[QDP] Pr2 batch throughput opt

[aloha1357]

Related Issues

related #1385

Changes

• Bug fix
• New feature
• Refactoring
• Documentation
• Test
• CI/CD pipeline
• Other

Why

As part of the IQP Encoding Optimization PR Split Plan, PR 2 focuses on "Batch throughput optimization" and lays the structural groundwork for Tensor Core (TC) acceleration (which will be fully introduced in PR 5 & 6).

Architectural Philosophy: Dual-Path Explicit Opt-in
It is crucial to note that these new Tensor Core optimizations do not automatically replace or override the existing standard algorithms. We are adopting a Dual-Path Architecture:

1. Standard Path (encode_batch): The original, hardware-agnostic FP64 FWT path is fully preserved. This ensures that users on older hardware (without Tensor Cores) or those requiring strict IEEE 754 standard FP64 behavior without any mixed-precision artifacts can continue running unmodified.
2. Tensor Core Path (encode_batch_tc): This is a new, highly specialized API path introduced here. Because Tensor Cores utilize INT8 mixed-precision arithmetic (compensated via the Chinese Remainder Theorem later in PR 6), there are microscopic floating-point differences. In HPC and quantum simulation, auto-dispatching to mixed-precision can cause difficult-to-debug numerical artifacts. Therefore, the TC pipeline is strictly an explicit opt-in for advanced users seeking maximum throughput on supported hardware (Turing/Ampere/Hopper).

To prepare for this encode_batch_tc pipeline, we need a robust scaffolding for batch data transformation. The original code processed matrices sequentially; this refactoring introduces batched layouts and kernels required for the Kronecker-based matrix multiplication that Tensor Cores will eventually execute.

How

• Created iqp_tc.cu: Introduced new kernels specifically designed to manage memory layout for batched operations.
• Phase Split Kernel (iqp_phase_split_kernel): Unrolls the batch and splits the initial phase computation into pure real and imaginary parts to prepare for INT8 matrix multiplication.
• Batch Transpose Kernel (iqp_tc_batch_transpose_kernel): Implemented a Shared Memory Bank-Conflict-Free matrix transpose kernel, essential for efficiently reordering data between Tensor Core FWT stages.
• Recombine Kernel (recombine_complex_kernel): Restores the split real and imaginary parts back into the standard cuDoubleComplex format expected by downstream processes.
• Rust Integration: Updated lib.rs and iqp.rs to expose and call the new launch_iqp_encode_tc function from Rust, laying the structural groundwork for the full Tensor Core pipeline.

Checklist

• Added or updated unit tests for all changes (Verified that existing tests pass, and batching logic doesn't break qdp-core)
• Added or updated documentation for all changes (Added explicit comments describing the purpose of the new kernels)