Quantum Cryptanalysis on Real Hardware: Pushing Symmetric-Structure Key Recovery Beyond the Published Frontier
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fp8 of DARWIN 398b BF16?
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#1 opened 4 days ago
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hfmefi69
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🔓 We ran genuine quantum key-recovery on 'real IBM quantum hardware' — and pushed the frontier well past the largest hardware demos we're aware of (which sat at N=4).
Using Simon's algorithm on
• Even–Mansour — N=5 → N=10
• 3-round Feistel (DES-family) — block 6 → 8
Each verified against an 'independent control key', using error mitigation only (no QEC).
🧭 Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.
📄 Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
🕹️ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
🏆 Leaderboard: FINAL-Bench/quantum-bench-leaderboard
#quantum #cryptography #quantumcomputing
Using Simon's algorithm on
ibm_kingston, we recovered the secret key of two symmetric-cipher structures:• Even–Mansour — N=5 → N=10
• 3-round Feistel (DES-family) — block 6 → 8
Each verified against an 'independent control key', using error mitigation only (no QEC).
🧭 Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.
📄 Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
🕹️ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
🏆 Leaderboard: FINAL-Bench/quantum-bench-leaderboard
#quantum #cryptography #quantumcomputing
SeaWolf-AI
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Quantum Cryptanalysis on Real Hardware: Pushing Symmetric-Structure Key Recovery Beyond the Published Frontier
FINAL-Bench
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🚀 Adding a GPU without building one
AI is usually framed as "how smart is the model / how many GPUs did you buy." The real bottleneck is elsewhere — how efficiently you use the GPUs you already have.
Training happens once; inference runs the entire time users use your product. So a service's economics come down to cost per token. Inference acceleration uses software to pull several times more out of the same GPU — the effect of plugging in one more "virtual GPU."
VIDRAFT's VKAE, measured (B200, same-harness, no quality loss):
Qwen3.5-35B-A3B (MoE): 25.7 → 601 tok/s (23.4×)
Darwin-36B-Opus (in-house MoE): 25.0 → 280.8 (11.2×)
10,000+ tok/s peak aggregate under concurrency
The key: it's reproducible — model + serving shipped as one container.
docker pull vidraft/qwen35-vkae:601
Don't take our word for it — run it yourself. The mechanism will be released as a paper.
🏆 Leaderboard & demo 👉 VIDraft/vkae
Articles 👉 https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard
AI is usually framed as "how smart is the model / how many GPUs did you buy." The real bottleneck is elsewhere — how efficiently you use the GPUs you already have.
Training happens once; inference runs the entire time users use your product. So a service's economics come down to cost per token. Inference acceleration uses software to pull several times more out of the same GPU — the effect of plugging in one more "virtual GPU."
VIDRAFT's VKAE, measured (B200, same-harness, no quality loss):
Qwen3.5-35B-A3B (MoE): 25.7 → 601 tok/s (23.4×)
Darwin-36B-Opus (in-house MoE): 25.0 → 280.8 (11.2×)
10,000+ tok/s peak aggregate under concurrency
The key: it's reproducible — model + serving shipped as one container.
docker pull vidraft/qwen35-vkae:601
Don't take our word for it — run it yourself. The mechanism will be released as a paper.
🏆 Leaderboard & demo 👉 VIDraft/vkae
Articles 👉 https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard
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Adding a GPU Without Building One
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