Syn@fac optimization reduces estimated circuit failure probability by a factor of 9 on average across non-Clifford benchmarks for bivariate bicycle code modular FTQC architectures, with additional gains from transvection deferral and Clifford insertion.
MQT Bench: Bench- marking software and design automation tools for quantum computing,
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Family-conditioned residual neural network predicts approximation thresholds and runtimes for tensor-network quantum circuit simulation from OpenQASM, achieving 79.5% exact accuracy and R²=0.82.
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Assessing System Capabilities and Bottlenecks of an Early Fault-Tolerant Bicycle Architecture
Syn@fac optimization reduces estimated circuit failure probability by a factor of 9 on average across non-Clifford benchmarks for bivariate bicycle code modular FTQC architectures, with additional gains from transvection deferral and Clifford insertion.
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Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance
Family-conditioned residual neural network predicts approximation thresholds and runtimes for tensor-network quantum circuit simulation from OpenQASM, achieving 79.5% exact accuracy and R²=0.82.