Once upon a time, the British Government decided to invest some 80 million into quantum computing.
Jeremy O'Brien told his peers that he had the best tech, and that he should get it all.
Some well connected peers from well known universities did not agree however, and also bid for the money, and won.
Jeremy was defeated. And pissed.
So he moved to Palo Alto and raised a total of $665 million instead as of 2021. The end.
Makes for a reasonable the old man lost his horse.
www.ft.com/content/afc27836-9383-11e9-aea1-2b1d33ac3271 British quantum computing experts leave for Silicon Valley talks a little bit about them leaving, but nothing too juicy. They were called PsiQ previously apparently.More interestingly, the article mentions that this was party advised by early investor Hermann Hauser, who is known to be preoccupied about UK's ability to create companies. Of course, European Tower of Babel.
The departure of some of the UK’s leading experts in a potentially revolutionary new field of technology will raise fresh concerns over the country’s ability to develop industrial champions in the sector.
These are a bit like the Verilog of quantum computing.
One would hope that they are not Turing complete, this way they may serve as a way to pass on data in such a way that the receiver knows they will only be doing so much computation in advance to unpack the circuit. So it would be like JSON is for JavaScript.
One important area of research and development of quantum computing is the development of benchmarks that allow us to compare different quantum computers to decide which one is more powerful than the other.
Ideally, we would like to be able to have a single number that predicts which computer is more powerful than the other for a wide range of algorithms.
However, much like in CPU benchmarking, this is a very complex problem, since different algorithms might perform differently in different architectures, making it very hard to sum up the architecture's capabilities to a single number as we would like.
The only thing that is directly comparable across computers is how two machines perform for a single algorithm, but we want a single number that is representative of many algorithms.
For example, the number of qubits would be a simple naive choice of such performance predictor number. But it is very imprecise, since other factors are also very important:
- qubit error rate
- coherence time, which determines the maximum circuit depth
- qubit connectivity. Can you only connect to 4 neighbouring qubits in a 2D plane? Or to every other qubit equally as well?
Quantum volume is another less naive attempt at such metric.
He's a bit lazy to explain why here, but Googling will be more than enough.
There is a risk it will fizzle and the bubble pop, like any revolution.
But recent developments are making it too exciting to ignore.
Really weird and obscure company, good coverage: thequantuminsider.com/2020/02/06/quantum-computing-incorporated-the-first-publicly-traded-quantum-computing-stock/
Publicly traded in 2007, but only pivoted to quantum computing much later.
Quantum mechanics is quite a broad term. Perhaps it is best to start approaching it from the division into:
- non-relativistic quantum mechanics: obviously the simpler one, and where you should start
- relativistic quantum mechanics: more advanced, and arguably "less useful"
Key experiments that could not work without quantum mechanics: Section "Quantum mechanics experiment".
Mathematics: there are a few models of increasing precision which could all be called "quantum mechanics":
Ciro Santilli feels that the largest technological revolutions since the 1950's have been quantum related, and will continue to be for a while, from deeper understanding of chemistry and materials to quantum computing, understanding and controlling quantum systems is where the most interesting frontier of technology lies.
Silicon Photonics: The Next Silicon Revolution? by Asianometry (2022)
Source. - youtu.be/29aTqLvRia8?t=714 GlobalFoundries seems to be one of the leaders at the time. E.g. quantum computing company PsiQuantum uses them. Part of this was from acquiring IBM's microelectronics division in 2014.
Running Neural Networks on Meshes of Light by Asianometry (2022)
Source. - youtu.be/t0yj4hBDUsc?t=440 block diagram
- youtu.be/t0yj4hBDUsc?t=456 Lightmatter lightmatter.co/ seems to be using an in-silicon Mach-Zehnder interferometer to do analog matrix multiplication with light. It is an actual analog computer element!
So that he can work full time on OurBigBook.com and revolutionize advanced university-level science, technology, engineering, and mathematics eduction for all ages.
Donating to Ciro is the most effective donation per dollar that you can make to:
- improve hardcore university-level STEM education for all ages
- help make every child into the next Nobel Prize/Fields Medal/deep tech unicorn co-founder
Ciro's goal in life is to help kids as young as possible to reach, and the push, the frontiers of natural sciences human knowledge, linking it to applications that might be the the next big thing as early as possible. Because nothing is more motivating to students than that feeling of:rather than repeating the same crap that everyone is already learning.
Hey, I can actually do something in this area that has never been done before!
To do this, Ciro wants to work in parallel both on:
- the multi-user website e-learning platform of OurBigBook.com
- creating amazing teaching content that motivates that platform, and that deeply interests Ciro, notably quantum mechanics and its related applications:
- quantum computing
- molecular biology
- condensed matter physics and chemistry
- slightly more theoretical stuff in somewhat related fields of:
- continue to dump his brain/research in areas Ciro has expertise in: software engineering and open source software
Ciro believes that this rare combination of both:produces a virtuous circle, because Ciro:
- proven passion and capability to learn and teach science, technology, engineering, and mathematics subjects
- proven programming skills, including web development
- wants to learn and teach, so he starts to create content
- then he notices the teaching tools are crap
- and since he has the ability to actually improve them, he does
As explained at OurBigBook.com and high flying bird scientist, Ciro is most excited to make contributions at the "missing middle level of specialization" that lies around later undergrad and lower grad education:But on that middle sweet spot, Ciro believes that something can be done, in such as way that delivers:in a way that is:
- at lower undergrad level, there is already a lot of free material out there to learn stuff
- at upper graduate level and beyond, too few people know about each specific subject, that it becomes hard to factor things out
- beauty
- power
- in your face, without requiring you to study for a year
- but also giving enough precision to allow you to truly appreciate the beauty of the subjectCiro's programming skills can also be used to create educational, or actually more production-like, simulations and illustrations.
Ciro believes that today's society just keep saying over and over: "STEM is good", "STEM is good", "STEM is good" as a religious mantra, but fails miserably at providing free learning material and interaction opportunities for people to actually learn it at a deep enough level to truly appreciate why "STEM is good". This is what he wants to fix.
The following quote is ripped from Gwern Branwen's Patreon page, and it perfectly synthesizes how Ciro feels as well:
Quote 1.
Omar Khayyam's chill out quote
. Omar Khayyam also came to the Vizier... but not to ask for title or office. 'The greatest boon you can confer on me,' he said, 'is to let me live in a corner under the shadow of your fortune, to spread wide the advantages of Science, and pray for your long life and prosperity.'
In addition to all of this, financial support also helps Ciro continue his general community support activities:
- writing and updating his amazing Stack Overflow answers: Section "Ciro Santilli's Stack Overflow contributions"
- saving the world from the CCP: Section "Ciro Santilli's campaign for freedom of speech in China"
Communicating at a distance, from Greek "tele" for distance!
A very cool thing about telecommunication is, besides how incredibly fast it advanced (in this sense it is no cooler than integrated circuit development), how much physics and information theory is involved in it. Applications of telecommunication implementation spill over to other fields, e.g. some proposed quantum computing approaches are remarkably related to telecommunication technology, e.g. microwaves and silicon photonics.
This understanding made Ciro Santilli wish he had opted for telecommunication engineering when he was back in school in Brazil. For some incomprehensible reason, telecommunications was the least competitive specialization in the electric engineering department at the time, behind even power electronics. This goes to show both how completely unrelated to reality university is, and how completely outdated Brazil is/was. Sad stuff.
We don't need to understand a super generalized version of tensor products to know what they mean in basic quantum computing!
Intuitively, taking a tensor product of two qubits simply means putting them together on the same quantum system/computer.
The tensor product is called a "product" because it distributes over addition.
E.g. consider:
Intuitively, in this operation we just put a Hadamard gate qubit together with a second pure qubit.
And the outcome still has the second qubit as always 0, because we haven't made them interact.
The quantum state is called a separable state, because it can be written as a single product of two different qubits. We have simply brought two qubits together, without making them interact.
If we then add a CNOT gate to make a Bell state:we can now see that the Bell state is non-separable: we've made the two qubits interact, and there is no way to write this state with a single tensor product. The qubits are fundamentally entangled.
Discrete quantum system model that can model both spin in the Stern-Gerlach experiment or photon polarization in polarizer.
Also known in quantum computing as a qubit :-)
At first I had intended to create a lot more content for the world class university located where I lived, but I ended up not doing that and just improving the project tech instead.
There are a few reasons for this, good or bad:
- as a tech nerd, my natural tendency is to first sit down by myself and code to solve big general problems rather than go out and try to solve specific people's specific problems to obtain money and users
- at one point I got the feeling that helping students with a bunch of small courses might be useful, and that instead I might get more impact by instead by focusing on creating content for a next big thing area such as: because many of the courses are fundamentally useless by design due to misalignment between university and reality.I'm still not sure what to do about that, but I do think I'll try to do a bit of course solving at least and see how it goes.One thing I've learned first hand through Ciro Santilli's Stack Overflow contributions and Linux Kernel Module Cheat is that the barrier to make money from a useful open source learning project that benefits a large number of people a little bit is huge, perhaps infinite, and that it might be better to instead focus more intensely on fewer users. This insight pushes me more towards going for solving local courses.Another consideration that supports going for courses is that being close to students is perhaps my only unfair advantage. There is likely no one else in the world in the same position that I'm at, with some "free time" to chill with undergrads and help them with 100% of my undivided attention and passion.A point that pulls me towards the big tutorials however is that my time is almost up, and focusing on them would increase the chances that I will be work in those fields afterwards. This feeling may go against the best interests of the project, but it is perhaps an inevitable self preservation consideration unless someone decides to free me from that forever with the 2M :-)
- the entry barrier to help students of a top university is rather high. The students are already extremely busy and pressured (this is pe), and if it is in the slightest hard to explain their problems to you because you are not fluent enough in their subject, they will find a faster way to obtain the knowledge and never come to you.
- I also did a bit of procrastinating with a few quick few exploration into cute programming projects. Nothing too crazy long however, just the usual. It's in my nature to have broad interests, and perhaps only such a person can make a OurBigBook.com. I'm not a fast worker. But I never stop. Once something is in my "this must be done or learnt list", I just keep coming back to it again and again until it happens.
The downsides of going for tech first are severe:There are however counterpoints to these as for anything else:
- you risk being misaligned with what users want and spend enormous amounts of time on useless features
- it is also rather demotivating that you are working hard on a really cool feature but you know that there are no users yet so no one will benefit from it, and that this feature alone is not enough to attract the users anyways
- I'm a user and I'm always improving it for myself. If there are other people like me out there, they will love it. If there aren't, perhaps I'll never be able to do anything that caters for them well enough anyways.
- as the two users made me understand, once someone touches your thing, they expect it to be perfect, and their standards are extremely high. This is understandable in part given the large number of note taking apps in existence, and notably WYSIWYG ones. As such, there is some rationale for improving tech.
This is a summary of the status of the OurBigBook Project, focusing notably on the past 9 months that I've been able to devote fully to it starting June 2024 notably due to the anonymous 1000 Monero donation and other supporters.
I have 3 months left and after unless some crazy person gives more money, I'll go back to some generic programming job that could be done by many other people so that my wife won't kill me. Hopefully I'll find something in quantum computing or AGI research this time that is not too boring, but we'll see.
I should also note that I have raised my requirement for a second year full time from 100k USD to 200k USD, such that there are about only 144k USD missing as of writing, a bargain. See also Section "Sponsor Ciro Santilli's work on OurBigBook.com". I have also set a 2M USD retirement goal in case someone wants to free me to lurk after university students for the rest of my life. Creepy.
The reason for this increase is partly because I'm jealous watching my university peers getting relatively richer and richer than me. More seriously though, as I'm likely going to be looking for a job soon, I don't want to scare employers off too much thinking that it is likely that I'll be leaving in a few months too easily. Plus inflation and the natural lack of security that such endeavour brings.
Poor countries don't have a lot of money.
Therefore, you have to pick a few key the next big thing deep tech bets, and invest on those enough.
These have to be few, because your country is poor, and so you can't invest on everything.
Therefore, the bets have to be well selected, because it is useless to make several insufficient investments: you have to pick a few ones, and put enough time and money into each one of them for them to stand any chance. These bets should be made and reevaluated on 5/10 year horizons.
The key things that you have to select are:
- which poor students you will bet on educating. Since you can't give amazing education to everyone, you have to select the most promising poor students somehow, and give those free amazing learning conditions: free gifted education
- which ares to focus on. Ciro believes that molecular biology technologies and quantum computing would be good bets. Focusing on the previous next big things, e.g. classic computers, is always a losing bet on average
And then you only tax those companies heavily when the start to bring in real money. These are startups remember! You only need 5 unicorns a year to call it a success. And countries should not be greedy and invest through equity, but rather recoup their investment through taxation alone.
Ciro's second removed uncle, who was a physicist at the University of Campinas, one of the best universities in the country, told him an anecdote. He had moved from fusion energy research to solar cell research. At some point, there was a research lab that needed 10 million to buy a machinery critical for their experiment. They asked and asked, and finally the government gave them only 2 million. So in the end they spent those 2 million in random ways, but of course did not achieve their research goal and no money came out of it.
He also explained how as a result of the insufficient investments, he felt clearly that some of the semiconductor production facilities related to solar power he saw simply were not able to control the production process adequately to produce consistent silicon. As a result, everything failed sooner or later as people found more and more bugs that they did not have the time to solve.
Another key investment is enticing back experienced exchange-students who have learnt new techniques to be heads of laboratory/founders to back in your country.
A fantastic initiative from Brazil for example is BRASA, which aims to put together Brazilian exchange students to make a difference back in Brazil.
Do not try to forbid external companies from selling in your country. Instead, fund your own companies to be able to fight the external market off. And if they can't, let them die and pick a different bet. Video "How Taiwan Created TSMC by Asianometry (2020)" has a good mention. Protectionism is something that Brazil notably tried to do, and look at what it led, not a single international success.
This is basically how quantum computing was first theorized by Richard Feynman: quantum computers as experiments that are hard to predict outcomes.
TODO answer that: quantumcomputing.stackexchange.com/questions/5005/why-it-is-hard-to-simulate-a-quantum-device-by-a-classical-devices. A good answer would be with a more physical example of quantum entanglement, e.g. on a photonic quantum computer.