AI Engines enable larger low-latency neural networks for extreme-edge scientific computing on FPGAs than programmable logic, via a new latency-adjusted resource equivalence metric and tailored optimizations.
Low-latency machine learning fpga accelerator for multi- qubit-state discrimination
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Reservoir computing using polynomial features from measurement signals achieves up to 50% error reduction on single-qubit and 11% on five-qubit datasets with 100x fewer multiplications than neural networks while reducing crosstalk.
citing papers explorer
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Design Rules for Extreme-Edge Scientific Computing on AI Engines
AI Engines enable larger low-latency neural networks for extreme-edge scientific computing on FPGAs than programmable logic, via a new latency-adjusted resource equivalence metric and tailored optimizations.
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Superconducting Qubit Readout Using Next-Generation Reservoir Computing
Reservoir computing using polynomial features from measurement signals achieves up to 50% error reduction on single-qubit and 11% on five-qubit datasets with 100x fewer multiplications than neural networks while reducing crosstalk.