HDL defines dynamic theories with lifting and combination operations, proves soundness and relative completeness in Isabelle, and demonstrates the approach on a Java controller steering a differential dynamic logic plant model.
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Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
A JAX-based framework extending quantum machine learning to pulse-level control with composable ansatzes, end-to-end optimization, and Fourier diagnostics.
Three scheduling strategies for hybrid quantum-HPC systems cut classical resource use by up to 64% or boost QPU utilization depending on workload balance, validated on real hardware.
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
XGBoost models trained on ≤16-qubit data predict eigensolver hyperparameters and reduce error by 0.12% on 28-qubit systems.
A synthesis of expert insights from the ADAC Quantum Computing Working Group and member survey on the complementary roles of quantum and classical high-performance computing in future hybrid infrastructures.
citing papers explorer
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Heterogeneous Dynamic Logic: Provability Modulo Program Theories
HDL defines dynamic theories with lifting and combination operations, proves soundness and relative completeness in Isabelle, and demonstrates the approach on a Java controller steering a differential dynamic logic plant model.
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Qurator: Scheduling Hybrid Quantum-Classical Workflows Across Heterogeneous Cloud Providers
Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
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Software Between Quantum and Machine Learning -- And Down to Pulses
A JAX-based framework extending quantum machine learning to pulse-level control with composable ansatzes, end-to-end optimization, and Fourier diagnostics.
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Three ways to share a QPU: Scheduling strategies for hybrid Quantum-HPC applications
Three scheduling strategies for hybrid quantum-HPC systems cut classical resource use by up to 64% or boost QPU utilization depending on workload balance, validated on real hardware.
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Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
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Accelerating Quantum Eigensolver Algorithms With Machine Learning
XGBoost models trained on ≤16-qubit data predict eigensolver hyperparameters and reduce error by 0.12% on 28-qubit systems.
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The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
A synthesis of expert insights from the ADAC Quantum Computing Working Group and member survey on the complementary roles of quantum and classical high-performance computing in future hybrid infrastructures.