Ciro Santilli @cirosantilli 35

NumPy does not automatically use the GPU for it: stackoverflow.com/questions/49605231/does-numpy-automatically-detect-and-use-gpu, and PyTorch is one of the most notable compatible implementations, as it uses the same memory structure as NumPy arrays.

Sample runs on P51 to observe the GPU speedup:

$ time ./matmul.py g 10000 1000 10000 100
real    0m22.980s
user    0m22.679s
sys     0m1.129s
$ time ./matmul.py c 10000 1000 10000 100
real    1m9.924s
user    4m16.213s
sys     0m17.293s

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PyTorch model by

Ciro Santilli 35  Updated 2025-03-28  +Created 1970-01-01

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torchvision by

Ciro Santilli 35  Updated 2025-03-28  +Created 1970-01-01

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Contains several computer vision models, e.g. ResNet, all of them including pre-trained versions on some dataset, which is quite sweet.

Documentation: pytorch.org/vision/stable/index.html

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HHL algorithm by

Ciro Santilli 35  Updated 2025-03-28  +Created 1970-01-01

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Organization developing quantum dot quantum computer by

Ciro Santilli 35  Updated 2025-03-28  +Created 1970-01-01

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QMUL research group by

Ciro Santilli 35  Updated 2025-03-28  +Created 1970-01-01

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ResNet v1 vs v1.5 by

Ciro Santilli 35  Updated 2025-03-28  +Created 1970-01-01

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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 small performance drawback (~5% imgs/sec).

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How many logical qubits are needed to run Shor's algorithm? by

Ciro Santilli 35  Updated 2025-03-28  +Created 1970-01-01

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quantumcomputing.stackexchange.com/questions/5048/how-many-logical-qubits-are-needed-to-run-shors-algorithm-efficiently-on-large

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Integer factorization algorithms better than Shor's algorithm by

Ciro Santilli 35  Updated 2025-03-28  +Created 1970-01-01

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2023 www.schneier.com/blog/archives/2023/01/breaking-rsa-with-a-quantum-computer.html comments on "Factoring integers with sublinear resources on a superconducting quantum processor” arxiv.org/pdf/2212.12372.pdf

A group of Chinese researchers have just published a paper claiming that they can—although they have not yet done so—break 2048-bit RSA. This is something to take seriously. It might not be correct, but it’s not obviously wrong.
We have long known from Shor’s algorithm that factoring with a quantum computer is easy. But it takes a big quantum computer, on the orders of millions of qbits, to factor anything resembling the key sizes we use today. What the researchers have done is combine classical lattice reduction factoring techniques with a quantum approximate optimization algorithm. This means that they only need a quantum computer with 372 qbits, which is well within what’s possible today. (The IBM Osprey is a 433-qbit quantum computer, for example. Others are on their way as well.)

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