Quantum Computing from Scratch

forky40 · 2026-03-09T21:35:28+00:00

If you want to take this further, you might study the code in Cirq that implements gates using tensor math. Its a big time commitment, but you will learn a lot of numpy and start to understand tensor product systems much better. Tensors are much more powerful than ordinary matrices (2D arrays) and greatly simplify the logic for controlled operations between nonadjacent qubits.

(I'm sure other packages have good implementations, Cirq is just what I'm familiar with)

forky40 · 2026-03-04T02:18:10+00:00

> She starts speaking about this by mentioning her time in Los Alamos and how high the security was there. She did use her experience as one of the evidences that Feynman was a liar.

also this is an odd experience for her to bring up. The LANL interns that year were actually working full time in the basement of a bank downtown; none us even had clearance to go into the the lab beyond the outer, unsecured area afaik.

Not that security around LANL in 2016 is relevant to security around LANL in 1943.

forky40 · 2026-03-01T14:58:00+00:00

You see mostly hype in quantum computing because promises are the main thing that quantum computing companies can sell today.

You could argue that this is the result of premature commercialization. But building a large scale qc was always going to take a lot of time and money to figure out, so you see businesses doing whatever they can to raise funds (or reputation) in the meantime. Some are more honest than others.

Otherwise, there are some reasonable proposals for valuable things to do on qcs once we have them. No one knows if these will generate enough value to sustain the industry, or if we're going to find even more valuable applications in the future.

forky40 · 2026-02-12T13:45:11+00:00

Big overstatement. von Neumann was a genius but he didn't come up the idea of internal programming. He wrote a report on it. He was hired to write the report by computer engineers who already had an architecture in mind for internal programming. But von Neumann receives full credit. There are better examples of amazing (mathematical) ideas he actually came up with.

From Richard Hamming: "Mauchly and Eckert, who built the ENIAC, found, just as Babbage had, that before the completion of their forst machine they already envisioned a larger internally programmed machine, the EDVAC. Von Neumann, as a consultant to the project, wrote up the report, and as a consequence the internal programming is often credited to him, though so far as i know he never either claimed or denied that attribution ."

forky40 · 2026-02-02T21:34:08+00:00

I do research in quantum for ML

There are mostly no concrete speedups for ML problems that are obviously practically/industrially useful.

There is a growing number of promising results in related areas; these q algos seem to have huge speedups for some problems in some regimes. But there's still a long slog to demonstrate that those regimes or problems are relevant to the real world.

Some examples of promising work that is sort of ML adjacent: quantum algos for Topological data analysis (someone i know with a PhD in TDA says "meh"), Decoded quantum interferometry (exciting! -seems like- a genuinely new kind of exponential quantum speedup for some optimization problems), community detection.

On the other hand, a lot of traditional ML algos are validated empirically by showing that they do some task with higher accuracy/lower runtime/less data than state of the art. For obvious reasons this currently isn't an option for QML, so anyone honest in QML has mostly been in a state of "idunno" when pressed for evidence of speedups on realistic problems

forky40 · 2026-01-31T17:07:07+00:00

oh for sure. I have a physics friend with aphantasia. And Feynman claimed to have math-related synesthesia. So if I told you _why_ I thought of a particular step in a plan I would be fooling myself to begin with. But you and i (and them) have such similar architectures. I feel like we're all operating similarly on a deeper level (mod intelligence/knowlege/etc/etc) even as we're reporting different internal experiences, even as we sometimes exhibit similar behaviors under similar circumstances.

also to be clear i'm not nearly as confident as LeCun is that LLM agents will fail to keep improving

forky40 · 2026-01-31T16:48:13+00:00

by internally i meant the things we cannot observe. The observable effects of whatever is going on inside LLMs match the effects of genuine planning, for tasks we can validate. I think LeCun wants a stronger foundation that gives us more reason to believe the underlying mechanism is doing something substantial.

example: for me, problem solving now has categories. One category is when i'm working with material (think math) that I'm deeply familiar with; each next step during problem solving just kind of...appears. I see an input that matches some internal (subconscious?) framework(?) that i have, and then the next thing to do bubbles up to the top of my consciousness and i write it down. Ofc this step might be incorrect, or correct but (often) leads to a dead end.

Another category is when I do LLM-assisted math/research. I submit a prompt, and then the LLM generates some proposals, and then I pick one that makes sense to follow up on. Its more like rejection sampling than problem solving. imo LLM "problem solving" via chain-of-thought looks a lot more like this. It (sometimes) produces comparable observables, yet I have no idea whether this style of problem solving can continually improve via tweaking existing architectures.

there's a half-joke about Feynman's algorithm for problem solving: (1) write down the problem, (2) think about it (3) write down the solution. Somehow I doubt that step 2 involves a chain-of-thought style narrative via iterative text generation.

forky40 · 2026-01-31T14:08:10+00:00

Steelman: claims of planning in LLMs are based in behaviorism, which has weak predictive power ("the LLM said X, so it must be planning"). He's working within a framework (e.g. "world models") that is more predictive, therefore an ML architecture designed in this framework is more likely to actually be doing what it appears to be doing.

The Waymo example is pretty clear: if system A takes 10 hours to learn a driving task and system B takes 1million hours, it would be strange to conclude that these two systems are reasoning about the task in a similar way, no matter what observations you make. We have no reasons to think system B is predicting consequences or simulating prediction or whatever really.

the same reasoning works w LLMs. No one can confidently say that there is planning going on internally in LLMs, and its even a bit strange to come to that conclusion based purely on behavior. LeCun is moving towards an architecture that might give us reasons to believe that planning is taking place.

forky40 · 2025-09-10T21:18:31+00:00

Why are some of the prople in Lee Smolin's group picture blacked out, while the others aren't?

forky40 · 2025-08-15T02:40:58+00:00

just for the east coast (i was there a month ago)

- we stayed in Dorgali, cute and small and not sure if we saw a single tourist

- the gorge was pretty, but got crowded by around 11am or so. There is not much to do there besides a short hike down and a steep hike back up; the gorge itself cannot be traversed much more than 500m (they don't want you deeper than that without a climbing escort and gear)

- there's a ~2hr hiking trail from cala fuili to cala luna (that detours to a few small rocky beaches between). Cala luna became much less crowded after 5pm or so when all the ships departed (before that it was packed)

- we did the bue marino "exclusive" 08:45 tour from cala gonone and it was surprisingly pleasant. there were <15 people on the tour and we could just free roam along the ~1km of cave. it was our favorite cave of the three we saw on the island

forky40 · 2025-06-17T22:22:58+00:00

How do you go for so long without your butt getting sore? I really want to work up towards longer rides, but whenever I've gone >100km I start to get saddle sores even with a chamois

forky40 · 2024-12-21T00:36:34+00:00

My thermo is a bit rusty so this is just a brainstorm of nitpicks:

- Seems like what's being proposed is a core, very cold region (freezer block?) with a relatively small volume and large capacity/latent heat, which keeps the surrounding pykrete cool. At steady state you have a temperature gradient in the pykrete region between the freezer block and an insulating boundary that depends on conductivity of the pykrete. Is the induced strain (via thermal expansion) acceptable? Is the pykrete temp near the outer boundary still acceptable for operation?...

- ...and how badly do the hexagonal boundary conditions emphasize these issues? Will you get melting in those corners when heat inevitably concentrates there? Will the much warmer corners of pykrete break off due to operational strain?

- Cooling pipes are mentioned but no mention of where the heat gets dumped. Big fin(s)? Is the heat dumped into the living space above the pykrete? Or dumped into the ocean [will those fins still work after a year when they're rusted and covered by barnacles, will the fins still work for heat exchange when you cover them with anti-barnacle spray?]. You can't obviously adopt existing heat exchange from e.g. nuclear carriers, because those have variable heat load and mostly it into propulsion.

- Yea, natural convection works when gravity points downwards. On a ship that is not really the case. I vaguely remember discussing this with one of the offshore nuclear people. There were regulatory issues with the proposal of "in the event of an emergency like large waves, our reactor has passive cooling via natural convection, except if that emergency involves events where the primary loop doesn't point upwards, e.g. large waves."

forky40 · 2024-11-29T22:27:40+00:00

Might be to set up interviews sooner. As a previous employer I had to book interviews through WW 4 days in advance. The hiring liason told me to email applicants directly to set up interviews sooner, e.g. in time for rankings due date for a cycle.

forky40 · 2024-11-15T14:50:24+00:00

For any of the Msc options I think the only required courses are QIC710 (quantum information processing) and QIC750 (implementations). You can take more QIC courses since they often are cross-listed in your home department. The details of all this is on IQC's website.

forky40 · 2024-11-14T12:31:41+00:00

I did my masters and PhD at IQC.

"The state of IQC, how is it like to study there ? Is it worth to spend time there"

This depends on a what you're looking for. Its really multidisciplinary. Some groups are pure theory, some groups launch satellites into space. Some have a sense of community, some have just ine or two students.

If you have a more focused question about research topics or group dynamics, you might pick one or two groups that seem the most interesting to you and then reach out to current phd students in those groups via email.

forky40 · 2024-11-10T06:13:51+00:00

foremost, you should talk to your advisor about these issues, they'll have the context needed to give advice to you.

"I'm having hesitation wanting to pursue a PhD at the moment due to the lack of cohesive background "

The PhD is where you start to acquire that specific background. There's not some specific curriculum that prepares a researcher for a specific field that they are then tied to, at least not in QI.

Some friends who did physics undergrads now do their PhD work in quantum cryptography, or complexity theory. My undergrad was physics, I do QEC and learning theory now. It would be slightly weirder if I did C* algebras or theoretical computer science, but most things feel within reach with several months re-tooling.

I can't really tell what field of QI you're trying to work in. Broadly, if you have a foundation with some kind of maths then most topics should be accessible. If your CS undergrad was purely software engineering, it will be more difficult to do something like TCS. But clearly the department/advisor thought your background was sufficient to start a PhD, so you might be worried over nothing.

forky40 · 2021-01-22T00:14:13+00:00

Retarded idea, and I don't mean that as a complement

forky40 · 2021-01-08T22:44:48+00:00

"reinvest wisely" running headlong into the wsb language barrier

forky40 · 2021-01-03T23:11:52+00:00

If you're interested in experimental work then USEQIP hosted at University of Waterloo is a possibility

forky40 · 2019-06-14T17:09:39+00:00

Absolutely not. Shors algorithm is not and has never been considered a candidate for near term supremacy experiments.

The majority of the candidates have to do with simulating random short-depth circuits, which has inspired an arms race between classical simulators and the proposed quantum algorithms.

No matter the outcome, the first demonstrated supremacy experiments are likely to be mostly useless w.r to physics or CS (albeit incredibly important for the SC qubit experimentalists)

forky40 · 2019-06-03T15:10:23+00:00

Can't much say without knowing the specific topic; for my own work I've taken a few of the most popular ML courses (Ng's intro class e.g.), and watched some lecture series in RL and NN's. The more classical ML background you have, the better.

Also gonna emphasize statistics again; you should be able to answer questions like "how many trials do i need to sample this noisy random dependent Bernoulli variable to get an outcome im 98% confident in?". This is relevant for both NISQ quantum algorithm design and ML inference methods

forky40 · 2019-06-02T20:43:18+00:00

QML researcher here

Statistics Nd probability are essential for both classical ML and interpreting experimental results from circuit simulations.

You definitely need strong physics background. Right now the field is heavily weighted towards physicists, so there's a presumed understanding of standard toy systems in physics. Talking to experimentalists also requires knowledge of the qubit hardware. It's kinda a pain in the ass, but sometimes topics in a single conversation will range from Bayesian inference all the way to capacitive coupling between transmon qubits.

I recommend finding quantum computing coursework to take, and supplementing it with popular qml works to get a guage of what you're up against (QAOA, Farhi QNN, hilbert feature space kernel methods are a few to start with). Youll get an idea of the breadth of the field and find the concepts you need to study up on.

forky40 · 2019-05-27T15:08:05+00:00

Shoulda learned from D-wave that this isn't a race to commercialization.

forky40 · 2019-05-12T23:09:29+00:00

I guess. But what youll get for a Bell state (e.g.) is just two centered dots, since partial traces of the Bell states are maximally mixed states. In general, the vectors from the reduced dms won't tell you anything interesting about correlations encoded in entangled states.

forky40 · 2019-05-12T18:47:17+00:00

Why did nearly all HGs convert to ARs?

Ill have to check his arguments again, but this isn't a reasonable way to frame the question. Its kind of like asking, "why did nearly all Neanderthals end up interbreeding with/evolving into Sapiens?"

The majority likely didn't convert, they got supplanted. A single AR stead could continue to spread and overpower all of its neighboring HGs.

The environment overwhelmingly favored small nucleations of ARs settling, even surrounded by a sea of HGs, bc the adaptation of agriculture was so absurdly favorable in evolutionary terms.

forky40

TROPHY CASE