qumat-qdp 0.2.0rc2

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qumat-qdp

GPU-accelerated quantum state encoding for Apache Mahout Qumat.

Installation

pip install qumat[qdp]

Requires one of:

• NVIDIA GPU (CUDA path via QdpEngine)
• AMD GPU with ROCm (AMD path via QdpEngine(backend="amd"))

Recommended environment setup:

python -m venv .venv
source .venv/bin/activate

# Install the GPU runtime for your platform first:
# - NVIDIA users: CUDA-compatible torch / triton
# - AMD users: ROCm-compatible torch / triton

uv sync --active --project qdp/qdp-python --group dev

Use --active so uv reuses the environment that already has the correct GPU runtime stack.

Usage

import qumat.qdp as qdp
import torch

# Initialize the unified QDP engine on GPU 0.
# Choose the backend explicitly.
engine = qdp.QdpEngine(device_id=0, backend="cuda")

# Encode data into quantum state
qtensor = engine.encode([1.0, 2.0, 3.0, 4.0], num_qubits=2, encoding_method="amplitude")

# Zero-copy transfer to PyTorch
tensor = torch.from_dlpack(qtensor)
print(tensor)  # Complex tensor on CUDA

AMD ROCm Usage

import qumat.qdp as qdp
import torch

# Unified AMD engine route
engine = qdp.QdpEngine(device_id=0, precision="float32", backend="amd")
qt = engine.encode(torch.randn(8, 4, device="cuda"), 2, "amplitude")
state = torch.from_dlpack(qt)
print(state.device, state.dtype)  # cuda:0, complex64

The public QdpEngine is a unified Python facade with explicit backend selection:

• backend="cuda" routes to the Rust _qdp.QdpEngine
• backend="amd" routes to the Triton AMD engine directly

See qdp/qdp-python/TRITON_AMD_BACKEND.md for Triton AMD setup and validation details.

Encoding Methods

Method	Description
amplitude	Normalize input as quantum amplitudes
angle	Map values to rotation angles (one per qubit)
basis	Encode integer as computational basis state
iqp	IQP-style encoding with full ZZ entanglement
iqp-z	IQP encoding with Z-only diagonal (no ZZ pairs)
phase	Per-qubit phase product state via H⊗P(x_k)

Backend support boundary:

• CUDA (QdpEngine): amplitude, angle, basis, iqp, iqp-z, phase
  • phase is currently only reachable on the CUDA path via host inputs (Python list / NumPy / file / CPU torch tensor). The Python extension's CUDA-tensor validation does not yet allowlist phase; cuda-resident torch tensors must use .cpu() first when targeting phase. Tracked as a follow-up.
• AMD (QdpEngine(..., backend="amd")): amplitude, angle, basis, iqp, iqp-z, phase

Pipeline / loader dtype (Rust internals)

QuantumDataLoader and run_throughput_pipeline build a Rust PipelineConfig with an encoding plus a dtype (float32 vs float64). The prefetch thread can only keep an end-to-end float32 host batch for encodings whose GPU stack implements the batch f32 path (encode_batch_f32). Today that is amplitude only. Angle and basis still fall back to float64 for that loop until their batch f32 implementations exist. The eventual full matrix (e.g. angle/basis under supports_f32 once kernels are wired) is broader than what the pipeline uses today.

Input Sources

# Python list
qtensor = engine.encode([1.0, 2.0, 3.0, 4.0], 2, "amplitude")

# NumPy array
qtensor = engine.encode(np.array([[1, 2, 3, 4], [4, 3, 2, 1]]), 2, "amplitude")

# PyTorch tensor (CPU or CUDA)
qtensor = engine.encode(torch.tensor([1.0, 2.0, 3.0, 4.0]), 2, "amplitude")

# File formats
qtensor = engine.encode("data.parquet", 10, "amplitude")
qtensor = engine.encode("data.arrow", 10, "amplitude")
qtensor = engine.encode("data.npy", 10, "amplitude")
qtensor = engine.encode("data.pt", 10, "amplitude")

# Remote object storage URLs (requires building with remote-io feature)
qtensor = engine.encode("s3://my-bucket/data.parquet", 10, "amplitude")
qtensor = engine.encode("gs://my-bucket/data.parquet", 10, "amplitude")

Links

• Documentation
• GitHub
• Qumat Package

License

Apache License 2.0