QRisk isolates backend-specific abnormal error patterns on NISQ devices via delta debugging and mitigates them with commuting gate swaps, cutting excess noise by 24-45% on IBM backends where noise models predict no difference.
Astra: Exploiting predictability to optimize deep learning
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MIST is a new simulator for heterogeneous multi-stage LLM inference that combines hardware traces with analytical models to explore configuration trade-offs in hybrid CPU-accelerator systems.
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.
citing papers explorer
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Isolating Recurring Execution-Dependent Abnormal Patterns on NISQ Quantum Devices
QRisk isolates backend-specific abnormal error patterns on NISQ devices via delta debugging and mitigates them with commuting gate swaps, cutting excess noise by 24-45% on IBM backends where noise models predict no difference.
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MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference
MIST is a new simulator for heterogeneous multi-stage LLM inference that combines hardware traces with analytical models to explore configuration trade-offs in hybrid CPU-accelerator systems.
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Learning-Optimized Qubit Mapping and Reuse to Minimize Inter-Core Communication in Modular Quantum Architectures
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.