I’ve just installed an excellent “Purple Yin Guestbook” script (free software!) in my website. I hope you will sign it if you are interested in quantum computer programming, and you want others, especially others in your city, to be aware of your existence. You don’t have to be a programmer. You could be, for example, an angel investor or entrepreneur (Hi Bill Gates 
) interested in the topic Maybe this will help you find a collaborator with whom to write that quantum computer program you’ve been meaning to write. Maybe you’ll meet here some venture capitalists interested in funding your wacky visionary software. Maybe you’ll meet here someone with whom to go out for a coffee, maybe even your future wife. Who knows?
June 27, 2009
Guestbook for People Interested in Quantum Computer Programming
June 26, 2009
Quantum Cryptography Snake Oil
In a previous post, I expressed my agreement with Bruce Schneier’s opinion about quantum crypto. Today I came across a news item entitled:
“Global Quantum Cryptography Market to Reach $842 Million by 2015, According to a New Report by Global Industry Analysts”
What a dumb investment that would be! That money could be used to build a large scale quantum computer. $842M = (70M)(12) = 12 D-Waves (or maybe more, if they come cheaper by the dozen)
June 13, 2009
Who will be the Microsoft of the Quantum Computer Age?
In a recent article, Patrick Cox at Penny Sleuth advises investors to invest now in companies that look like they might become the Intel of the quantum computer age. For every Intel, there is usually a Microsoft. So I advise investors to also invest in companies that might become the Microsoft of the quantum computer age. (Needless to say, I wouldn’t mind working as a programmer at this future Microsoft ) If I were an investor interested in investing in quantum computing, I wouldn’t put all my eggs in the hardware basket, I would also put some in the software basket. This, not only because diversification usually lowers risk, but also because investing in QC software companies is far less risky than investing in QC hardware companies. Indeed, a QC software company has the following advantages over a QC hardware one:
- No need for bricks and mortar. You could have a QC software company in which all the employees worked at their homes, at different geographical locations. They could easily collaborate via the internet. On the other hand, a QC hardware company requires a laboratory/workshop at which most of its employees congregate daily.
- Cheaper equipment. The tools required by a QC software company are mostly just computers and internet connections. These are relatively cheap. The tools required by a QC hardware company are much more expensive. Low temperature and high vacuum equipment, clean rooms, etc., all this costs millions of dollars. Some of the tasks that use expensive, highly specialized equipment can be contracted out, but that too is expensive.
- No need to bet on a specific hardware model. There are several hardware models vying to become the first large scale quantum computer. (ion-trap, semiconductor, superconductor, photons, quantum dots. etc.) It’s hard to predict which hardware model will win out. Because of the high costs involved in the equipment and human expertise, each QC hardware company is forced to bet on just one hardware model. The beauty of QC software is that it can be written so that it is pretty much hardware-model independent. Alternatively, one can write software that translates from one machine language, suitable for a specific platform, to another.
See also this blog post on a Pixar analogy.
June 11, 2009
D-Wave Company
I recommend the following recent article on D-Wave Inc.
“The Tortoise and the Hare”, by Danielle Egan, Phillip Chin, BCBusiness, Jan. 01, 2009
I found the article to be well-written, balanced, candid and full of interesting details about D-wave. It quotes several science and business experts who run the gamut from very optimistic to very pessimistic about the prospects of D-Wave. Personally, I believe that D-wave, like any other company, has some good and bad features. Let me discuss in this blog post what I see as its good features:
- Focal Point for QC Investors. One thing that few can dispute is that D-wave has done an impressive job at courting investors for money, about $70 million in the last 15 years (about $20 million from the Canadian government, and about $50 million from private investors). D-wave has proven that there are people out there who believe in quantum computing and are willing to invest in it. One just has to make an effort to find them.
- First Step towards a QC Industry . What has D-wave gotten in return for $70 million in 15 years? Their current chip has 128 qubits, and they are promising a 1000 qubit chip by the end of 2009. By comparison, the US government has spent about 1 billion dollars on quantum computing during the same period, and their best quantum computers (ion trap QCs) have about 10 qubits, and cannot be scaled to much higher qubit numbers, except through some Rube Goldberg schemes. Of course, the D-wave computer is adiabatic and the ion trap ones aren’t, so comparing them in terms of number of qubits is not very fair. Still, the difference between $70 million and $1 billion is astonishing. This supports the contention that quantum computing will advance much faster if it is both an industrial and an academic enterprise. The example of Craig Venter and Celera is quite pertinent here. Without industry (Celera), scientists probably would have taken at least ten more years to map the human genome. Unfortunately, quantum computing in the US is so far mostly an academic, government funded endeavour — a very narrow combination. The US has no quantum computing industry similar to Canada’s D-wave.
- Job Generator. Another commendable fact about D-Wave is that it has generated some rewarding HiTech jobs. It “has 13 full-time employees, as well as 60 research collaborators in the U.S. and Europe.”
- Beautiful Experiment. Even if D-wave runs out of money before it can generate a profit, it will undoubtedly have carried out some very interesting physics experiments. For example, it will go a long way towards answering the question of whether adiabatic quantum computers are useful in practice.
- Seems Well Positioned for the Future. D-wave has assembled a team of first rate experts in superconductor physics. It has gone where no man has gone before in terms of producing chips with a large number of coupled SQUIDS. It has accumulated a large number of patents and much experience pertaining to such models. If adiabatic QCs prove to be profitable, then it will become the first player in a new market. If not, it might still be able to use the expertise it has accumulated to produce other superconductor devices, maybe even a discrete-steps (sequence of elementary operations) QC with error correction (See recent DiVincenzo paper).
May 24, 2009
Software For Bayesian Networks
If you are interested in software for Bayesian nets, I enthusiastically recommend that you read K. Murphy’s excellent 3 page review article entitled “Software for Graphical Models – A Review” (ISBA Bulletin, Dec 2007).
By the way, the website of Prof. K. Murphy (Uni. of British Columbia) is also well worth a visit. I found the class notes for his courses on B nets and related topics very clear and helpful. Prof. Murphy is the author of a popular B net software package (in MatLab/C) called Bayes Net Toolbox (BNT). For many years, he has kept an authoritative table comparing various B net computer programs.
May 17, 2009
WolframAlpha + Bayesian Networks = ?
WolframAlpha is a beautiful, amazing computer program. As with any truly novel idea, on first encountering it, one struggles to put it into context. Here is how I view it, from a Bayesian Networks perspective.
Quote from the WolframAlpha website:
What is the core technology of WolframAlpha?
There are many parts to it, each with significant innovations. Four key general areas are the data curation pipeline, the algorithmic computation system, the linguistic processing system, and the automated presentation system.
I view the current WoframAlpha as an intermediate step towards full AI. To answer my own question in the title, I would say
WolframAlpha + Bayesian Networks = Hal 100 (not yet 9000) possible in five years
| AI task | WolframAlpha equivalent task | limitations of current WolframAlpha |
| remember | data curation pipeline | Can’t self-add data (for instance, data acquired from internet) to curated data. In this sense, it’s ability to remember what it hears is very limited. |
| analyze | algorithmic computation system | It’s a deterministic, rule-based analyzer. If it also used bayesian networks and statistics, it could analyze cases, common in real life, in which some of the input data is missing or contradictory. |
| hear | linguistic processing system | Can’t hear except from command line. |
| speak | automated presentation system | Limited ability to say things in meaningful prose. |
| self-teach (learn independently) | not yet | Can’t learn new rules from its own analyses. Bayesian network learning could help here. |
May 12, 2009
QC Paulinesia
Picture yourself strolling down this Polynesian beach
Tensors are like multi-legged creatures. A leg (index) of one creature (tensor) can be connected (contracted, summed over) with the leg of another creature. Tensor networks consisting of many interconnected creatures are very common in Physics. For example:
‘t Hooft and Veltman used tensor networks to explain some aspects of quantum field theory (Ward-Takahashi identities, renormalization, etc) in their famous 1973 paper entitled “Diagrammar”.
Predrag Cvitanovic used tensor networks (he calls them birdtracks) to explain both, quantum field theory in this “webBook”, and group representation theory in this webBook.
Sir Roger Penrose used tensor networks to do general relativity calculations. See here
Quantum circuit diagrams are tensor networks too. In my 2004 paper, “QC Paulinesia”, I tried to compile all the tensor network (circuit diagram) identities I had encountered while learning about quantum computing. Sort of like a table of integrals for quantum computerists. These identities almost always entail Pauli matrices, and I was reading a travel book about the Polynesian islands at the time, hence the title. (My original plan was to publish periodic updates of QC Paulinesia, with corrections and additions. I haven’t done a single update yet though, because, because…okay, because I’m damn lazy.)
May 8, 2009
The QIS Workshop
Prof. Scott Aaronson (MIT) has just written a blog post about his experiences at the Quantum Information Science (QIS) Workshop (April 23-26 Vienna VA, 2009). (QIS research includes quantum computer research.) This is my comment about his blog post. (I also wrote an earlier blog post about the politics behind the QIS workshop.)
On 2002, a team of researchers published this paper:
Chi02: Andrew M. Childs, Richard Cleve, Enrico Deotto, Edward Farhi, Sam Gutmann, Daniel A. Spielman, “Exponential algorithmic speedup by quantum walk”. Abstract here
Chi02 uses a vector space V spanned by a set of orthogonal states, one state for each node of the following graph.
Fig.1- Chi02 graph
Chi02 considers a pure state from V. The state is a traveling wave moving from left to right of the above graph, evolving according to Schrodinger’s equation with a “quantum walk” Hamiltonian (proportional to the adjacency matrix of the graph). Chi02 finds that the quantum walk travels from entrance to exit exponentially faster than the classical random walk. (A sort of quantum tunneling effect)
More recently, Prof. Alán Aspuru-Guzik (Harvard) and Prof. Seth Loyd(MIT) have published this paper with their students:
Moh08: Masoud Mohseni, Patrick Rebentrost, Seth Lloyd, and Alan Aspuru-Guzik “Environment-Assisted Quantum Walks in Photosynthetic Energy Transfer”. Abstract here
where they claim that a quantum walk (albeit a heavily damped one) can be used to describe photosynthesis. They also claim, without proof, that their model and photosynthesis exhibit the Chi02 effect. Of course, the Moh08 paper doesn’t use its model to predict any observed experimental data.
Aspuru gave a talk about his work at the QIS Workshop. In the words of Scott Aaronson
Fig. 2- Light Harvesting Molecule
In a talk that was so good, you almost forgot it involved chemistry, Alán Aspuru-Guzik discussed applications of quantum complexity theory to understanding photosynthesis and the design of efficient solar cells (!). …Shown above is a light-harvesting molecule (image snagged from Alán’s slides), which apparently is efficient at concentrating light at its center for essentially the same reason the Childs et al. quantum walk reaches the target vertex exponentially faster than a classical walk:..
And according to Prof. Andrew Landhal (U. of New Mexico)
Just a quick comment on Alán Aspuru-Guzik’s talk, which was indeed fantastic and inspirational…
Well, let’s see. The Chi02 result is for a pure state with quantum coherence over all the nodes of Fig.1. There is no way that you are going to get quantum coherence over the thousands of atoms in Fig.2, for any significant amount of time, when these atoms are sitting inside a stew at room temperature or hotter. The quantum walk MohO8 is considering will degrade in this thermal environment to a classical random walk, so it can’t possibly exhibit Chi02’s purely quantum effect.
I don’t see how a paper that proposes a very speculative model of photosynthesis, makes dubious, amazing and unproven assertions about its connection to complexity theory, and predicts no observed data, can be so inspirational. But maybe I’m totally wrong. (would not be the first time ) You be the judge. See Aspuru’s comment (#20) here. Extraordinary claims require extraordinary proof.
May 4, 2009
PageRank – How Google used Statistics to Change the World
How I think history books circa 2100 will read:
When widespread adoption of classical computers and the Internet arrived in the late 20th century, human society suddenly became capable of quickly storing, accessing, and analyzing huge data sets. The stage was set for a band of young technologists called Google to harness Probability and Statistics to analyze these huge data sets. PageRank was their algorithm. Simple math, just finding the stationary state of a Markov chain, was key to how Google changed the world.
A second wave of technology followed soon after, and this one towered over the first in importance. Quantum Computer mainframes became available from 2020 onwards. These were harnessed by the visionary <insert your name here> to analyze, again huge data sets, again using the tools of Probability and Statistics (such as Bayesian Networks, of which Markov Chains are an example). But this time the data sets came fast and furious, not just from what people wrote and posted on the Internet, but also from AI and biotech and nanotech. Quantum computers could do such analyses much faster than classical computers. Subtle AI finally became possible. Quantum thinkers that could ponder about terabytes of data, and make sense of it in a heartbeat; machines, called johnnies, with IQ’s thousands of times higher than that of Johnny von Neumann, had finally arrived.
Okay, enough geek day dreaming for today. Here are some excellent links explaining Google’s PageRank. It’s clever but simple math (understandable by most undergraduate students in engineering or science).
BigTable and MapReduce are also very interesting Google technologies that go hand in hand with PageRank. Mike Nielsen (co-author of a popular textbook on Quantum Computing) explains those nicely in his Google Technology Stack
April 30, 2009
MRI for Quantum Computerists
Recently, the press has been reporting on how NIST researchers used the NMR (a.k.a. MRI) technique of Spin Echo to decrease the decoherence rate of an array of hundreds of ultracold beryllium ions.
If you are interested in the physics of quantum computer hardware, and are a beginner in this area, I recommend that you familiarize yourself with the rudiments of NMR – stuff like 
