Quantum winter is coming
backreaction.blogspot.com
backreaction.blogspot.com
Oh. I thought things were further along than that.
I've worked with quantum computing researchers before (I mean in the same building not doing the work), it's interesting work, but we're still at the stage where a focused background in physics doing research in is the prereq, not skills with quantum algorithms and their implementations. "Programming quantum computers" is still physics not software engineering.
Dumb question, is 21 harder than 4?
However. This paragraph straight up displays a fundamental lack of understanding by Hossenfelder:
> Last time I looked, no one had any idea how to do a weather forecast on a quantum computer. It’s not just that no one has done it, no one knows if it’s even possible, because weather is a non-linear system whereas quantum mechanics is a linear theory.
Unitary evolution generated by the Schrödinger equation is a linear map on _probability amplitudes_, just like how classical (probabilistic) computing performs linear operations on _probability distributions_. The commonly used quantum circuit model is a superset of classical logic gates and can accomplish anything a probabilistic classical computer can, so if anything is possible in a classical computing scheme, it's also possible in the quantum circuit scheme.
I don't have much sympathy for her since this is not the first time Hossenfelder has displayed a lack of understanding, recently she has published a paper criticizing another one [1], now replaced with a much shorter text due to being told [2] by the authors of the original paper.
Yeah I get it, it's dumb when the president of BofA is talking about how QC is "the next big thing", I know it's not coming Soon^TM, but saying "we will never have a quantum computer because the current ones suck" has the same energy as "the world doesn't need more than 5 computers" imo.
To predict weather on a computer, we need to run large CFD simulations. When we do this on a classical computer, this involves a discretization of a system of PDEs with millions or billions of degrees of freedom, requiring 4 or 8 bits per floating point number. It may be possible to do the same CFD simulations on a quantum computer, but this is several constrained by the small number of qubits currently available on quantum computers. And clearly, even if you could run the same algorithm, presumably the point of using a quantum computer would be reap the "quantum advantage" in order to do something algorithmically superior to what's possible on a classical computer.
I think this is a pretty small point to get hung up on. The rest of her article is perfectly reasonable.
Indeed. And it's possible SH is confused by this, since she had another video about quantum chaos in asteroids where similar observation applied and she didn't address that. However...
> There's no question about how you would implement the said logic in a quantum computer - you can just do what the classical implementation does. Yeah we don't have nowhere enough qubits and it would be a gross waste of resources, but we _do_ know how to do it. Saying they don't know how to do it is a false statement at best.
Here you're being a little uncharitable. Indeed theoretically one could make the quantum computer simulate the classical computer with the non-linear weather algorithm. But the interesting point Mrs. Hossenfelder may be making here is there is no known way to make quantum computers calculate/simulate the weather evolution in a "quantum computer way", that is, not simulating discrete-state classical computer which would be wasteful and most probably not with advantage, but realizing the differential equation evolution in analog mode, using the quantum superposition capabilities. That is not known to be possible. Quantum computer may be an analog computer (continuous evolution of state), but it is not clear how to use it to integrate interesting sets of differential equations like weather models.
Bytes, not bits.
I googled around and found this research though, which does propose using a nonlinear quantum system: https://arxiv.org/abs/2210.17460. It doesn't really claim the issue is solved.
The above isn't the only place that betrays her lack of understanding, though.
For instance, she confidently writes "Ion traps are used for example by IonQ and Honeywell. They must “only” be cooled to a few Kelvin above absolute zero," but this is just wrong; trapped-ion qubits do not, a priori, require cryogenic cooling. Yes, lowering the temperature can be useful for incidental reasons, as it improves the vacuum quality and reduces some technical excess noise sources, but this is simply an engineering choice. Many of the high-profile results in trapped-ion quantum information processing were in fact achieved in room-temperature systems. And even if one does opt for cryogenic cooling, the ~tens of Kelvin regime of interest here is incomparably easier to reach than the tens of milli-Kelvin required for superconducting qubits and other solid-state spin platforms (where those elaborate dilution refrigerator "chandeliers" are actually required to keep the qubits intact). In fact, in ratiometric terms, the temperatures of interest are actually closer to room temperature than to that millikelvin regime!
Like many physicists, I'd naturally be inclined to agree with Sabine Hossenfelder as far as her distaste of marketing hype is concerned, but in making authoritative-sounding statements without having the knowledge to back them up, and misrepresenting what one would hope she knows are the actual scientific facts in the service of a punchy script, she is hardly doing any better than those private-sector hype evangelists she ridicules. Beware of Gell-Mann Amnesia…
From the outside QC looks looks less like traditional computing (as you're suggesting) and more like cold fusion. There are plenty of hopeful stories and investments but it's hard to tell if it'll ever happen in a meaningful way.
I'm guessing you go to UofT or Waterloo.
If you have seen the kind of equipment required to perform these experiments, it's absolutely unimaginable that these concepts could be miniaturized enough that someone would be able to put them in a desktop size box, and to do so usefully and safely within a timeline that is competitive with the advancement of microelectronics.
https://dyson-sphere-program.fandom.com/wiki/Quantum_Chip
QUANTUM CHIPS Running low? Never Again! | Dyson Sphere Program Master Class
Even worse, there is no reason to think that there will ever be - as far as we know, QCs only show an exponential advantage on problems with very very specific structures, while the whole problem of NP-hard problems is that they have no structure in general.
It is suspected to be NP but not even NP-complete, nevermind NP-hard. It is suspected not to be in P, but that is not yet proven.
Some problems in BQP are suspected to be in NP (integer factorization, for which the best known classical algorithm is sub-exponential, but we have a polynomial time quantum algorithm), but there is no known NP-complete problem for which a quantum algorithm is known, or even suspected to exist.
Edit - some links:
[0] https://www.scottaaronson.com/papers/npcomplete.pdf - chapter 4
> If we interpret the space of 2n possible assignments to a Boolean formula φ as a “database,” and the satisfying assignments of φ as “marked items,” then Bennett et al.’s result says that any quantum algorithm needs at least ∼n/2 steps to find a satisfying assignment of φ with high probability, unless the algorithm exploits the structure of φ in a nontrivial way. In other words, there is no “brute-force” quantum algorithm to solve NP-complete problems in polynomial time, just as there is no brute-force classical algorithm.
[1] https://youtu.be/0jrybODBUpA?t=30m28s "P versus NP"
First of all, while not proven, it is considered most likely that integer factorization is not in P, so potentially we already know of 1 NP-P problem which can have an exponential speed-up from a QC (Shor's algorithm).
Secondly, there is one non-exponential speedup that can potentially apply to even NP-complete problems - using Grover's algorithm to find an element in an unordered list with complexity O(sqrt(n)) instead of the classical O(n).
Ironically, if that were to happen, it would probably be a much more important boon for humanity than if we successfully build a working QC.
It could very well be that they are both great approximations and it’s actually the underlying information structure that shifts depending on scale. This doesn’t seem likely to us perhaps, but only because of existing intuition which we know is likely wrong at some level.
> It could very well be that they are both great approximations and it’s actually the underlying information structure that shifts depending on scale.
Right now, both QM and GR claim that they apply at any scale. If it turns out that the laws of physics change with scale, that means that both QM and GR are wrong, even though they may each be perfectly correct at the scale they have been seen to work so far.
the thing is I don't believe that either does make such a claim. I believe certain people have said that and the untrained masses may assume that's the case. But I don't think the scholarly proponents or intellectual founders of either system made such a claim (in fact Newton was religious and Einstein believed we were way off by his death.)
>that means that both QM and GR are wrong
How though? They are both right for their use case so are likely subsets of a greater theory.
Not only does the math apply at any scale, but no one has any idea how to add a scale parameter to prevent it from doing so, or what value that parameter should have. QM at least has the Measurement Postulate that could allow this to fit, but no scale is added.
Note that when I say "a scale parameter", I'm referring to something like the sqrt(1-v²/c²) of special relativity, but for "size", added to the Schrodinger equation and to Einstein's equations, that would mean they take the "scale" of the phenomenon into account. Without such a parameter, the equation says that it applies to a star as well as to a neutron. The only reason we don't apply them that way is that we have already tried and we know they give the wrong results.
Also, both GR and QM give the right results if applied at the scales of day to day life. You can use the Schrodinger equation and the Born postulate to compute where two trains traveling in opposite direction with some speed will meet, or you can use Einstein's field equations, and you'll get the same response within some small margin or error (with some reasonable assumptions, such as an almost flat spacetime in the area).
Furthermore, there are at least significant numbers of QM practitioners who do believe that QM applies at any scale - those who believe in the Many Worlds Interpretation, which states this very explicitly. On the GR side, the limitations of GR if applied at subatomic scales are well accepted and considered a flaw in the theory - which is why people hope to replace it with a theory of quantum gravity.
And Shor is based on quantum superior FFT if I recall correctly, which could have applications outside of discrete log.
Disclaimer: I’m not an expert on this stuff, I’m sure someone will correct me if I’m wrong because there are real pros on here.
Whether it will ever justify the investment is, of course, another question.
It would be better just exposing this as a library of functions and then hooking it up to a cloud service to solve. Which Amazon, Microsoft and IBM have. Microsoft and IBM are using their own hardware And Amazon is reselling other providers. [2,3,4,5]
Researching post quantum cryptography algorithms are already on their way [7] but most likely feasible quantum computers are 80 years away when I was reading a great deal of quantum algorithm papers as a class and I asked the professor how long it would take.
The interesting strategy if you were to hack a organization which has encrypted backups would be to exfiltrate the backups and then wait for a quantum computer that could break it which is why post quantum encryption needs to be researched but the algorithms involved are still in their early stages.
Post [1] https://en.wikipedia.org/wiki/Quantum_algorithm
[2] https://quantumai.google/hardware [3] https://azure.microsoft.com/en-us/solutions/quantum-computin... [4] https://aws.amazon.com/braket/ [5] https://www.ibm.com/quantum [6] https://en.wikipedia.org/wiki/Post-quantum_cryptography?wpro... [7] https://pqcrypto.org/conferences.html
AI/ML easily demonstrates superiority - from playing games, classification, translation, generative art etc.
QC is stuck at no practical use with claims that it'll stay this way for decades, some claiming forever as there may be physical walls that can't be broken.
I do not think that there will be a QC project done on the same basis for 40 years.
Still, my point still stands if we limit it to electronic computers, I think.
QC are big because the energy levels are so high that they require complex equipment to focus energy and remove heat, akin to the tyranny of the rocket equation.
> "look it responds sort of like a qubit!"
And behave like them too ;)
The value is in a quantum computer existing at all, not it's availablity to consumers.
Might see a similar bout of miniaturization if we can come up with a good defensive/offensive application for putting quantum computers in orbit.
Actual cooling (stuff that pumps liquid nitrogen and helium) was external to all this.
The UHV equipment is pretty intense too, fwiw.
Of course (back to skepticism) it's not like no one thought to try using quantum mechanics and history is starting over at the 60's. Modern QC research comes after decades of ideas failing, throughout more recent times where we have been much more technologically knowledgeable versus the early days of computing.
In a previous life I worked with NMR machines, the ones with superconducting magnets cooled by liquid helium which is itself cooled by liquid nitrogen.
I would dispute "minimal amount of harm", part of the our training involved what to do if the magnet quenches, I recall "run for the exit before you suffocate" was basically the SOP...
Anyway, they were loads of fun to work with, I won't ever forget that time I nearly had my house keys snatched out of my hand by one, but back then (25 years ago) they occupied entire rooms. AFAIK they still do.
OT but I had an MRI a couple of weeks ago, and forgot to take off my gold wedding band. I could distinctly feel the magnetic field pulsing in my ring as the scan started. After a brief moment of sheer panic I realised it wasn't a problem ... and as I lay there I was idly wondering about just how much gold was in my ring :)
Yes, at current scale it would be very hazardous, but at miniature scale a gram of liquid helium could do how much damage considering it would have to make its way through the internals of a machine to contact skin?
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Same as airplanes, basically the same since 1960, like we fly on Super Fortresses with the bomb bays replaced with cargo holds...like different dispenser, and the plexiglass fishbowl artillery in the front done differently. I would love to be in one of those fishbowls, like all exposed flying at the horizon like panoramic view. So suicidal, like all aviation.
Like not getting shot at like in Catch-22 though. Hopefully.
Nit picking, the B-29 Superfortress was a propeller driven aircraft [1]. Modern commercial planes generally have jet engines. Jet engines represent a leap forwards in aerospace engineering.
[1] https://en.m.wikipedia.org/wiki/Boeing_B-29_Superfortress
Happily, there are many fields beside computing where quantum technology comes into play - better and cheap chip fabrication, semiconductor lasers and diodes, all kinds of materials science research, and of course, solar energy conversion systems modeled on the photosynthetic apparatus:
https://sci-hub.se/10.1038/nature22012
Romero, et al. (2017). Quantum design of photosynthesis for bio-inspired solar-energy conversion. Nature
As far as what today's working scientists will pursue, the silly popular notion that researchers are free to explore whatever they find exciting and interesting is mostly nonsense; successful researchers in the modern science system are as keen as hounds on the scent for new funding disbursements from the major federal agencies (and some private sponsors). If the money dries up, they turn their attention to other things, except perhaps for a few back-burner projects handed off to some hopelessly naive yet charmingly enthusiastic grad student.
Gibson got some stuff wrong, but it’s borderline scary how much he got right. Book is like 43 years old or something.
it is well known, but not to laypeople, that a quantum computer is efficiently (quadratic overhead) simulable if it only operates on the eigenstates of the generalized Pauli matrices with the so called Clifford operators. This is a really fancy way of saying that this group action is not dense in the unitary operators, which is itself a fancy way of saying that it behaves like rolling a die, instead of like rolling a ball.
In order to achieve density in the unitaries it suffices to construct a single state that is not one of these magic states (their language, not mine), to a sufficiently high level of purity.
The much touted paper which claims to do this only succeeds in showing that the problem is equivalent to some other problem which we also do not know how to solve, (creating many, worse separable copies of this state) and there is no particular reason to believe that it can be. Moreover given what it would be able to do, it seems much more likely to me that there is a proof, waiting to be discovered, that there is a fundamental obstruction to harvesting such a state without at least waiting as long as you would have to wait to do your computation the old fashioned way.
In my view, making the noisy physical magic states is the easy part of the distillation process. You reset a qubit, then rotate it 90° around the Y axis, then 45° around the Z axis. That's the magic state. Note that the tolerance on those rotations is forgiving: getting them to within 10°, 95% of the time, is sufficient. All the error correcting code stuff that follows has fidelity requirements an order of magnitude stricter.
As you note, there'd need to be some unforeseen obstacle for state prep to be the showstopper. Given how apparently easy it is to make these states, I think any obstacle like that would basically have to falsify quantum mechanics as we know it. It would be like finding out that light can't be diagonally polarized.
This is the paper, https://arxiv.org/abs/quant-ph/0403025, and it is well understood by the paper that the independence of the noisy magic states is necessary for the distillation process to proceed. Note that the probability of having some entanglement between your partial states goes up rather dramatically with the number of them that you have, and not obviously in a way that you can do anything about.
> Note that the probability of having some entanglement between your partial states goes up rather dramatically with the number of them that you have
Entanglement is not binary, it is continuous. If you start with states like CPHASE(5°)|TT>, a few rounds of distillation will have turned them into states like CPHASE(0.0000000000000001°)|TT>. Sure the output states are "still entangled", but the amount of entanglement is so negligible that you don't have to care. Such small distortions won't prevent trillion step computations from working.
- We're collectively less smart/rational than we think
- People with money/power are not much better than average
- We tend to be very gullible when we don't understand the underlying principles
The same human flaws can be seen in UFO/Conspiracy theories and to some extent in the crypto/NFT scene.
A lot of this is amplified to several orders of magnitude by incompetent journalism.
I think it's way worse than that, the only things that people with money/power are better at ... is getting / hanging on to money and/or power.
They're not better at anything apart from that, by any objective measure.
Seems to me that most all of the quantum computing community is trying to be in the right place when they can start cracking current encryption standards at a commercial scale. At that moment, anyone with a functional quantum computer will drown in money. Then a few weeks later new quantum-resistant algorithms will appear and the gold rush will end. All the other quantum projects seem like attempts to keep ones foot in the market while waiting for that day.
So with off-the-shelf electronics an analog computer can compute 1000 times faster with 1000 times better precision than if it were mechanical. Until the 01960s they used vacuum tubes and so used more power and were less reliable; since then electronics have used less power and been more reliable.
Today we still use plenty of analog computation, but it's pushed to the margins. Every sound card has an antialiasing analog filter on its front end before switching to the digital domain. Even software-defined radios still use analog electronics to upconvert and downconvert signals between baseband or IF and the RF. Your Wi-Fi card can't sample that 2.4 GHz signal at its 4.8 Gsps Nyquist rate; doing that is not impossible but still requires high-end digital electronics. Submillimeter-wave communication is very much dependent on precise analog signal processing to modulate your desired signal into the hundreds of GHz range.
("Precise" in this case doesn't mean with linearity errors as low as 1%.)
However, an algorithm being promising doesn't mean it works. Do you know how well the development of these other techniques is progressing?
I'm trying to imagine and am totally stumped.
An opamp performs multiplication faster than a digital computer (speed of light vs a few cycles). It's not super useful on its own, but it does fit the criteria.
In Veritasium's video 2/2 on analog computers [0] they show some startup products near the end.
Analog multiplier ICs are available.[1] They're not common, and they cost $10-$20 each. Error is about 2% worst case for that one. There are several clever tricks used to multiply. See "Gilbert Cell" and "quarter square multiplier".
[1] https://www.digikey.com/en/htmldatasheets/production/1031484...
[1]: https://en.wikipedia.org/wiki/Differential_(mechanical_devic...
They are not very different from a computation inside an injection controller of an ICE, with its results consumed within microseconds, as motions of injection valves. They key difference is the intermediate use of an electronic computer, an MCU, instead of a purely mechanical and pretty inflexible device, the camshaft.
Certainly we could replace a swashplate with some electric or hydraulic actuators driven by an MCU if we needed to compute something more complex than what a swashplate currently computes, much as we did with the camshaft. This is not very probable though, because a new system should also work unpowered to allow auto-rotation, to say nothing of higher reliability requirements than a system for a car.
In the north-pointing chariot or the Antikythera mechanism, the differential performed a computational function, with its action of transmitting power quite peripheral to that; in your car's rear end, it performs a power-transmission function, with its action of computation quite peripheral to that.
The same situation holds with transistors. You can use a 2N7000 to toggle a light or control a relay or a motor, or you can use it for (digital or analog) computation.
If you're using it in an NMOS NOT gate or the input stage of an op-amp, you're using it for computation, and so you wish it were smaller; it would work better if it were smaller because then it wouldn't need so much energy to turn it on or off. (For analog computation, you only wish it were smaller up to a point, because at extremely small sizes that makes it more sensitive to noise, but you wish it were really a lot smaller than a 2N7000.) A 2N5457 is generally better for an amplifier input stage, and the no-longer-available discrete signal MOSFETs are probably better for NMOS NOT gates. The N-MOSFETs integrated into a chip are enormously better at computation than a 2N7000.
By the same token, though, a 2N5457 or signal MOSFET is much worse than a 2N7000 at power transmission. If you're using it to PWM a motor, you wish it were larger; it would work better if it were larger because then it would be at less risk of overheating, be more efficient at a given current level, and be able to control a bigger motor. An IRF630 is a better power MOSFET than a 2N7000; an IRF540N is better still. But they're enormously worse at computation than a 2N7000.
Helicopter swashplates and differentials are very much on the power-transmission end of the spectrum, not the computation end, even though they cannot avoid doing computation as part of their job.
You might be able to build a fluid device to test a property faster than you can simulate the fluid dynamics in full detail. Perhaps not on the first iteration, but iterating small changes to get a desired result could certainly be faster than simulating it, for simple systems.
https://www.techspot.com/trivia/97-1930s-which-countries-bui...
I like the COMPAQ branding added to it!
For mechanical naval fire control computers the cutoff frequency is on the order of 100 Hz and the error is on the order of 1%. You won't learn anything interesting by sampling them every microsecond that you wouldn't learn by sampling them every millisecond.
One example, if I need something that when two switches are triggered will turn on a light bulb (basically an AND gate) it's obviously faster doing that with an analog (mechanical) device, that is the two switches wired in series, than acquiring the signal with a microcontroller and outputting a signal to turn on the light bulb.
Thinking about the industrial world, there are cases where you have constraints about speed and real time that make sense to do signal processing with analog components rather than digital ones. And that was always the case before computers where invented, by the way (missile guidance systems were purely analog, as one example, you can do a lot of stuff!)
This forum gets more and more detached from reality every day.
But I expect that in 5-10 years, most security systems designed by competent professionals (up-to-date OS security services, TLS servers, SSH servers, VPN, firmware update systems etc) will have post-quantum crypto enabled by default. And I expect it will take longer than that to build a QC that can break classical crypto.
More likely it will play out like the SHA-1 break: all professional security engineers should have switched off SHA-1 (at least for unkeyed hashing) years before any collision was found, and users who apply security patches should therefore by mostly up to date, but I'm sure some are still using the older crypto.
“NSA intends that all NSS will be quantum-resistant by 2035, in accordance with the goal espoused in NSM-10.”
Source: https://media.defense.gov/2022/Sep/07/2003071836/-1/-1/0/CSI...
Anything that is private today is private for a reason. That reason doesn't automatically disappear over time.
I don't think you've been paying attention to the news.
Also the US government isn't a great example. JFK was assassinated in 1963 and all records surrounding that still haven't been released.
The idea that people don't care about secrets across the span of decades is utterly wrong.
And the US intelligence community is the greatest data hoarder on the planet, rivaled only perhaps by the combined forces of facebook/google.
At the same time, from history it seems almost certain that, if indeed a CRQC ever gets built, a significant number of users will not have secure PQC rolled out on day 0.
(1) mathematically
(2) as a finite list of things quantum computers can and cannot do
and most people are not going to understand it mathematically, least of all money people who have to watch and evaluate 10 powerpoint pitches in a day or whatever. Without the math you cannot possibly explain how superposition and entanglement work, and even that explanation requires your audience already understand how classical computers work. So you are often reduced to saying "Here is what we think quantum computers will be able to do. The timeline for accomplishing this is at least a decade out. Here are some other things that people have said quantum computers can do which they definitely will not be able to do." But then you're purely relying on your audience believing you based on your credentials rather than following their own reasoning from a place of understanding. Someone else can come in with different credentials and say different things, motivated by money or simple ignorance mixed with hope, and now your audience is playing the credential evaluation game rather than the quantum computing capability evaluation game. Mix in low interest rates and a few people who have learned what to say to get attention, and you get the current state of things.
There is a quiet core of real quantum computing research happening, surrounded by a moat of noise and hype that is required to interface with investors and the public. My sincere hope is that this quiet core accomplishes real advances before the music stops.
Sprinkle some hand waiving around how you can "average them all together" or have them "check the answer with each other" before querying them.
- Here are matrices, this is how we multiply them and get a resulting vector.
- Here are special kinds of matrices SU(N) which leave |\Psi|^2 invariant, and here is an example for multiplying.
- Quantum computers are just SU(N) matrix multiplication accelerators.
At this point, no hype can survive and neither funding.
We know that an ideal quantum computer provides an exponential speed-up in commercially relevant applications. There is a significant amount of smoke and mirrors in the quantum space.
Unless there is some unknown fundamental reason why we cannot realize a good-enough quantum computer with sufficient knowledge and engineering, governments and business are well-advised to invest into R&D of quantum computers.
Which is to say, just because something is possible in theory doesn't mean we have a clear idea how to get there, and if we're waiting on research breakthroughs, more money is unlikely to significantly speed up the process.
Do we know that though?
From the abstract:
> I survey, for a general scientific audience, three decades of research into which sorts of problems admit exponential speedups via quantum computers -- from the classics (like the algorithms of Simon and Shor), to the breakthrough of Yamakawa and Zhandry from April 2022. [...] I make some skeptical remarks about widely-repeated claims of exponential quantum speedups for practical machine learning and optimization problems. Through many examples, I try to convey the "law of conservation of weirdness," according to which every problem admitting an exponential quantum speedup must have some unusual property to allow the amplitude to be concentrated on the unknown right answer(s).
I am very confused at how so many people are absolutely convinced that quantum computing is known to do, or already does, all sorts of things it is not known to do. There's a sister comment here saying exponentially superior quantum computers are available in AWS!
It's like an urban legend that circulates among software engineers.
Integer factorization and quantum simulation would be two examples of commercially highly relevant applications where an exponential speed-up applies.
"Quantum simulation" is too vague to speak to applicability in any domain. There is no proven or empirically demonstrated exponential speedup on any simulation problem with known commercial applications.
https://arxiv.org/pdf/2011.06571.pdf
It does seems too good to be true.
The two caveats are
1. Only works for "mildly" nonlinear equations
2. Results are in quantum world and have to be translated back into normal deterministic world results, and this hasn't been figured out yet
Currently the choice of replacements is taking its time e.g. https://www.nist.gov/news-events/news/2022/07/nist-announces... but if tonight we'd find out that people do have sufficiently powerful quantum computers, we'd just start using one of the candidates in a jiffy)
As a parallel, on a purely theoretical level, we know how to construct a warp drive as well[1].
Solving a theoretical equation is one thing, replicating the prerequisite conditions experimentally is another entirely. Theoretically you can balance a perfect sphere upon another perfect sphere. In practice, you can't because setting up such an arrangement is practically impossible.
Yes, but we don't actually know that we can build such a device.
In practice you're right, though, usually new funding in one place gets taken from somewhere else. But if the world really wanted scientific progress we could afford to fund some more speculative projects.
Yes, but it is not possible to assert that both are true at the same time with full certainty.
i do not know enough about quantum computing to judge whether this article is accurate or not, but it certainly is well written and entertaining
Regardless, memorizing a few facts won't help with reasoning about "is quantum computing even possible".
* 2017 - the year of 3D TV.
* 2019 - the year of VR
* 2021 - the year of the Metaverse.
All duds, or no more than niche products. Related duds include quantum computing, fusion power, and self-driving cars. (There are self-driving cars that work, from Waymo and Cruise, but they're a long way from being cost-effective.)
On the other hand, there's lots of work to be done deploying the stuff that works. Solar. Wind. Batteries. Electric cars. Desalination plants. Automated manufacturing. Electrical transmission infrastructure to get power to where it's needed. All are profitable. None has either huge margins or monopolization potential. This discourages the Silicon Valley funding model.
Honestly, this should be in all caps, and heard by everyone. I am regularly disturbed by this fact.
I am lead/senior software engineer working with frontend development. I could easily do embedded software engineering, automation or any other more pressing field for humanity. But economics are simply not there to justify. I would be getting a %60 pay-cut if I chose to work on anything that actually matters.
How is 2019 the year of VR? Look at Google trends for "Oculus Quest". You can see that it continues to be growing. 2019 was not some kind of peak.
ETA..in case anyone is interested, the author of the article is a theoretical physicist, which lends more credibility to the article.
I'm a bit bitter that physics handed us this magical tool, and the best we managed so far is using it to invalidate decades of security research.
I don't think that is true (or maybe I'm underestimating how many qbits other uses of QCs take). Estimates are still in the many millions: https://cacm.acm.org/news/237303-how-quantum-computer-could-...
Is that true? Hardware from the likes of IBM and IonQ has already gone from < 10 to >= 20 “algorithmic qubits” [1] in the space of a few year.
And compared to other uses (quantum AI anyone?), it's surprisingly compact.
The only true benchmark is a factorisation of numbers. Number 21 has been factorised with nudging. Let's wait for a number 45 in the coming next decade.
I think this is a mischaracterization. Moore's law is the result of many individual multiplicative advances that stack in each other (many of them allowing further miniaturization, but alternatively can also go into the direction of larger chips: chiplets, 450mm wafers, wafer-scale chips).
There isn't a reason this stacking couldn't also be possible for Quantum Computers. Indeed, the number of qubits seems to grow exponentially [1]: 1 -> 2 -> 5 -> 17 -> 49 -> 76 -> 127 -> 216 -> thousands. If anything, the iterative miniaturization of classic circuits made the first steps more approachable compared to quantum computers that had to start on the atomic level.
However, the existence of more and more stackable advances is indeed no law of nature that some authors seem to assume [2].
[1] https://en.wikipedia.org/wiki/List_of_quantum_processors [2] https://en.wikipedia.org/wiki/Accelerating_change#Kurzweil.2...
It's a case for research money. I wouldn't invest in it but billionaires could spend their money worse.
I think this is arguable. To be sure, a lot of linear-system solving goes on in science, but you cannot conclude from this that it is the "single most important problem", it's just the one that we happen to know how to solve, and so that's where the compute power goes. It's like looking for your keys under the street light because it's easier to see rather than because it's where you lost your keys. Protein folding and the Navier-Stokes equation are arguably "more important", we just have no idea how to solve those problems.
Love it!
I laugh so hard on this. But it's true.
Either quantum computers pan out over the time investors stay interested or they don’t. It’s not unlike any other technology. As far as physicists feeling embarrassment, why should we? One, we’re largely incapable of feeling it, and two, we tried and that’s cool.
You have these VCs who have a very narrow view of how the future should be (chiefly being that which makes them richer) so you can only pitch so many technologies that fit that mold. To be blunt, find me a better technology that could potentially push the boundaries of human capability or understanding or whatever that VCs will invest in. Beats the hell out of VR. Is it cooler than shooting stuff into orbit? Seems on par to me.
Finally, again, dil fridges are a solved problem. Not only are they solved but there’s a ton of room for improvement. They’re very inefficient. You can just make bigger ones with more dil units.
That leaves the wiring, but you remove a lot of that with cryosilicon computers and multiplexers.
Hossenfelder describes a situation in which universities rent equipment from large companies, but fits this into a worldview in which quantum computing as an industry will not be commercially viable.
But isn't this exactly what happened with the internet and other new forms of large scale computation? Initially demand came largely from academia (or government via defense), companies competed on cost and usability. After a few years or decades of competition and scaling, the technology became so commercially useful that it's now ubiquitous. Why won't that happen with quantum computers, why would that be a bad thing, and why shouldn't academics want to be working on that?
Quantum computers on the other hand, don't appear to be good for anything now and in foreseeable future.
And this is why I keep coming back to Sabine Hossenfelder.
Note that I'm putting neuromorphic in quotes because it's mostly a marketing term, the resemblance between memirstors and neurons is at best symbolic.
I've been following Sabine for quite a while. She's really working on her snark.
This lady have no idea what she is talking about.
In electronics, in a linear system, you can decompose a complex waveform and analyze its response to each frequency discretely, and when you recompose them, you get a correct answer.
In a non-linear system, such as a mixer, no such analysis is possible, you have to consider all of the frequencies, and their levels at the same time.
Also consider that most algorithms are founded on a deterministic computational method.
"New Quantum Algorithms Finally Crack Nonlinear Equations. Two teams found different ways for quantum computers to process nonlinear systems by first disguising them as linear ones."
https://www.quantamagazine.org/new-quantum-algorithms-finall...
Quantum computing and quantum mechanics, not the same thing.
There was indeed an AI boom at the moment, just not Watson.
1. it all started with a technology trigger (much to the like of early AI development by Turing and McCarthy)
2. then we reach the Peak of Inflated Expectations (trying to solve real world hard problems like Travelling Salesman)
3. and swamp through the Trough of Disillusionment (Death of LISP machine and overall major AI projects halts)
4. until the Slope of Enlightenment (accidental discovery of using GPU to accelerate AI computation)
5. and finally reaching the Plateau of Productivity (developing Tensorflow, PyTorch, and the overall AI democratization though the use of DL and AutoML).
We have just barely in between the stage of Peak of Inflated Expectations and going to Trough of Disillusionment for Quantum Computing and very much likely to stay for a while. Don't you worry child, It's all part of the cycle.