

Bryan Daugherty, CCI, CBI, SME
21.9K posts

@BWDaugherty
Ω = 24 | PQC | AI | Blockchain — Building trust infrastructure @SmartLedgerTech @OriginNeuralAI #DSC3 | @DoD_CSIAC SME | @NVIDIA Inception Partner






My brain broke after reading this paper 🤯! Quantum computing just took a serious step toward breaking classical AI limits. MIT Scientists just showed that small quantum computers can outperform EXPONENTIALLY larger classical systems on massive datasets. In a new study, researchers introduced a framework called Quantum Oracle Sketching, where instead of loading entire datasets, the system streams random classical samples, processes them in quantum superposition, and then discards them. This directly bypasses the biggest bottleneck in quantum machine learning, which is data loading 👀 The results are honestly kind of ridiculous. A system with fewer than 60 logical qubits can perform core machine learning tasks that would require exponentially larger classical memory, translating to roughly 10,000× to 1,000,000× smaller system size. They validated this on real data, including single-cell RNA sequencing and movie review sentiment analysis, and showed it works across fundamental tasks like classification, dimension reduction, and linear algebra. They also used Classical Shadows to efficiently extract useful outputs from quantum states, solving another long standing limitation. This matters because modern AI is basically brute force, more data, more GPUs, more cost.

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