Ciro Santilli @cirosantilli 37

This paper appears to calculate the Schrödinger equation solution for the hydrogen atom.

TODO is this the original paper on the Schrödinger equation?

Published on Annalen der Physik in 1926.

Open access in German at: onlinelibrary.wiley.com/doi/10.1002/andp.19263840404 which gives volume 384, Issue 4, Pages 361-376. Kudos to Wiley for that. E.g. Nature did not have similar policies as of 2023.

This paper may have fallen into the public domain in the US in 2022! On the Internet Archive we can see scans of the journal that contains it at: ia903403.us.archive.org/29/items/sim_annalen-der-physik_1926_79_contents/sim_annalen-der-physik_1926_79_contents.pdf. Ciro Santilli extracted just the paper to: commons.wikimedia.org/w/index.php?title=File%3AQuantisierung_als_Eigenwertproblem.pdf. It is not as well processed as the Wiley one, but it is of 100% guaranteed clean public domain provenance! TODO: hmmm, it may be public domain in the USA but not Germany, where 70 years after author deaths rules, and Schrodinger died in 1961, so it may be up to 2031 in that country... messy stuff. There's also the question of wether copyright is was tranferred to AdP at publication or not.

An early English translation present at Collected Papers On Wave Mechanics by Deans (1928).

Contains formulas such as the Schrödinger equation solution for the hydrogen atom (1''):where:

This is the true key question: what are the most important algorithms that would be accelerated by quantum computing?

Some candidates:

Do you have proper optimization or quantum chemistry algorithms that will make trillions?

Maybe there is some room for doubt because some applications might be way better in some implementations, but we should at least have a good general idea.

However, clear information on this really hard to come by, not sure why.

Whenever asked e.g. at: physics.stackexchange.com/questions/3390/can-anybody-provide-a-simple-example-of-a-quantum-computer-algorithm/3407 on Physics Stack Exchange people say the infinite mantra:

Lists:

Formulated as a quantum field theory.

Just like a classic programmer does not need to understand the intricacies of how transistors are implemented and CMOS semiconductors, the quantum programmer does not understand physical intricacies of the underlying physical implementation.

The main difference to keep in mind is that quantum computers cannot save and observe intermediate quantum state, so programming a quantum computer is basically like programming a combinatorial-like circuit with gates that operate on (qu)bits:

For this reason programming a quantum computer is much like programming a classical combinatorial circuit as you would do with SPICE, verilog-or-vhdl, in which you are basically describing a graph of gates that goes from the input to the output

For this reason, we can use the words "program" and "circuit" interchangeably to refer to a quantum program

Also remember that and there is no no clocks in combinatorial circuits because there are no registers to drive; and so there is no analogue of clock in the quantum system either,

Another consequence of this is that programming quantum computers does not look like programming the more "common" procedural programming languages such as C or Python, since those fundamentally rely on processor register / memory state all the time.

Quantum programmers can however use classic languages to help describe their quantum programs more easily, for example this is what happens in Qiskit, where you write a Python program that makes Qiskit library calls that describe the quantum program.

One important area of research and development of quantum computing is the development of benchmarks that allow us to compare different quantum computers to decide which one is more powerful than the other.

Ideally, we would like to be able to have a single number that predicts which computer is more powerful than the other for a wide range of algorithms.

However, much like in CPU benchmarking, this is a very complex problem, since different algorithms might perform differently in different architectures, making it very hard to sum up the architecture's capabilities to a single number as we would like.

The only thing that is directly comparable across computers is how two machines perform for a single algorithm, but we want a single number that is representative of many algorithms.

For example, the number of qubits would be a simple naive choice of such performance predictor number. But it is very imprecise, since other factors are also very important:

Quantum volume is another less naive attempt at such metric.

One possibly interesting and possibly obvious point of view, is that a quantum computer is an experimental device that executes a quantum probabilistic experiment for which the probabilities cannot be calculated theoretically efficiently by a nuclear weapon.

This is how quantum computing was originally theorized by the likes of Richard Feynman: they noticed that "Hey, here's a well formulated quantum mechanics problem, which I know the algorithm to solve (calculate the probability of outcomes), but it would take exponential time on the problem size".

The converse is then of course that if you were able to encode useful problems in such an experiment, then you have a computer that allows for exponential speedups.

This can be seen very directly by studying one specific quantum computer implementation. E.g. if you take the simplest to understand one, photonic quantum computer, you can make systems for which you need exponential time to calculate the probabilities that photons will exit through certain holes and not others.

The obvious aspect of this idea is by coming from quantum logic gates are needed because you can't compute the matrix explicitly as it grows exponentially: knowing the full explicit matrix is impossible in practice, and knowing the matrix is equivalent to knowing the probabilities of every outcome.

Quantum is getting hot in 2019, and even Ciro Santilli got a bit excited: quantum computing could be the next big thing.

No useful algorithm has been economically accelerated by quantum yet as of 2019, only useless ones, but the bets are on, big time.

To get a feeling of this, just have a look at the insane number of startups that are already developing quantum algorithms for hardware that doesn't/barely exists! quantumcomputingreport.com/players/privatestartup (archive). Some feared we might be in a bubble: Are we in a quantum computing bubble?

To get a basic idea of what programming a quantum computer looks like start by reading: Section "Quantum computing is just matrix multiplication".

Some people have their doubts, and that is not unreasonable, it might truly not work out. We could be on the verge of an AI winter of quantum computing. But Ciro Santilli feels that it is genuinely impossible to tell as of 2020 if something will work out or not. We really just have to try it out and see. There must have been skeptics before every single next big thing.

Really weird and obscure company, good coverage: thequantuminsider.com/2020/02/06/quantum-computing-incorporated-the-first-publicly-traded-quantum-computing-stock/

Publicly traded in 2007, but only pivoted to quantum computing much later.

Social media:

TODO WTF is this? How is it built? What is special about it?

Mentioned a lot in the context of superconducting quantum computers, e.g. youtu.be/t5nxusm_Umk?t=268 from Video "Quantum Computing with Superconducting Qubits by Alexandre Blais (2012)",

where:

Note that this is the sum of the:Note that the relationship between $\psi$ and $F$ is not explicit. However, if we knew what type of particle we were talking about, e.g. electron, then the knowledge of psi would also give the charge distribution and therefore $F$

As mentioned at the beginning of Quantum Field Theory lecture notes by David Tong (2007):

Technique that uses multiple non-ideal qubits (physical qubits) to simulate/produce one perfect qubit (logical).

One is philosophically reminded of classical error correction codes, where we also have multiple input bits per actual information bit.

TODO understand in detail. This appears to be a fundamental technique since all physical systems we can manufacture are imperfect.

Part of the fundamental interest of this technique is due to the quantum threshold theorem.

For example, when PsiQuantum raised 215M in 2020, they announced that they intended to reach 1 million physical qubits, which would achieve between 100 and 300 logical qubits.

Video "Jeremy O'Brien: "Quantum Technologies" by GoogleTechTalks (2014)" youtu.be/7wCBkAQYBZA?t=2778 describes an error correction approach for a photonic quantum computer.

Bibliography:

Bibliography review:

Course outline given:

Non-relativistic QFT is a limit of relativistic QFT, and can be used to describe for example condensed matter physics systems at very low temperature. But it is still very hard to make accurate measurements even in those experiments.

Defines "relativistic" as: "the Lagrangian is symmetric under the Poincaré group".

Mentions that "QFT is hard" because (a finite list follows???):But I guess that if you fully understand what that means precisely, QTF won't be too hard for you!

Notably, this is stark contrast with rotation symmetry groups (SO(3)) which appears in space rotations present in non-relativistic quantum mechanics.

www.youtube.com/watch?v=T58H6ofIOpE&t=5097 describes the relativistic particle in a box thought experiment with shrinking walls

Lecture notes found by Googling "quantum field theory pdf":

Author: David Tong.

Number of pages circa 2021: 155.

It should also be noted that those notes are still being updated circa 2020 much after original publication. But without Git to track the LaTeX, it is hard to be sure how much. We'll get there one day, one day.

Likely used at: David Tong's 2009 Quantum Field Theory lectures at the Perimeter Institute.

Some quotes self describing the work:

A follow up course in the University of Cambridge seems to be the "Advanced QFT course" (AQFT, Quantum field theory II) by David Skinner: www.damtp.cam.ac.uk/user/dbs26/AQFT.html

Quantum version of the Hall effect.

As you increase the magnetic field, you can see the Hall resistance increase, but it does so in discrete steps.

Gotta understand this because the name sounds cool. Maybe also because it is used to define the fucking ampere in the 2019 redefinition of the SI base units.

At least the experiment description itself is easy to understand. The hard part is the physical theory behind.

TODO experiment video.

The effect can be separated into two modes:

One key insight, is that the matrix of a non-trivial quantum circuit is going to be huge, and won't fit into any amount classical memory that can be present in this universe.

This is because the matrix is exponential in the number qubits, and $2^{100}$ is more than the number of atoms in the universe!

Therefore, off the bat we know that we cannot possibly describe those matrices in an explicit form, but rather must use some kind of shorthand.

But it gets worse.

Even if we had enough memory, the act of explicitly computing the matrix is not generally possible.

This is because knowing the matrix, basically means knowing the probability result for all possible $2^N$ outputs for each of the $2^N$ possible inputs.

But if we had those probabilities, our algorithmic problem would already be solved in the first place! We would "just" go over each of those output probabilities (OK, there are $2^N$ of those, which is also an insurmountable problem in itself), and the largest probability would be the answer.

So if we could calculate those probabilities on a classical machine, we would also be able to simulate the quantum computer on the classical machine, and quantum computing would not be able to give exponential speedups, which we know it does.

To see this, consider that for a given input, say 000 on a 3 qubit machine, the corresponding 8-sized quantum state looks like:and therefore when you multiply it by the unitary matrix of the quantum circuit, what you get is the first column of the unitary matrix of the quantum circuit. And 001, gives the second column and so on.

As a result, to prove that a quantum algorithm is correct, we need to be a bit smarter than "just calculate the full matrix".

Which is why you should now go and read: Section "Quantum algorithm".

This type of thinking links back to how physical experiments relate to quantum computing: a quantum computer realizes a physical experiment to which we cannot calculate the probabilities of outcomes without exponential time.

So for example in the case of a photonic quantum computer, you are not able to calculate from theory the probability that photons will show up on certain wires or not.