Recent developments in quantum hardware, including the scaling of physical qubits and advanced quantum error correction techniques, have increased the number of reliable logical qubits. However, this progress has introduced new challenges for quantum algorithm developers. Limited access to physical quantum machines and the insufficient performance of classical quantum simulator for near-term scales (~30 logical qubits) hinder the simulation and validation of quantum algorithms. To address this urgent need for improving simulation performance, we propose a novel end-to-end full-stack solution for Schrödinger-style simulation that jointly explores algorithm, software and hardware optimizations. At the algorithmic level, by identifying the inefficiency in executing complex signed permutations and complex unitary permutation gates, we introduce multiple optimization techniques that significantly reduce data movement and computational complexity. At the hardware level, we propose a reconfigurable dataflow architecture with adaptive memory scheduling and swapping optimizations. At the software level, an end-to-end toolchain is introduced to jointly explore both algorithmic and hardware optimizations. A comprehensive evaluation across 16 quantum circuits demonstrates that our work achieves a maximum speedup of $53\times$ over GPU-based Qiskit implementations and $46\times$ over state-of-the-art customized simulators.