physicsAI

The paper is also available without a paywall here: http://qpt.physics.harvard.edu/p364.pdf

I think Scott Aaronson is right to rail against the explanation of "trying all possibilities at the same time". The emphasis should be on *choreographing* the right interference, not on the fact that all possibilities get accounted for by a quantum computer (since each outcome has a measurable probability).

In fact, "trying all possibilities at the same time" is a ubiquitous phenomenon in Nature. Light rays find their trajectory by tryin out all possible paths and only the one with the highest constructive interference survives (this is what Feynman discovered in Quantum Electrodynamics, which is referenced in the article).

The real trick in quantum computing is manipulating qubits through quantum gates such that the outcome that gets assigned the highest probability is the correct one. This is indeed hard as the environment usually interferes and spreads out the probabilities across all outcomes, leading to a junky, random outcome.

I took John's class on theoretical cosmology while a master's student in Cambridge. He was remarkably old fashioned — he scribbled in his undecipherable handwriting on a bunch of transparent slides and used a projector to share things in class. He had a deep mellow voice and clearly had a supreme grasp of all areas of cosmology. It is a real shame he died so early.

Great product, solving exact pain point we have right now: non-technical team members glued to Django Admin all day long and waiting days to get much-needed data from engineers, who cannot find the time to write simple views with relevant SQL queries.

I have signed up and started testing your product. Looks great so far. If things check out, there is a good chance we will sign up for real and pay.

Congrats on the launch!

+1 for Mercury. We also switched to Mercury after 8 months with SVB. It is crazy how much difference a good UI makes and the time it saves.

Something important to keep in mind is the fact that to execute Shor's prime factorization algorithm (which can break encryption protocols, like the RSA) you need millions of physical quantum bits.

Even the most optimistic quantum engineers agree that building such enormous quantum devices is likely 30+ years away. So Bitcoin has plenty of time to adapt.

I cannot seem to find your email address in your profile.

Drop me a line - my email is in my profile.

I thought I would also add my two cents, though there have been many excellent responses already. I am about to defend my PhD in Physics at MIT.

First of all - great idea! It is never too late to learn math and physics! In fact, with hard work and commitment, anybody can muster them to a high level.

(1) Reading =/= understanding in math and physics. You understand a topic only if you can solve the problems.

(2) Work through the solved problems you encounter in textbooks carefully.

(3) Most people around me have never read any physics textbook cover to cover. E.g. reading Halliday, Resnick & Walker completely might take you years! Not all topics are equally important. Focus on the important parts.

(4) You need guidance on what is important and what is not. Online courses, college material (especially problem sets!), teaching webpages could be a helpful guide. Checkout MIT OCW, once you are ready.

(5) Finding someone to talk to is really useful. You will likely have questions. Cultivating some relationship that allows you to ask questions is invaluable.

(4) College courses in math and physics have a very definitive order. It is really difficult to skip any step along the way. E.g. to understand special relativity, you must first understand classical physics and electrodynamics.

(5) Be prepared that the timescales in physics are long. Often, what turns people off is that they do not get things quickly (e.g. in 15-30 minutes). If you find yourself thinking hours about seemingly simple problems, do not despair! That is normal in physics.

(6) You have to 'soak in' physics. It takes time. Initially, you might feel like you do not make a lot of progress, but the more you know, the quicker it will get. Give yourself time and be patient and persistent.

(7) Often, just writing things down helps a lot with making things stick. It is a way of developing 'muscle memory'. So try and take notes while reading. Copying out solved problems from textbooks is also a good technique.

(8) Counterintuitive: If you get completely stuck, move on! Learning often happens in non-linear ways. If you hit an insurmountable roadblock, just keep going. When you return in a few days/weeks, things will almost certainly be clearer.

@alpb Thank you for your helpful reply! The Kubernetes Service Catalog looks like an amazing project, I hope it will be released soon.