by Ciro Santilli (@cirosantilli, 34)
By Xanadu.
Apparently meant to be higher level.
Homepage: pennylane.ai/
Python library, claims multiple backends, including simulation and real IBM quantum computer.
Our version at qiskit/hello.py.
Our example uses a Bell state circuit to illustrate all the fundamental Qiskit basics.
Sample program output, counts are randomized each time.
First we take the quantum state vector immediately after the input. 
input:
state:
Statevector([1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
            dims=(2, 2))
probs:
[1. 0. 0. 0.]
We understand that the first element of Statevector is , and has probability of 1.0.
Next we take the state after a Hadamard gate on the first qubit: 
h:
state:
Statevector([0.70710678+0.j, 0.70710678+0.j, 0.        +0.j,
             0.        +0.j],
            dims=(2, 2))
probs:
[0.5 0.5 0.  0. ]
We now understand that the second element of the Statevector is , and now we have a 50/50 propabability split for the first bit.
Then we apply the CNOT gate: 
cx:
state:
Statevector([0.70710678+0.j, 0.        +0.j, 0.        +0.j,
             0.70710678+0.j],
            dims=(2, 2))
probs:
[0.5 0.  0.  0.5]
which leaves us with the final .
Then we print the circuit a bit: 
qc without measure:
     ┌───┐
q_0: ┤ H ├──■──
     └───┘┌─┴─┐
q_1: ─────┤ X ├
          └───┘
c: 2/══════════

qc with measure:
     ┌───┐     ┌─┐
q_0: ┤ H ├──■──┤M├───
     └───┘┌─┴─┐└╥┘┌─┐
q_1: ─────┤ X ├─╫─┤M├
          └───┘ ║ └╥┘
c: 2/═══════════╩══╩═
                0  1
qasm:
OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
creg c[2];
h q[0];
cx q[0],q[1];
measure q[0] -> c[0];
measure q[1] -> c[1];
And finally we compile the circuit and do some sample measurements: 
qct:
     ┌───┐     ┌─┐
q_0: ┤ H ├──■──┤M├───
     └───┘┌─┴─┐└╥┘┌─┐
q_1: ─────┤ X ├─╫─┤M├
          └───┘ ║ └╥┘
c: 2/═══════════╩══╩═
                0  1
counts={'11': 484, '00': 516}
counts={'11': 493, '00': 507}
In this example we will initialize a quantum circuit with a single CNOT gate and see the output values.
By default, Qiskit initializes every qubit to 0 as shown in the qiskit/hello.py. But we can also initialize to arbitrary values as would be done when computing the output for various different inputs.
Output: 
     ┌──────────────────────┐
q_0: ┤0                     ├──■──
     │  Initialize(1,0,0,0) │┌─┴─┐
q_1: ┤1                     ├┤ X ├
     └──────────────────────┘└───┘
c: 2/═════════════════════════════

init: [1, 0, 0, 0]
probs: [1. 0. 0. 0.]

init: [0, 1, 0, 0]
probs: [0. 0. 0. 1.]

init: [0, 0, 1, 0]
probs: [0. 0. 1. 0.]

init: [0, 0, 0, 1]
probs: [0. 1. 0. 0.]

     ┌──────────────────────────────────┐
q_0: ┤0                                 ├──■──
     │  Initialize(0.70711,0,0,0.70711) │┌─┴─┐
q_1: ┤1                                 ├┤ X ├
     └──────────────────────────────────┘└───┘
c: 2/═════════════════════════════════════════

init: [0.7071067811865475, 0, 0, 0.7071067811865475]
probs: [0.5 0.5 0.  0. ]
which we should all be able to understand intuitively given our understanding of the CNOT gate and quantum state vectors.
quantumcomputing.stackexchange.com/questions/13202/qiskit-initializing-n-qubits-with-binary-values-0s-and-1s describes how to initialize circuits qubits only with binary 0 or 1 to avoid dealing with the exponential number of elements of the quantum state vector.
This is an example of the qiskit.circuit.library.QFT implementation of the Quantum Fourier transform function which is documented at: docs.quantum.ibm.com/api/qiskit/0.44/qiskit.circuit.library.QFT
Output: 
init: [1, 0, 0, 0, 0, 0, 0, 0]
qc
     ┌──────────────────────────────┐┌──────┐
q_0: ┤0                             ├┤0     ├
     │                              ││      │
q_1: ┤1 Initialize(1,0,0,0,0,0,0,0) ├┤1 QFT ├
     │                              ││      │
q_2: ┤2                             ├┤2     ├
     └──────────────────────────────┘└──────┘
transpiled qc
     ┌──────────────────────────────┐                                     ┌───┐   
q_0: ┤0                             ├────────────────────■────────■───────┤ H ├─X─
     │                              │              ┌───┐ │        │P(π/2) └───┘ │ 
q_1: ┤1 Initialize(1,0,0,0,0,0,0,0) ├──────■───────┤ H ├─┼────────■─────────────┼─
     │                              │┌───┐ │P(π/2) └───┘ │P(π/4)                │ 
q_2: ┤2                             ├┤ H ├─■─────────────■──────────────────────X─
     └──────────────────────────────┘└───┘
Statevector([0.35355339+0.j, 0.35355339+0.j, 0.35355339+0.j,
             0.35355339+0.j, 0.35355339+0.j, 0.35355339+0.j,
             0.35355339+0.j, 0.35355339+0.j],
            dims=(2, 2, 2))

init: [0.0, 0.35355339059327373, 0.5, 0.3535533905932738, 6.123233995736766e-17, -0.35355339059327373, -0.5, -0.35355339059327384]
Statevector([ 7.71600526e-17+5.22650714e-17j,
              1.86749130e-16+7.07106781e-01j,
             -6.10667421e-18+6.10667421e-18j,
              1.13711443e-16-1.11022302e-16j,
              2.16489014e-17-8.96726857e-18j,
             -5.68557215e-17-1.11022302e-16j,
             -6.10667421e-18-4.94044770e-17j,
             -3.30200457e-16-7.07106781e-01j],
            dims=(2, 2, 2))
So this also serves as a more interesting example of quantum compilation, mapping the QFT gate to Qiskit Aer primitives.
If we don't transpile in this example, then running blows up with: 
qiskit_aer.aererror.AerError: 'unknown instruction: QFT'
The second input is:
(1)
and the output of that approximately: 
[0, 1j/sqrt(2), 0, 0, 0, 0, 0, 1j/sqrt(2)]
which can be defined simply as the normalized DFT of the input quantum state vector.
This function does quantum compilation. Shown e.g. at qiskit/qft.py.
You get an error like this if you forget to call qiskit.transpile(): 
qiskit_aer.aererror.AerError: 'unknown instruction: QFT'
Related: quantumcomputing.stackexchange.com/questions/34396/aererror-unknown-instruction-c-unitary-while-using-control-unitary-operator/35132#35132
These are a bit like the Verilog of quantum computing.
One would hope that they are not Turing complete, this way they may serve as a way to pass on data in such a way that the receiver knows they will only be doing so much computation in advance to unpack the circuit. So it would be like JSON is for JavaScript.
On Qiskit qiskit==0.44.1: 
qc.qasm()
E.g. with our qiskit/hello.py, we obtain the Bell state circuit: 
OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
creg c[2];
h q[0];
cx q[0],q[1];
measure q[0] -> c[0];
measure q[1] -> c[1];
Some people call it "operating System".
The main parts of those systems are:
  • sending multiple signals at very precise times to the system
  • reading out some quantum error correction bits and sending error correcting signals back in a control loop
It seems that all/almost all of them do. Quite cool.
Video 1.
FPGA Architecture of the Quantum Control System by Keysight (2022)
Source. They actually have a dedicated quantum team! Cool.
Video 2.
FPGA based servo system by Atoms & Laser (2018)
Source. The Indian lady is hardcore.
Someone attempted a Wikipedia page apparently: en.wikipedia.org/wiki/Q-CTRL. Nice try, nice try.
When you fail a HR interview, then you know you've reached rock bottom.
Investments:
Video 1.
The Operating System for Quantum Computing by Steve Brierley (2021)
Source. Founding CEO. He seems nice. You might as well just start watching at: youtu.be/ugzWnw1LTBE?t=1166 where more specific things start to come out.
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.

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