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liked a model 11 days ago
FINAL-Bench/Darwin-398B-JGOS
upvoted an article 12 days ago
FINAL-Bench Quantum: An Open, Neutral Benchmark for Quantum-Computing Methods
reacted to SeaWolf-AI's post with ❤️ 12 days ago
🚀 Introducing FINAL-Bench Quantum — an open, neutral benchmark that finally puts quantum-computing methods on one fair yardstick. Quantum results are notoriously hard to compare. The same "logical error rate" or "query fidelity" means very different things depending on the code, noise model, hardware, and shot count. FINAL-Bench Quantum fixes that: five events judged under identical, published protocols, where every number is labeled as either measured here or quoted from a source. Five events: ① QEC Decoder ② Optimization (Max-Cut) ③ VQE ④ QRAM ⑤ Quantum Simulation The rules are simple and strict: ✅ Track A (measured here, with 95% confidence intervals) is kept separate from Track B (quoted from papers, not directly comparable). 🔬 Simulation and real hardware are clearly distinguished, and no quantum-advantage claims are made. 🌍 Methods from Google, IBM, NVIDIA, USTC, Riverlane and more sit side by side, with origin flags and author credits. 📤 Anyone can submit their own method via the Submit tab for review and listing. Already on the board: real IBM Heron r2 measurements (repetition-code distance boundary, 29–175× error reduction from d3 to d5), a real-chip QRAM query fidelity of 0.92, and H₂ VQE at chemical accuracy — always labeled honestly as simulation vs hardware. A leaderboard is only useful if you can trust it, so neutrality is the whole point: strong competitors stay in even when they beat the host, sources are quoted faithfully, and a simulation is never rounded up into a hardware claim. Leaderboard: https://huggingface.co/spaces/FINAL-Bench/quantum-bench-leaderboard Article: https://huggingface.co/blog/FINAL-Bench/quantum-leaderboard #quantum #QEC #QuantumComputing #benchmark
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