Scalable Quantum Error Detection via Tensor-Network Statevector Sampling

Quantum circuit simulation under realistic noise remains a bottleneck for validating NISQ-era quantum hardware. We introduce a tensor-network statevector sampling framework utilizing NVIDIA cuQuantum to simulate and detect stochastic Pauli errors across parameterized quantum circuits. By employing statistical syndrome measurement techniques, our method pinpoints single- and two-qubit gate errors with reduced measurement overhead, providing a scalable pathway for quantum error correction (QEC) verification.