To overcome the exponential memory bottleneck inherent in classical quantum simulation, we implement a heterogeneous parallelization architecture combining CPU memory capacity with NVIDIA H100 GPU computational throughput. The system leverages high-bandwidth tensor operations on NVIDIA H100 GPU while extending qubit capacity through distributed CPU memory allocation. This hybrid model enables simulation of larger Hilbert spaces while preserving performance-critical GPU execution paths.
Simulations were executed using a GPU-accelerated quantum backend (Qiskit Aer with Nvidia H100 CUDA/cuQuantum integration), achieving numerical state fidelities exceeding 0.9999998 over 100 Floquet cycles, validating the stability of syntrodynamic states. The resulting dynamics exhibit a π-spectral gap characteristic of robust time-crystalline order, with decoherence suppressed exponentially via frequency-domain encoding.