Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.
Error resilience analysis for systematically employing approximate computing in con- volutional neural networks
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scaleTRIM is a truncation-based approximate integer multiplier using linearization and piecewise constant compensation that reports 15.2% better MRED and 22.8% better PDP than prior designs while maintaining usable accuracy in DNNs.
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Iceberg Beyond the Tip: Co-Compilation of a Quantum Error Detection Code and a Quantum Algorithm
Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.
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scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation
scaleTRIM is a truncation-based approximate integer multiplier using linearization and piecewise constant compensation that reports 15.2% better MRED and 22.8% better PDP than prior designs while maintaining usable accuracy in DNNs.