I end this post by answering the simple riddle which I posed in the title of this post. The rise of Trump was definitely a step backwards for humanity, but there are lots of times when stepping backwards is a good thing to do. Minimization by back propagation is a powerful tool, and it can be described as walking backwards. Also, when one gets lost in a forest or in a city and GPS is not available, I have found that a good strategy for coping with this mishap, is to, as soon as I notice that i am lost, back track, return to the place where I think I first made a mistake. Finally, let me include in this brief list the ancient Chinese practice of back walking. Lots of Chinese still do back-walking in public gardens today, just like they do Tai Chi. Both are healthy low impact exercises that are specially popular with the elderly. Back walking is thought to promote muscular fitness, because one uses muscles that are not used when walking forwards. Back walking is also thought to promote mental agility, because you have to think a little bit harder to do it than when walking forwards. (Just like counting backwards is a good test for sobriety and for detecting advanced Alzheimer’s)


March 5, 2019

The iSWAP, sqrt(iSWAP) and other up-and-coming quantum gates

Filed under: Uncategorized — rrtucci @ 8:28 am

The writing is on the wall. The engineers behind the quantum computers at Google, Rigetti, IonQ, etc., are using, more and more, certain variants of the simple SWAP gate, variants that are more natural than the SWAP for their devices, variants with exotic, tantalizing names like the iSWAP, and sqrt(iSWAP). In the last day or two, I decided to bring Qubiter up-to-date by adding to its arsenal of gates, a gate that I call the SWAY. SWAY is very general. It includes the humble SWAP and all its other variants too. So, what is this SWAY, you ask?

Let \sigma_X, \sigma_Y, \sigma_Z be the Pauli Matrices.

Recall that the swap of two qubits 0, 1, call it SWAP(1, 0), is defined by

SWAP = diag(1, \sigma_X, 1)

NOTE: SWAP is qbit symmetric, meaning that SWAP(0,1) = SWAP(1,0)

We define SWAY by

SWAY = diag(1, U2, 1)

where U2 is the most general 2-dim unitary matrix satisfying \sigma_X U2 \sigma_X=U2. If U2 is parametrized as

U2 = \exp(i[ \theta_0 + \theta_1\sigma_X + \theta_2\sigma_Y + \theta_3\sigma_Z])

for real \theta_j, then


NOTE:SWAY is qbit symmetric (SWAY(0,1)=SWAY(1,0)) iff \sigma_X U2 \sigma_X=U2 iff \theta_2=\theta_3=0

The Qubiter simulator can now handle a SWAY with zero or any number of controls of type T or F. Very cool, don’t you think?

Here is a jupyter notebook that I wrote to test Qubiter’s SWAY implementation


February 26, 2019

Seth Lloyd invented PennyLane. It’s a well known fact.

Filed under: Uncategorized — rrtucci @ 6:01 pm

I forgot to mention that Seth Lloyd invented PennyLane. The other people at Xanadu are all identical worker ants faithfully following his brilliant instructions on what to do, according to a Xanadu press release

Excerpt from press release:

“Deep learning libraries like TensorFlow and PyTorch opened up artificial intelligence to the world by providing an interface to powerful GPU hardware. With PennyLane, Xanadu is now doing the same for machine learning on quantum hardware,” said Seth Lloyd, Xanadu’s chief scientific advisor, MIT professor and a founding figure in both quantum computing and quantum machine learning. “We’re going to see an explosion of ideas, now that everyone can train quantum computers like they would train deep neural networks.”

Xanadu is a company whose “chief scientific advisor, MIT professor and a founding figure in both quantum computing and quantum machine learning”, Prof. Seth Lloyd, has promised to build a “continuous values” quantum computer device, which is a device invented by Seth Lloyd, according to him. This would be a quantum computer that is more analog and more classical than DWave’s. DWave, a qc company which was founded in 1999, 20 years ago, has never been able to provide error correction for its qc, so many experts believe that Xanadu’s Lloydian qc will be very difficult to error correct too. But if anyone can solve this prickly conundrum, it’s Seth Lloyd, who, according to him, is the original inventor of DWave’s device too.


Wow wee! Seth Lloyd’s invention, an optical computer that runs Tensorflow, is really taking off at MIT. I just came across this news report on a company called LightMatter, funded by some heavyweights like Google Ventures, that proposes to do just that.



Lightmatter just picked up its first backing from a corporate investor: GV, a venture arm of Google parent company Alphabet.

In 2014, Nick Harris and Darius Bunandar were trying to combine optical technology with quantum computing at the Massachusetts Institute of Technology, where they were doing Ph.D. work in the same research group.

But in 2015, Harris and Bunandar began looking at fields beyond quantum computing, including AI. “Our feeling is that there are a huge number of challenges that remain to be solved” for their quantum approach, Harris said.

“There is a lot of effort that goes into making this kind of device plug and play and making it look a lot like the experience of an Nvidia GPU,” Harris said. The team wants to ensure the chips work with popular AI software such as the Google-backed open-source project TensorFlow.

February 24, 2019

Welcome to the Era of TensorFlow and PyTorch Enabled Quantum Computer Simulators

Filed under: Uncategorized — rrtucci @ 8:40 pm

In my previous blog post, I unveiled a new Jupyter notebook explaining how to use Qubiter (a quantum computing simulator managed by me) to do hybrid quantum-classical (HQC) quantum computing. In that prior blog post, I admitted that even though that meant that Qubiter could now do a naive type of HQC, Qubiter could not yet do fully fledged HQC, which I defined as (1) using distributed computing/back propagation driven by TensorFlow or PyTorch (2) using as backend a physical qc device such as those which are already accessible via the cloud, thanks to IBM and Rigetti. I pointed out that the wonderful software PennyLane by Xanadu can now do (1) and (2).

This blog post is to unveil yet another Jupyter notebook, this time showing how to use Qubiter to translate potentially any quantum circuit written in Qubiter’s language to the language of PennyLane, call it Pennylanese. This means Qubiter can now act as a front end to PennyLane, PennyLane can act as an intermediary link which is TensorFlow and PyTorch enabled, and Rigetti’s or IBM’s qc hardware can act as the backend.

So, in effect, Qubiter can now do (1) and (2). Here is the notebook


I, Nostradamucci, have been prognosticating the merging of quantum computing and TensorFlow for a long time in this blog

I, Nostradamucci, foresee that PennyLane will continue to improve and be adopted by many other qc simulators besides Qubiter. Those other qc simulators will be modified by their authors so that they too can act as frontends to PennyLane. Why not do it? It took me just a few days to write the Qubiter2PennyLane translator. You can easily do the same for your qc simulator!

I, Nostradamucci, also foresee that many competitors to PennyLane will crop up in the next year. It would be very naive to expect that everyone will adopt PennyLane as their method of achieving (1) and (2).

In particular, Google will want to write their own (1)(2) tool. Just like Google didn’t adopt someone else’s quantum simulator, they started Cirq instead, it would be naive to expect that they would adopt PennyLane as their (1) (2) tool, especially since TensorFlow is their prized, scepter of power. Just like Google rarely adopts someone else’s app for Android, they write their own, Google rarely adopts someone else’s app for TensorFlow (and Cirq, and OpenFermion), they write their own.

And of course, the Chinese (and the independence-loving French, Vive La France!) prefer to use software that is not under the control of American monopolies.

I see PennyLane as a brilliant but temporary solution that allows Qubiter to achieve (1) and (2) right now, today. But if Google provides a (1)(2) tool in the future, I will certainly modify Qubiter to support Google’s tool too.

In short, welcome to the era of TensorFlow and PyTorch Enabled Quantum Computer Simulators.

February 21, 2019

Qubiter can now do Hybrid Quantum-Classical Computation, kind of

Filed under: Uncategorized — rrtucci @ 11:33 am

Habemus papam…kind of. So here is the scoop. Qubiter can now do Hybrid Quantum-Classical Computation…kind of. It is not yet of the most general kind, but we are getting there. “The journey of a thousand miles begins with one step.” (a saying attributed to Chinese philosopher Laozi, 600 BC)

The most general, what the Brits would call The Full Monty, would be if Qubiter could
(1) use distributed computing and back-propagation supplied by TensorFlow, PyTorch, and

(2) run a hybrid quantum-classical simulation on a physical hardware backend such as those already available to the public via the cloud, thanks to the companies IBM and Rigetti.

At this point, Qubiter cannot do either (1) or (2). Instead of (1), it currently does undistributed computing executed by the Python function scipy.optimize.minimize. Instead of (2), it uses Qubiter’s own built-in simulator as a backend.

Amazingly, the wonderful open-source software Pennylane by Xanadu already does (1) and (2). So far, they are the only ones that have accomplished this amazing feat. None of the big 3: Google Cirq, IBM Qiskit, and Rigetti Pyquil can do (1) yet either so we are in good company. I am sure that eventually, the big 3 will succeed in coaxing their own software stacks to do (1) and (2) too. But probably not for a while because large companies often suffer from infighting between too many generals, so they tend to move more slowly than small ones. They also almost always shamelessly copy the good ideas of the smaller companies.

I too want to eventually add features (1) and (2) to Qubiter, but, for today, I am happy with what I already have. Here is a jupyter notebook explaining in more detail what Qubiter can do currently in the area of hybrid-quantum classical computation


February 15, 2019

Derivative of matrix exponential wrt each element of Matrix

Filed under: Uncategorized — rrtucci @ 1:26 am

In the quantum neural net field, in order to do backpropagation, one often wishes to take the derivative of a unitary matrix with respect to a parameter it depends on. The wonderful software PennyLane by Xanadu evaluates such derivatives using a simple formula which gives an exact answer, albeit only in special cases. Here I will discuss a simple formula that is fully general, albeit only an approximation, although reputedly a very good approximation, probably due to its symmetric nature and the smoothness of exponential functions. The method is a simple symmetric finite difference approximation.

In a StackExchange question with exactly the same title as this post, somebody called Doug suggested what he calls Higham’s “Complex Step Approximation”, to wit:

If A is a Real matrix and E_{rs} is the matrix which is 1 at position r,s and zero elsewhere,

\frac{d}{dA_{rs}}e^{A} \approx  \frac{ e^{A + ihE_{rs}} - e^{A-ihE_{rs}} }{2ih} = \frac{{\rm Im}(e^{A + ihE_{rs}})}{h}

But what if A is a Hermitian matrix and we want the derivative of exp(iA)? Here is a simple adaptation of Higham’s formula to that case.

Let E^\pm_{rs} = E_{rs} \pm E_{sr}. Note that (E^\pm_{rs})^\dagger = \pm E^\pm_{rs}.

Define a matrix M(A) by

M(A) = \left[  \begin{array}{cc} 0 &-e^{-iA}\\ e^{iA} & 0 \end{array}\right]


M(A+ih) = \left[ \begin{array}{cc} 0 &-e^{-iA+h}\\ e^{iA-h} & 0 \end{array}\right]


M(A+ih)^\dagger = \left[ \begin{array}{cc} 0 &e^{-iA-h}\\ -e^{iA+h} & 0 \end{array}\right] =-M(A-ih)

From this, one learns the following simple recipe: the effect of the dagger on M(A) is to put a minus sign in front of the M and to take the Hermitian of the argument too.


\frac{d}{d{\rm Re\;}A_{rs}}M(A) \approx \frac{ M(A + ihE^+_{rs}) - M(A-ihE^+_{rs}) }{2ih}= \frac{{\rm Re\;}[M(A + ihE^+_{rs})]}{ih}


\frac{d}{d{\rm Im\;}A_{rs}}M(A) \approx \frac{ M(A + hE^-_{rs}) - M(A-hE^-_{rs}) }{2h}= \frac{{\rm Re\;}[M(A + hE^-_{rs})]}{h}.


\frac{d}{dx}M(A) = \left[ \begin{array}{cc} 0 &-\frac{d}{dx}e^{-iA}\\ \frac{d}{dx}e^{iA} & 0 \end{array}\right] ,

it follows that

\frac{d}{dx}e^{iA} = \left[\frac{d}{dx}M(A)\right]_{10}

for x = {\rm Re\;} A_{rs}, {\rm Im\;} A_{rs}

When A is Hermitian, A_{rs} and A_{sr} = A^*_{rs} are complex conjugates so they are not independent, but the real and imaginary parts of A_{rs} are independent, so one can treat A_{rs} and A^*_{rs} as independent and do a change of variables from (A_{rs}, A^*_{rs}) to ({\rm Re}A_{rs}, {\rm Im}A_{rs}).

For discussion about this topic, in the context of quantum neural networks, see the following thread of the Pennylane discourse website

February 9, 2019

Extra, extra, read all about it! Next Toronto Quantum Computing Meetup event just announced. The speaker will be physicist and tech start-up entrepreneur Wojtek Burko

Filed under: Uncategorized — rrtucci @ 6:13 pm

The Toronto Quantum Computing Meetup is the second largest meetup in the world dedicated to quantum computing, so we claim the silver medal of Quantum Meetup Supremacy, at least for now. (currently we have 1565 Supremos as members. The biggest club is in London with a distinguished 1756 Brexiters members. We used to be the biggest, but, oh well, sic transit gloria mundi).

Those poor Brexiters. We don’t envy them one bit, or one qubit. They are in a though spot. We are so worried for them that we will soon be shipping to them a few “brexit boxes”, just in case. Just like in quantum physics, their predicament can be illustrated very well using cats. The perilous Brexit cat jump

But I digress. The main purpose of this blog post is to cordially invite you to our next meeting on Thursday, February 21, 2019. At the usual outstanding venue, Rotman School, Univ. of Toronto. Our speaker is quite impressive. I will just quote the writeup:

We are delighted to have physicist and tech start-up entrepreneur Wojtek Burko to talk about the challenges to build a venture based on the current state of the art in quantum computing.

Wojtek not only co-funded Beit.tech and succeeded in making it cash-flow positive in less than a year, but also co-launched the Bitspiration Booster VC practice, which invests in and incubates deep tech start-ups.

Before that he served as Engineering Director at Google until 2014, Chief Technology Officer at Allegro until 2015, entrepreneur, mentor, advisor, angel investor; member of the Board in several start-ups incl. EGZOTech, JAM Vehicles, Airly; chairman of the Board at ASPIRE – representing the multinational corporate scene in Poland.

Microsoft reacquires ProjectQ

Filed under: Uncategorized — rrtucci @ 9:03 am

Huawei is a major Chinese company that produces smart phones, telecommunications equipment used in 5G, etc. This company has been much in the news recently because the founder’s daughter, Meng Wanzhou, who is also the CFO of the company, was arrested at the airport in Vancouver, Canada, at the request of the US government.

As far as the subject of quantum computing is concerned, on Oct 12, 2018, Huawei promised in a conference and accompanying press release, that it would soon provide a quantum computing cloud service named HiQ. As far as I know, HiQ hasn’t been opened yet to the general public. I have searched in vain for it on the internet. According to the press release, “Huawei also showcased its quantum programming framework for the first time, which is compatible with the ProjectQ.” As you can see by the two linkedin screenshots below (slightly cropped to omit my personal info), Huawei was employing, at the time of the press release, the two authors of projectQ, Haner and Steiger, whose thesis advisor at ETH Zurich was Matthias Troyer, a Microsoft star employee.

Seems like Haner and Steiger duped Huawei, they only stayed with Huawei for 3 months, while they were secretly looking for a job at Microsoft. They were hired in the last 1-2 months as Senior Quantum Computing Researchers by Microsoft. So ProjectQ was owned by Huawei for just 3 months and is now back in the hands of the evil empire Microsoft.

Huawei and China must feel “slightly” disrespected by Microsoft for hiring those two. What if the Chinese government bans Azure from China for this slight and for the arrest of Huawei’s founder’s daughter? And what about ProjectQ versus Q#? Does this mean, if we read the tea leaves, that ProjectQ/Troyer is back again in the ascendancy at Microsoft and Q#/Krysta is in the decline? It’s like a power struggle inside the Third Reich of Microsoft

February 8, 2019

Quantum Complexity Theory for Fish

Filed under: Uncategorized — rrtucci @ 6:29 am

(This graphic was inspired by a slide in a talk by John Preskill. His version makes no allusion to sea or fish. It just uses Venn diagrams)

February 4, 2019

Translating Between Quantum Programming Languages, The Importance of Being Qubiter

Filed under: Uncategorized — rrtucci @ 7:05 am

The United Nations has 6 official languages: English, French, Spanish, Chinese, Russian and Arabic. I think the more languages you learn, the smarter you become. Don’t you?

Qubiter, a computer program/quantum programming language that I wrote, can translate from itself to the 3 most popular quantum languages that currently have a hardware backend: IBM Qiskit, Rigetti Pyquil, and Google Cirq. Here is a Jupyter notebook illustrating this feature of Qubiter.


I’ve recently noticed that others, both in Academia and in Industry, are attempting to write their own translators between quantum languages, so there seems to be a lot of interest out there for this sort of thing. Many quantum computerists are interested in running the same quantum program side by side on the 3 hardware devices just mentioned (and others soon to come) to compare performance and final results, a worthy scientific goal.

I want to argue briefly here that Qubiter does it better 😎

Qubiter has ALL the features that each of the big 3 quantum programming languages has and then some. This makes it the most expressive tool in town. You get a more succinct, efficient translation if you using a more expressive computer language to express a command in a less expressive one. For example, if you

use more expressive to express less expressive,
less expressive -> more expressive
you might find
10 lines of code -> 10 lines of code,

whereas if you go in the opposite direction,

use less expressive to express more expressive,
more expressive -> less expressive
you might find
10 lines of code -> 30 lines of code,

because you are “trying to reinvent the wheel” in the second case.

So if Qubiter is to maintain its edge as a translator of quantum programming languages, it must always try to surpass all the other languages in expressiveness. This is what I have done so far and will try to do in the future. (For example, I recently added to Qubiter, placeholders and loops at the English file level. And take a look at the feature comparison table that I gave in a previous, recent blog post).

February 3, 2019

My Mini-Talk in absentia for FOSDEM 2019 Feb 2-3 (this weekend)

Filed under: Uncategorized — rrtucci @ 1:19 am

As I mentioned in the previous blog post, Henning Dekant, CEO of our startup Artiste-qb.net, will be speaking for our company at FOSDEM 2019 this weekend. I can’t be there so I prepared a mini-talk to be delivered long distance, via the old fashioned internet, as opposed to that new fangled technique that some are calling tête à tête. Henning is speaking mainly about Quantum Fog and Bayesian networks so I made my mini talk about a different topic; it’s a progress report on Qubiter, about the fact that Qubiter now supports Placeholders and loops at the English file level. Here is my mini-talk:


its front page:

January 27, 2019

Artiste-qb.net will be at FOSDEM 2019, Feb 2-3, 2019

Filed under: Uncategorized — rrtucci @ 3:45 am

FOSDEM is a yearly hacker conference dedicated to open source software. It is usually attended by > 8,000 hackers. Its venue is the Université Libre de Bruxelles Solbosch campus in the southeast of Brussels, Belgium. This year, FOSDEM will be held on Saturday Feb 2 and Sunday Feb 3, and Henning Dekant, the CEO of our startup Artiste-qb.net, will be attending. Henning will give a short 25 minute talk entitled “Elevating the Stack” on Sunday morning, followed by a 4 hour workshop on Sunday afternoon.

If you are interested and want to prepare, Henning will issue the following grand, in your face, challenge to the attending hackers.

IBM Qiskit already provides software that converts quantum circuits to DAGs (aka, Quantum Bayesian Networks). Can you build an engine that does the reverse, converts Quantum Bayesian Networks to quantum circuits?

Someday, we hope Quantum Fog will be able to feed qb nets to Qubiter in this way. Quantum Fog and Qubiter are 2 open source Python programs that I wrote. You think you are so smart and such a great hacker, but are you smart enough to write such a translator before we do? In your face challenge.

January 26, 2019

Quantum Computing Startups That are like the ghost of Jacob Marley in Dickens’s novel “A Christmas Carol”

Filed under: Uncategorized — rrtucci @ 8:52 pm

A Trumpian argument that I often hear from VCs interested in investing in quantum computing startups is that software-only startups like Artiste-qb.net are too risky, whereas startups that are trying to build hardware and a full software library for that hardware, are much less risky.

Huh? This is the type of illogical thinking that Trump, his MAGA supporters, and Fox News commentators often use, a type of thinking that I find totally inscrutable. It flies in the face of all available evidence and facts.

Microsoft, Google and Facebook were originally software-only companies.

Hardware is 100 times more risky and expensive to develop than software. If you are going to invest in a qc company that does hardware, you should expect, optimistically, to have to wait at least 5 years before it builds a product, and 10 years before it makes a profit. And hardware development often fails. You can’t bullshit Mother Nature. Also, you should be sure that the startup will be able to procure from all sources at least $100M investment in it’s first 5 years of operation, like Rigetti has. Otherwise, your baby startup will die of malnutrition. In contrast, software companies like Artiste-qb.net can make a profit almost from day 1, by doing non-quantum AI programming on the side.

Software companies are very nimble; they can pivot quickly, changing their business plan and product if the current one is not working. Hardware companies are tied to their hardware like Jacob Marley is to his ball and chain.

Take DWave as an example. Sorry if I get into trouble for stating the obvious, but DWave will NEVER be able to recoup the $200M+ dollars that have been invested mostly in its hardware. DWave is now trying mightily to rebrand itself as an AI services company, but how far can they run with that annealer ball and chain tied to their leg, in the very crowded AI field? It is highly questionable whether their annealer device ever showed any sufficient advantage over classical devices to make it commercially viable, and now a bumper crop of new qc companies has taken the limelight away from them. I was always supportive of Dwave to some extent; they did start the current avalanche of interest in commercial qc, for which I am grateful. But, as far as Dwave investors are concerned, it was a TOTAL loss for them.

Despite DWave’s cautionary tale, I still find a lot of VCs trying to fund the next Dwave. I won’t name names, but some of the hardware qc startups that have been funded recently seem to be following in DWave’s footsteps, except I doubt they will find as much funding. I think they will die of malnutrition. DWave got so much funding because it was the first.

Artiste-qb.net is a software-only company. This is a VIRTUE, you low IQ person who thinks otherwise. Besides, we already have a product on the market, http://www.Bayesforge.com, not just a vague promise of a product. What we need now are investors to help us ramp up the product. For these reasons, we are 1/100 times as risky an investment as a hardware qc startup.

January 25, 2019

Chinese Startup, Origin Quantum, publishes impressive software, QRunes, for hybrid classical-quantum computing

Filed under: Uncategorized — rrtucci @ 7:44 am

Check this paper out, especially if you are a VC or work for Rigetti

“QRunes: High-Level Language for Quantum-Classical Hybrid Programming”, by Zhao-Yun Chen, Guo-Ping Guo, https://arxiv.org/abs/1901.08340

The authors work for Origin Quantum. Will this company someday provide a serious alternative to Rigetti’s hybrid classical-quantum cloud service? Maybe.

Origin Quantum is a very well funded Chinese startup, based in Heifei, that is working on qc hardware and software, both. I sincerely wish them success! They remind me very much of Rigetti in that they are developing both hardware and software, plus they are strongly committed to the hybrid approach. Not as advanced as Rigetti yet, but a serious challenger. They have the formidable advantage that they will be strongly favored over American companies like Rigetti, by Chinese users. Besides, quantum computing is a marathon, not a sprint, so Rigetti’s head start might turn out to be of little value in the long run.

As you can see from reading the numerous posts in this blog, our startup Artiste-qb.net is very different from both Origin Quantum and Rigetti, although in some very specialized areas, we think we are much stronger than both of them 😎. Our crown jewel is http://www.Bayesforge.com, a collection of many popular open source softwares in a docker image that is currently available on both the Amazon and Tencent clouds. Although Artiste is incorporated in Canada and based in Toronto, one of our cofounders, Dr. Tao Yin, runs an affilate company in Shenzhen, China. Our software Qubiter is similar to QRunes in some important respects.

January 24, 2019

Qubiter (a quantum programming language) now has functional placeholders

Filed under: Uncategorized — rrtucci @ 11:04 pm

On Jan 11, just 13 days ago (by comparison, Trump’s shutdown is now 34 days old), I wrote a blog post announcing that what I call “placeholders” had been incorporated into Qubiter. Since then, my ideas about placeholders have been undergoing a Darwinian evolution culminating with an update that I uploaded today to Qubiter’s Github repo, in which I add to Qubiter, a new kind of placeholder, what I like to call “functional placeholders”. (previous types of placeholders still work). In a functional placeholder, instead of entering a numerical rotation angle \theta for a quantum gate like R_Z(\theta), one enters a string that includes a function’s name. Here is an example written in the Qubiter language. In the example, '-my_fun#2#1' is evaluated to -my_fun(#2, #1). Notice from the example that we delay deciding the value of all placeholders until the moment right before we run the simulator, at which time we enter the values of all the placeholders as an input to the simulator. That is the essence of placeholders.

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