Quantum computers ready to leap out of the lab in 2017
nature.com
nature.com
Shameless plug: I wrote a linear algebra book that discusses applications to quantum mechanics, including quantum computing. Check out an extended preview here: https://minireference.com/static/excerpts/noBSguide2LA_previ... Available electronically here https://gum.co/noBSLA and the print version is coming soon.
Edit: just to elaborate on this. I hear often from people "what math do i need to learn so i can do X?" And I just think that alot of the time people should just do X and this will force them to figure out the math as needed. Learning math from a book is brutally difficult! Just like learning a programming language, what is the best way to do that? You start with a project that you want to do. People who want to read the "pre-requisite book" often-times just seem to be deferring the real crunch that happens when you actually get down to the business of actually learning X.
Excellent flick. If you haven't seen it, I recommend giving it a watch.
greenaddress.it
N.B. My maturity level is normally only a half step above the average 12-year-old. I can play this game all day ;)
Understanding diagonalization (and more generally, spectral theory), scalar products, and norms would be a great start. Then you can pick any quantum information textbook.
It was a collaboration with Scott Aaronson ( http://scottaaronson.com/ ), so hopefully it's as accurate an intro to quantum computing as any webcomic could be.
What if we relaxed/paraphrased this as: For a biological system to be a mind the physical systems that underlie the mental substrate must have limited/circumscribed physical interactions that are not under the complete control of the mind.
Yes, linear algebra (and complex numbers) are necessary. Though, complex numbers are fun on its own, and linear algebra is crucial for other cool things, e.g. artificial neural networks.
ETA: reminds me of Tim Pope renaming Vim Foreplay to Vim Fireplace a few years ago. Apparently, that really helped him [1].
You're not the first to point out the limited recommendation potential. At the same time, I think the title is really what makes people stop and look in the first place. There are so many good math, physics, and LA textbooks out there, so it's important to stand out. I'm thinking I might release a PG13 version that cuts down on the edgy subjects, but I fear something will be lost in the process...
I've just completed a PhD in this area, and I've never heard this sentiment before. It seems quite unlike fusion research, which is famously always 50 years (?) away. Quantum computing research is proceeding at a rapid pace, and actually it is perhaps only 20 years old in total. Although Feynman considered something akin to a quantum computer in the 80's, it really wasn't until the 90's that quantum error correction became a thing. So it really is a very young field of research. The "old-timers", they are all about 40 years old now, unlike other areas of physics where the oldies really are very old!
But these guys transferred sideways to (aka. invented parts of) quantum computing.
I'd really like to work for one of the quantum computing companies (Rigetti, etc.) or research groups (QuAIL), but I'm not really sure how to proceed in applying to these places given that my background isn't exactly quantum computing research. Since you have experience in this area, would you have any tips on how to apply to one of these groups? My current line of thought is to just apply directly via their websites, and if I don't hear anything back, begin contributing to the relevant Github quantum computing projects as proof of capability. Would you have any suggestions for a better approach?
If you or anyone else with similar background is looking to get into quantum computing, then send along your resume at will@rigetti.com
Close. It's been 10 years away for 50 years. :)
(Although more like 65 now).
From their website it seems that they use a 3D cavity approach to quantum computing, which is (or at least was) pursued by IBM as well. Here, instead of using superconducting resonators on a chip, you use superconducting 3-dimensional cavity resonators (typically made from Aluminium) in which you place "naked" qubits that are fabricated on a Sapphire substrate. The advantage of this is that the attainable quality factor of the cavity is much higher than for a coplanar waveguide resonator (although those resonators have caught up quite a bit thanks to work that was done e.g. by the Martinis lab), and that there are fewer noise sources around the qubits.
Personally my current bet would be on Martinis or Schoelkopf to win the race, as they have the best access to both funding (though I don't know the funding details of QuantumCircuits) and Academic resources. Most of the original work on Transmon-based qubits was done at the Schoelkopf lab, and it seems that he has excellent technicians on his team.
In general though it still seems more probable that a ion-based quantum computer will be the first "real" quantum computer, as the technology is more mature and (for small numbers of qubits at least) easier to scale.
Exciting times!
https://newsroom.unsw.edu.au/news/science-tech/quantum-compu...
https://newsroom.unsw.edu.au/news/science-tech/quantum-compu...
I'd much rather ultimately see silicon-based quantum computers, which may eventually become advanced (streamlined) enough to be used by mainstream users, too. Quantum computers using superconductors and liquid nitrogen will never become mainstream. They would just be used by corporations and governments.
So I hope there will be more investing in silicon-based quantum computing research sooner rather than later. Or if not silicon, at least another material that could one day become as cheap and practical.
EDIT: As for who will be the "first" with a practical quantum computer, it does seem like Google will be the one to release a 50-qubit computer this year or the next [2]. There are like 10 other companies that have built 5-qubit quantum computers, but that was still mainly a science experiment to prove that a quantum computer can be made at all.
A 50-qubit quantum computer would establish a new threshold and according to Martinis [3], at least, it should be easier to scale from that point forward. In other words, the "quantum era" may actually begin then. I imagine those "stuck" at 5-qubits still have a long way to go before they can scale quickly beyond that.
[1] http://www.theaustralian.com.au/higher-education/new-spin-on...
[2] https://www.technologyreview.com/s/602283/googles-quantum-dr...
Really the only game in town currently for room-temperature quantum computing are impurity centers in diamond (most commonly nitrogen-vacancy centers). Unfortunately, there are still many difficulties there, especially since nanofabrication of diamond and precise implantation of the impurities is still a new field.
What does this even mean in this context?
My understanding (which is limited!) is that there are some apparently NP-complete problems that are P for a quantum process.
Now, it may be that these problems were never actually NP-complete, but are a superset of P for quantum computers.
Or it may be that quantum computers can really solve some NP-complete problems in P time.
Either way though, it isn't a consequence of CPU-cycles per se, but the underlying mathematical distinction (ie, what a silicon vs quantum computer does, not what it can do in a "cycle").
As it stands, quantum computers can solve some problems faster than classical computers currently can; factorisation, discrete log and other problems with a particular kind of repeated structure are some such problems. However, we do not know for certain that these problems are not efficiently solvable using classical computers (I'd bet that factorisation will be put in P within my lifetime).
A big goal of the QC community is to demonstrate quantum supremacy: to unconditionally show that quantum computers can solve a problem with fewer resources than classical computers. We've made progress on this (check out Scott Aaronson's blog for more info), but still haven't achieved it.
Lastly, a small nitpick. "P time" is not a meaningful phrase; P is a complexity class consisting of problems solvable efficiently (i.e., in polynomial time) on classical computers.
The class of efficiently solvable problems on quantum computers is BQP: Bounded-error Quantum Polynomial-time, which means that BQP is the class of problems that are efficiently solved by a quantum computer, but only with some probability bounded away from 1/2. This means the QC can return incorrect answers, but there are techniques to reduce the error probability to extremely small values.
The classical analogue of this class is BPP, which is Bounded-error probabilistic poly-time. There's a conjecture that BPP=P, and if we have any sort of crypto then the conjecture is true.
> there are some apparently NP-complete problems that are P for a quantum process
The definition of P refers to deterministic machines; no quantum. 'P for a quantum process' is a meaningless phrase. Problems solvable in polynomial time by quantum computers are contained in the classes EQP (if the answers are to be always correct) and BQP (if the answers are to be correct with a probability which may be made as high as desired).
> it may be that these problems were never actually NP-complete, but are a superset of P for quantum computers.
'NP-complete' is not an exact synonym of informal concepts like 'hard' or 'intractable'. It has a precise definition: NP-complete problems are those for which are simultaneously NP (the solution can be verified in polynomial time) and NP-hard (every instance of any NP problem can be reduced in polynomial time to it). There are problems that have been proved NP-complete. For others there are no known polynomial time algorithms, but they are suspected not to be NP-complete either. If P = NP, the notion of NP-completeness becomes trivial and equivalent to simply being in P. There's no way a problem now known to be NP-complete will later be shown not to be.
> Or it may be that quantum computers can really solve some NP-complete problems in P time.
This has not been proved false, but generally considered unlikely. (Also, 'some NP-complete problems' would imply 'all of them', by the definition of NP-completeness.)
Another class of algorithms use quantum computers as simulators (e.g. for chemical simulations). You can also encode certain optimization problems as "constraints" in a quantum system and hope that quantum dynamics will find good solutions to the optim. problem faster.
[1] https://en.wikipedia.org/wiki/Grover%27s_algorithm
[2] https://en.wikipedia.org/wiki/Shor%27s_algorithm
[3] https://en.wikipedia.org/wiki/Quantum_algorithm_for_linear_s...
Yeah it really is. Quantum states just do not even fit in classical memory. Like, exponentially so. The parent was specifically asking about "possibility" not speedup.
One of the first things proven[1] about BQP is that it's contained in P^#P, which is contained in PSPACE. That means a Turing machine can simulate any quantum computer with only polynomial space (but possibly very slowly).
Calling it a speed up is kinda correct but seems slightly wrong to me. It is a completely different computing model which tends to use different algorithms that make solutions to certain problems feasible on a quantum computer that aren't on an equally powered classical computer. That is why it's so powerful, but at the same time, classical computing could stay "faster" than quantum computing for a long time, or even forever.
Most concepts carry over, but some just don't make sense anymore, and a lot of effort must be invested into fundamentally new click-clack (or zip-zap) arrangements to get things done. A large part of the "intelligence" in the final system is actually latent in its structure.
Taking a very long-term timeline into consideration, would it be speculated that quantum computers would ultimately replace traditional computers? Are there categories of problems for which it would be worse to use a quantum computer?
Will these replace traditional computers? Probably not. But you can bet there will eventually be cloud services that you submit very hard jobs and it spits back an answer faster than any traditional computer could.
Simulated annealing is just an optimization. I use it for modelling human motion. You have the position and acceleration of each bone in the legs and feet. You start by deciding on a variable (say, reducing energy expenditure for gait), and then letting the algorithm alter variables and attempt to minimize a variable.
So if quantum annealing is anything like simulated annealing, you aren't "solving" for a single, correct answer, but getting a solution that minimizes or maximizes an outcome faster than brute force.
So a quantum computer could solve "What is the factorization of this huge number?", while quantum annealing could solve "Given these 30 different engine configurations, which combination of intake pressure, fuel flow rate, turbocharger performance curve, and engine timing would result in the best fuel efficiency?"
Here's a value of the fuel efficiency; find me a setting of the parameters that achieves a fuel efficiency better than that.
It's now a search problem. To find the best fuel efficiency, just binary search over possible values of the efficiency.
Where D-Wave differs is that you can only solve a particular kind of optimisation problem, and this kind of problem can't encode general computation. General quantum computers can solve general computation problems, not just the simulated annealing of D-Wave QC.
D-Wave markets themselves as having hundreds or thousands of qubits, but these qubits aren't easily controllable or measurable in ways that would allow Shor's to be executed on them (for example), so at the least, it seems like dishonest marketing. In order to build a general quantum computer, you need to be able to apply gates to arbitrary collections of qubits.
I'd recommend everyone to check out IBM's online http://www.research.ibm.com/quantum/ for a brief tutorial on how you need to be able to wire qubits together, and what a general quantum computer would look like. (You can even execute the result on an actual Quantum Computer; so it gives some idea of what an API would look like).
Quantum computers have come leaps and bounds in the past few years. Especially impressive is research into improving the signal-to-noise ratio of qubits through "dressing" them in microwaves[0].
Despite this, quantum computers still aren't, in the overwhelming majority of cases, better at solving problems than classical machines. Some work still needs to be done in terms of getting better signal-to-noise in qubits, as well as in terms of getting more performant hardware in the processors themselves.
And before there can be an industry built around quantum computers, there'll likely need to be some new way of keeping quantum processors running without having to keep them in a fridge at near-absolute zero.
Again, though, I'm hopeful that QC starts becoming more mainstream soon.
[0] https://arxiv.org/pdf/1603.04800v1.pdfhttps://security.stackexchange.com/questions/87345/how-many-...
I think it's possible we'll have a 4,000 qubit quantum computer by 2030 if a 50-qubit one is released this year, and if quantum computers can also follow a "Moore's Law" of sorts. At least D-Wave seems to be doing it - from 28-qubits in 2007 to 2,000 in 2017.
https://en.wikipedia.org/wiki/D-Wave_Systems
I imagine the NSA/Russia/China would be interested in decrypting communications of at least some people even 15 years after they happened, and they could find some use for them. We already know they intend to store encrypted data indefinitely and they just add new storage for the newly obtained data rather than overwriting the old one.
On that note though, exactly how much encrypted data do they intend to store? You could probably thwart it by sending tons and tons of noise for a small signal. Do they have more storage than Amazon? This stuff isn't free.
Furthermore, AI is very different from the fundamental research underlying quantum computing.
I'm no expert, but at the very least I believe it's recognized that some subset of NP problems, perhaps not NP-hard, should be solvable as if they were polynomial, using quantum computers.
That said, my knowledge of this subject starts and ends with things I've read on the internet, so I could be mistaken. Either way, this[1] was a very interesting introduction to some of the implications/concepts that are involved with this.
P is a separate class from the class of problems considered efficiently solvable; BQP is the class of problems that are efficiently solved by quantum computers, and it is believed that BQP is strictly larger than P.
The missing word here is "efficiently": all problems in NP are solvable.