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A minimalist and organized academic archive in the fields of artificial intelligence and quantum computing.
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Google & MIT Break Through with Shor Algorithm Variant: Qubit Requirements Reduced by 40%, RSA-2048 in Danger
A joint research team between Google Quantum AI and MIT published a new variant of Shor's algorithm in Nature, reducing the qubit requirement for breaking RSA-2048 by 40%—from 20 million to approximately 12 million physical qubits.
DeepMind Unveils Gemini Ultra 2.0: First AI System to Achieve Human-Level Performance on 87% of Academic Benchmarks
Google DeepMind released Gemini Ultra 2.0, achieving human-level or superhuman performance on 87% of academic benchmarks—including 94.2% on MMLU, 67.3% on GPQA, and 86.5% on MATH—powered by a new 'Mixture of Reasoning Experts' architecture.
Google Quantum AI & DeepMind: First Demonstration of Quantum Advantage for Machine Learning Task
A Google Quantum AI and DeepMind collaboration demonstrated the first concrete quantum advantage for a practical machine learning task—molecular property prediction—with 15% better accuracy than classical approaches using only 2% as much training data.
Microsoft & Quantinuum Unveil "Flock of Qubits": Error Rates Reduced by 800x, Logical Qubits Become Practical
Microsoft and Quantinuum jointly announced a new error correction architecture called 'Flock of Qubits,' achieving error rates 800 times lower than underlying physical qubits while using just 30 physical qubits to create four logical qubits.
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Quantum Optimization: Finding the Best Solution in a World of Possibilities
Optimization problems underlie logistics, finance, drug discovery, and more. Quantum approaches like annealing and QAOA are already delivering real-world speedups.
Quantum Neural Networks: Building Brain-Inspired Models with Qubits
Quantum neural networks adapt the architecture of deep learning to quantum hardware. They face unique challenges—but a 2026 breakthrough on 100 qubits points the way forward.
Quantum Machine Learning: When Quantum Physics Meets Artificial Intelligence
Quantum machine learning combines quantum computing with AI to tackle problems beyond classical reach. The first practical demonstrations of advantage are emerging.
Reinforcement Learning: Teaching Machines Through Trial and Error
Instead of labeled data, reinforcement learning agents learn by interacting with an environment and collecting rewards. It's how AlphaGo mastered Go and robots learn to walk.