MQT Bench
PulseAugur coverage of MQT Bench — every cluster mentioning MQT Bench across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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Machine learning optimizes quantum circuits, reducing gates by up to 95%
Researchers have developed an automated method for optimizing quantum circuits using machine learning models. By analyzing thousands of circuits from the MQT Bench suite, they trained a predictive model to select the mo…
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New VQCSim framework accelerates quantum-classical ML training
Researchers have developed VQCSim, a new statevector simulation framework designed for hybrid quantum-classical machine learning workflows. This PyTorch-native system optimizes the execution of parametrized circuits by …
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Graph RL router boosts quantum circuit fidelity using calibration data
Researchers have developed a new quantum circuit routing method using graph reinforcement learning that incorporates calibration data from quantum processors. This approach, trained with proximal policy optimization and…
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Quantum Circuit Routing Enhanced by Calibration-Aware Reinforcement Learning
Researchers have developed a novel routing method for quantum circuits that incorporates calibration data to improve fidelity. This graph reinforcement learning approach uses same-day calibration information from IBM He…