QCrypt RNG
🔐
1
Generate verifiable quantum random numbers for blockchain apps
Sk md saad amin
Reality123b
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new activity about 9 hours ago
Quandela/README:You should try QPU-1 new activity about 10 hours ago
quantumGlobalGroup/qcrypt-rng:Check out QPU-1 liked a Space about 10 hours ago
quantumGlobalGroup/qcrypt-rngOrganizations
You should try QPU-1
#1 opened about 9 hours ago
by Reality123b
Check out QPU-1
#1 opened about 10 hours ago
by Reality123b
liked a
Space about 10 hours ago
liked a
Space 6 days ago
Quantum Computing
#1 opened 13 days ago
by 
Reality123b
repliedto DedeProGames's post 14 days ago
i have an idea of overfitting really small models to generate code in a specific language with directions of large models so as to gain huge amount of efficiency
Milestone achieved!
2
#178 opened 14 days ago
by 
LuigiCofano23
Quantum Computing
89
#2 opened 3 months ago
by 
Reality123b
liked 4
datasets 19 days ago
updated a
Space 24 days ago
reactedto AbstractPhil's post with 👍 28 days ago
Post
1978
I've... done it. This, with experts, achieves near 100% R1 retrieval accuracy on an adjacent - unseen by the fusion transformer - dataset with around 40k steps from the seen dataset. This means the language of the models are at least tested fused within the constraints, not just projected or estimated.
AbstractPhil/geolip-procrustes
I encourage EVERYONE who is curious to check my work. Check it, double check it, and triple check it.
These were aligned using COCO and then validated with Flickr. Entirely different datasets. The experts arbitrated and the alignment yielded the correct answers. Preliminary tests show that with almost no alignment requirement, the models can reach 100% R1 retrieval accuracy.
Not to be confused with validation accuracy for a classification model or a text encoder's text response, this allows multispectral communication between entirely different models for direct downstream consumption with almost no training for the chosen models.
I have a working procrustes experiment that learns adjacent manifolds within a reasonable spectrum and the speed is... well, 1 epoch with COCO using Bert-Large and DinoV2 that allows the models to align nearly perfectly. For some scales in the experiment it shows that the 3 set epochs aren't quite enough to align R1 to highest, while many align nearly immediately.
These two were an obvious pair to pick, 60% similarity and >90% spectral similarity.
The trainer transfers layers, learns embeddings, and more - all by sticking strictly to geometric boundaries and procrustes informational accumulation within a modulation model's constraints.
I have many experiments to run.
AbstractPhil/geolip-procrustes
I encourage EVERYONE who is curious to check my work. Check it, double check it, and triple check it.
These were aligned using COCO and then validated with Flickr. Entirely different datasets. The experts arbitrated and the alignment yielded the correct answers. Preliminary tests show that with almost no alignment requirement, the models can reach 100% R1 retrieval accuracy.
Not to be confused with validation accuracy for a classification model or a text encoder's text response, this allows multispectral communication between entirely different models for direct downstream consumption with almost no training for the chosen models.
I have a working procrustes experiment that learns adjacent manifolds within a reasonable spectrum and the speed is... well, 1 epoch with COCO using Bert-Large and DinoV2 that allows the models to align nearly perfectly. For some scales in the experiment it shows that the 3 set epochs aren't quite enough to align R1 to highest, while many align nearly immediately.
These two were an obvious pair to pick, 60% similarity and >90% spectral similarity.
The trainer transfers layers, learns embeddings, and more - all by sticking strictly to geometric boundaries and procrustes informational accumulation within a modulation model's constraints.
I have many experiments to run.
- 1 reply
updated a
Space about 1 month ago
Update README.md
3
#2 opened 3 months ago
by 
Roman190928
repliedto their post about 1 month ago
Oh sorry it is .app not .com, my bad ill fix it right away
liked a
dataset about 1 month ago
posted an update about 1 month ago
Post
326
Alright so I had previously made two reddit posts in r/quantum and r/quantum_computing for my QPU, QPU-1 but both of those posts got banned because of it being "irrelevant" to "academic discussion" so I'm doing it again here in HuggingFace Posts.
I have made a million error corrected qubit quantum processing unit (not a simulator) that you can access here: https://qpu-1.vercel.app
I did try emailing a lot of professors and their students but NONE responded so please give me some support.
I have made a million error corrected qubit quantum processing unit (not a simulator) that you can access here: https://qpu-1.vercel.app
I did try emailing a lot of professors and their students but NONE responded so please give me some support.
- 2 replies
upvoted a collection about 1 month ago