[QDP] refactor: introduce 2 traits for flexible io type w/ example

[ryankert01]

Refactor QDP to Support Multiple Input Types

Problem

In QDP, we had/want to support multiple input types (we now support parquet/arrow ipc, we want to add more like numpy, torch). The solution needed to:

1. Make it relatively easy to add more input types
2. Not sacrifice speed or memory

Created a flexible, trait-based system that achieves all goals:

Core Architecture

• DataReader trait: Basic batch reading interface
  • read_batch()
  • get_sample_size()
  • get_num_samples()
• StreamingDataReader trait: Advanced streaming for large files
  • read_chunk()
  • total_rows()
• Format implementations: Parquet (batch + streaming), Arrow IPC (batch), NumPy (batch)
• Placeholders: PyTorch (with implementation guide)

Zero Performance Impact

• Static dispatch: No virtual function overhead
• Memory efficient: Maintains streaming (O(1) memory for any file size)
• Zero-copy: Direct buffer access where possible (NumPy uses into_raw_vec_and_offset)
• Benchmarks: Same performance as before refactoring + new NumPy benchmark

Benchmark

python benchmark_numpy_io.py --qubits 16 --samples 1000

For reviewer

qdp/docs/readers/README.md

[400Ping]

Nice PR, I could help with the PyTorch implementation part.

[ryankert01]

PTAL @guan404ming @rich7420 @400Ping