Hello everybody! I'm a bioinformatics student and I've recently grown an interest in quantum computing and I was wondering if it was possible to develop algorithms that can run on quantum computers to solve biology related problems.
One use of quantum computers is breaking encryption. Another is to use the idea of entanglement to set up really secure encryption that cannot be intercepted by third parties. If a third party tries to intercept an entangled encryption process, then things become disentangled, and you can actually detect their presence.
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I was thinking more that STEM students would be able to understand the book. But certainly it would be useful for chemistry and biology, and maybe as time goes on quantum computing is going to be applied to more and more STEM fields.
Probably the major excitement of the moment concerns the progress in actually building a quantum computer. With quantum computing you do have all these errors that keep creeping in so you have to do something to try and ameliorate that. That is the big challenge.
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I suppose electrical engineering plays two roles in quantum computing. The first role is that generally all quantum hardware needs a stack of various classical components for the control and readout of qubits, like any physics experiment. But I imagine you are more interested in something like superconducting qubits, where electrical circuits themselves form the qubits in use. If this is the case, as far as textbooks go, I can suggest "Quantum Engineering: Theory and Design of Quantum Coherent Structures" by A.M. Zagoskin, which provides ample details on Josephson junctions, circuit quantum electrodynamics (cQED), and quantum noise, though it's a bit of a deep dive if you are new to this stuff. There are a lot of review papers on these topics as well, that are arguably a better way to learn the subject.
I am a computer science student and am currently searching for resources from where I can learn about quantum computers, quantum computing models, their working principles, their gates and some simple quantum algorithms.
After a few weeks with the Mermin textbook, I bought Quantum Computing for Computer Scientists by Yanofsky & Mannucci. This is a much softer introduction than Mermin, almost too soft: I skipped the first few chapters on linear algebra and complex numbers. However, in combination with the Mermin textbook, I acquired a good understanding of quantum computing basics. It was at this point I reached my own personal threshold for feeling I "understood" quantum computing.
People often recommend Quantum Computation and Quantum Information by Nielsen & Chuang (also called "Mike & Ike") for beginners. I believe this is not good advice. Had I tried to learn from that textbook, I would have failed. However, it is an excellent textbook after you already understand the basics. Anecdotally, I knew two people who tried to learn quantum computing at the same time as me: one used Mike & Ike, and the other used a book called Quantum Computing: A Gentle Introduction. Neither of those people understand quantum computing today.
My experience learning quantum computing required a huge amount of mental effort, and in the end what I learned wasn't actually complicated! So, I created a lecture called Quantum Computing for Computer Scientists (slides) which is the lecture I wish I'd had access to before trying to read any textbooks. The lecture is popular and well-received, and I think it covers all the stuff that's really conceptually tricky; once you're over those conceptual hurdles, you can apply your regular computer science skills to learn everything else about quantum computing you need (how specific algorithms work, etc.) Thus my "hindsight" study guide is as follows:
The book Quantum computation and quantum information by Nielsen and Chuang is a good read in order to introduce yourself to the world of quantum computation. The book assumes minimal prior experience with quantum mechanics and with computer science, aiming instead to be a self-contained introduction to the relevant features of both, so it is really a nice starting point for anyone who wishes to introduce himself to world of quantum information science.
It really depends on where your brain is at. In particular, how much mathematics you have under your belt. Much of what you will need to understand is contained within linear algebra (over the complex numbers.) Zooming in more: it's all in the tensor product. Most explanations I see of how tensoring works are brutally difficult to understand as a novice. In fact, the case can be made that the whole field of quantum computing has been held back by our understanding of tensor products and ability to work with them (calculate.) In this vein, I would highly recommend the recent book by Coecke and Kissinger "Picturing Quantum Processes." Although perhaps you would like to struggle with a more traditional text first, in order to more appreciate the diagrammatic approach.
The book assumes very little mathematical background, but covers a large breadth of topics with some focus on comparing and contrasting classical and quantum computing, with many many questions (and answers at the end).
"I am addressing computer scientists, electrical engineers, or mathematicians who may know little or nothing about quantum physics... but who wish to acquire enough facility in the subject to be able to follow the new developments in quantum computation.... Not the least of the surprising things about quantum computation is that remarkably little background in quantum mechanics has to be acquired...."
[Quantum computers] are a new type of computer that can tackle problems that digital computers can never solve, even with an infinite amount of time. For example, digital computers can never accurately calculate how atoms combine to create crucial chemical reactions, especially those that make life possible. Digital computers can only compute on digital tape, consisting of a series of 0s and 1s, which are too crude to describe the delicate waves of electrons dancing deep inside a molecule. For example, when tediously computing the paths taken by a mouse in a maze, a digital computer has to painfully analyze each possible path, one after the other. A quantum computer, however, simultaneously analyzes all possible paths at the same time, with lightning speed.
Thank you for posting this. For people like me this *IS* valuable. As someone who knows quantum mechanics, has some idea about quantum computing but only very surface leve and well nehind the times (we had lunch once about 15 years ago), I often find myself giving benefit of tie doubt to other scientists who speak with authority and seem to have expertise (I.e. I have heard of them). How do I know what to take seriously without investing implausible amounts of time and effort? Posts like this help immensely
If this sort of popularization is needed to get funding, then the industry has serious problems. And if criticizing this sort of thing risks substantially damaging the quantum computing industry, then the industry is far more being driven by a hype bubble than even some of its ore vocal critics would likely suspect.
The statement about measuring a quantum state resulting in a single outcome is true, but the power of quantum computing lies in the ability to manipulate a large number of superimposed and entangled states before measurement, which can provide computational speedups for certain problems.
Scott #58: I would have probably watched most of them if not all, but I singled out the Dresden one because it was short, funny and yet conveyed the gist of QC. Now that the the so called popular science messiahs are piling up on QC beyond videos and into writing books, thought it high time you took this beyond this blog. But not in your characteristic style of debunking the myths of QC which you do so well, but being opposite and probably portray what would the most plausible usefulness of QC for the general public. Thanks
Serious scientists with substantial real contributions to a field, who are still practicing, rarely have time or the inclination to be science popularizers (Richard Feynman may be an exception..). I have always considered Kaku to be a crank. No wonder he is drawn to String theory, and other untestable theories. Maybe he can try drawing funding to be mega particle accelerator far more ambitious than LHC.
Rather than bringing money to the field this kind of hype building will trigger a quantum Winter.
Causal Network, the press release you linked to says nothing about algorithms. It says a combined classical (tensor) and quantum (39 qubits) circuit has been designed and simulated, presumably awaiting the availability of 39 error-correcting qubits. It does not say such a circuit has been or could be made at this time; just that they are ready to make one when practical quantum computing becomes available. It is not clear from the press release why they think this would produce some breakthrough in jet engine design. Based on experience with turbine design at GE, I doubt it. Design of jet-engine flow paths is already an computer-automated process involving iterative optimization, which takes up a small part of the design, development, and testing time for a new engine. I think the press-release is meant to convey that the companies involved have state-of-the-art expertise.
A qubit is a quantum mechanical system that can be MEASURED to be in one of two possible basis states. When it is not being measured, it can be placed in a superposition of the basis states. The possible superpostions can be viewed as a sphere where latitude determines probability of measuring each basis state and longitude is called the phase.
After applying gates, your system may cantain an exponential amount of info compared to the number of qubits, but you cannot access this info directly. You can only get info out of the system by measuring it. The measurement will only contain n bits of info. This is the issue Scott spends years on, find algorithms of quantum gates that can take the info in a large qc and arrange for useful info to be the outcome of a measurement. This is generally done by using the phases of superposition to emphasize an outcome or deemphasize an outcome. Scott is interrested in problems where the number of quantum gates required to solve is much fewer than the number of operations a digital computer would require. be457b7860
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