convert -size 512x512 xc:blue blue.pngConda is like pip, except that it also manages shared library dependencies, including providing prebuilts.
This has made Conda very popular in the deep learning community around 2020, where using Python frontends like PyTorch to configure faster precompiled backends was extremely common.
It also means that it is a full package manager and extremely overbloated and blows up all the time. People should just use Docker instead for that kind of stuff: www.reddit.com/r/learnmachinelearning/comments/kd88p8/comment/keco07k/
You also have to buy a license to use their repos if you are part of a large-enough organization: stackoverflow.com/questions/74762863/are-conda-miniconda-and-anaconda-free-to-use-and-open-source
Term invented by Ciro Santilli, similar to "nuclear blues", and used to describe the feeling that every little shitty job you are doing (that does not considerably help achieving AGI) is completely pointless given that we are likely close to AGI as of 2023.
en.bitcoin.it/wiki/Jercos mentions:www.bitcoinwhoswho.com/jercosinterview is the source. Persumably the contact was initiated via the private messaging feature of the Bitcoin Forum.
According to jercos the transaction was finalized over IRC chats. Jercos was 18 at the time of the transaction.
Bibliography:
en.bitcoin.it/wiki/Jercos
en.bitcoin.it/wiki/Jercos
By default, LSF only sends you an email with the stdout and stderr included in it, and does not show or store anything locally.
One option to store things locally is to use:as documented at:
bsub -oo stdout.log -eo stderr.log 'echo myout; echo myerr 1>&2'Or to use files with the job id in them:
bsub -oo %J.out -eo %J.err 'echo myout; echo myerr 1>&2'By default as mentioned at:
bsub -oo:To get just the stdout to the file, use
bsub -N -oo which:- stores only stdout on the file
- re-enables the completion email
Another option is to run with the bsub This immediately prints stdout and stderr to the terminal.
-I option:bsub -I 'echo a;sleep 1;echo b;sleep 1;echo c'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
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