Ross Ulbricht's LinkedIn profile picture
. Source. Still up as of 2023: www.linkedin.com/in/rossulbricht/Can be approximated with a diaphragm.
Fixed quantum angular momentum in a given direction.
Can range between .
E.g. consider gallium which is 1s2 2s2 2p6 3s2 3p6 4s2 3d10 4p1:
- the electrons in s-orbitals such as 1s, 2d, and 3d are , and so the only value for is 0
- the electrons in p-orbitals such as 2p, 3p and 4p are , and so the possible values for are -1, 0 and 1
- the electrons in d-orbitals such as 2d are , and so the possible values for are -2, -1, 0 and 1 and 2
The z component of the quantum angular momentum is simply:so e.g. again for gallium:
- s-orbitals: necessarily have 0 z angular momentum
- p-orbitals: have either 0, or z angular momentum
Note that this direction is arbitrary, since for a fixed azimuthal quantum number (and therefore fixed total angular momentum), we can only know one direction for sure. is normally used by convention.
catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch explains:
The difference between v1 and v1.5 is that, in the bottleneck blocks which requires downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a smallperformance drawback (~5% imgs/sec).
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:
- qubit error rate
- coherence time, which determines the maximum circuit depth
- qubit connectivity. Can you only connect to 4 neighbouring qubits in a 2D plane? Or to every other qubit equally as well?
Quantum volume is another less naive attempt at such metric.
The mantra of the computer simulation engineer.
Boooooring.
These are the rules which specify what different concurrent read/write memory accesses from different threads/processes can or cannot see.
Notable such set of rules include:
- C++ memory model. These are also reflected on the semantics of memory of the corresponding instruction set architecture
- SQL transaction isolation level
A "quantum computer operating system". Or in English, control system + quantum error correction.
uknqt.ukri.org/wp-content/uploads/2021/10/UKNQTP-Strategic-Intent-2020.pdf page 24 mentions UKNQTP investment and gives an overview of some layers.
Although transistors were revolutionary, it is fun to note that they were just "way cheaper and more reliable and smaller" versions of exactly the main functions that a vacuum tube could achieve
Like isomorphism, but does not have to be one-to-one: multiple different inputs can have the same output.
The image is as for any function smaller or equal in size as the domain of course.
This brings us to the key intuition about group homomorphisms: they are a way to split out a larger group into smaller groups that retains a subset of the original structure.
As shown by the fundamental theorem on homomorphisms, each group homomorphism is fully characterized by a normal subgroup of the domain.
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