TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
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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.
citing papers explorer
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TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency
TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
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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.