In the context of mathematics, particularly in the fields of algebra and number theory, a **multiplicatively closed set** is a subset of a given set that is closed under the operation of multiplication. This means that if you take any two elements from this set and multiply them together, the result will also be an element of the set. Formally, let \( S \) be a set.
Funding:
- 2023-01-23 €5 Million
Other good lists:
- quantumcomputingreport.com/resources/tools/ is hard to beat as usual.
- www.quantiki.org/wiki/list-qc-simulators
- JavaScript
- algassert.com/quirk demo: github.com/Strilanc/Quirk drag-and-drop, by a 2019-quantum-computing-Googler, impressive. You can create gates. State store in URL.
- github.com/stewdio/q.js/ demo: quantumjavascript.app/
Bibliography:
- www.epcc.ed.ac.uk/whats-happening/articles/energy-efficient-quantum-computing-simulations mentions two types of quantum computer simulation:
The most common approach to quantum simulations is to store the whole state in memory and to modify it with gates in a given order
However, there is a completely different approach that can sometimes eliminate this issue - tensor networks
The official hello world is documented at: qiskit.org/documentation/intro_tutorial1.html and contains a Bell state circuit.
Our version at qiskit/hello.py.
Sample program output,
counts are randomized each time.First we take the quantum state vector immediately after the input.We understand that the first element of
input:
state:
Statevector([1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
dims=(2, 2))
probs:
[1. 0. 0. 0.]Statevector is , and has probability of 1.0.Next we take the state after a Hadamard gate on the first qubit:We now understand that the second element of the
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. ]Statevector is , and now we have a 50/50 propabability split for the first bit.Then we apply the CNOT gate:which leaves us with the final .
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]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];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:which we should all be able to understand intuitively given our understanding of the CNOT gate and quantum state vectors.
┌──────────────────────┐
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. ]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.QFTOutput:So this also serves as a more interesting example of quantum compilation, mapping the
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))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:and the output of that approximately:which can be defined simply as the normalized DFT of the input quantum state vector.
[0, 1j/sqrt(2), 0, 0, 0, 0, 0, 1j/sqrt(2)]From this we see that the Quantum Fourier transform is equivalent to a direct discrete Fourier transform on the quantum state vector, related: physics.stackexchange.com/questions/110073/how-to-derive-quantum-fourier-transform-from-discrete-fourier-transform-dft
You get an error like this if you forget to call Related: quantumcomputing.stackexchange.com/questions/34396/aererror-unknown-instruction-c-unitary-while-using-control-unitary-operator/35132#35132
qiskit.transpile():qiskit_aer.aererror.AerError: 'unknown instruction: QFT'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.
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.
FPGA Architecture of the Quantum Control System by Keysight (2022)
Source. They actually have a dedicated quantum team! Cool.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.
As en.wikipedia.org/w/index.php?title=ZX-calculus&oldid=1071329204#Diagram_rewriting tries to explain but fails to deliver as usual consider the GHZ state represented as a quantum circuit.
The naive way would be to just do the matrix multiplication as explained at Section "Quantum computing is just matrix multiplication".
However, ZX-calculus provides a simpler way.
And even more importantly, sometimes it is the only way, because in a real circuit, we would not be able to do the matrix multiplication
This is always possible, because we can describe how to do the conversion simply for any of the Clifford plus T gates, which is a set of universal quantum gates.
Then, after we do this transformation, we can start applying further transformations that simplify the circuit.
It has already been proven that there is no efficient algorithm for this (TODO source, someone said P-sharp complete best case)
But it has been proven in 2017 that any possible equivalence between quantum circuits can be reached by modifying ZX-calculus circuits.
There are only 7 transformation rules that we need, and all others can be derived from those, universality.
So, we can apply those rules to do the transformation shown in Wikipedia:
and one of those rules finally tells us that that last graph means our desired state:because it is a Z spider with and .
Working with PyZX by Aleks Kissinger (2019)
Source. This video appears to give amazing motivation on why you should care about ZX-calculus, it mentions Pinned article: Introduction to the OurBigBook Project
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