qector-decoder

Introduction

QECTOR Decoder v3 is a production-quality library designed for quantum error correction decoding, implemented in both Python and Rust. It supports a wide variety of decoder families, facilitating efficient error correction in quantum computing applications.

Key Features

• Multiple Decoder Families: Offers over 25 decoder families, including:
  • MWPM (Maximum Weight Perfect Matching) using Blossom
  • Union-Find
  • Belief-Matching
  • BP-OSD (Belief Propagation with Ordered Statistical Decoding)
  • LDPC/qLDPC support
• Batch Decoding: Efficiently process multiple decoding tasks in parallel using both CPU and CUDA/GPU implementations, catering to high throughput needs.
• AutoDecoder: A 7-tier self-debugging fallback engine that ensures decoding continues even in the event of hardware failures.
• License Verification: Uses Ed25519 cryptographic signature verification for validating commercial licenses, ensuring secure and reliable usage.

Installation

For Python environments, installation is straightforward via pip:

pip install qector-decoder-v3

Usage Examples

Basic Decoding

To illustrate its functionality, here is a quick example of how to utilize the Union-Find and Blossom decoder types:

import numpy as np
from qector_decoder_v3 import UnionFindDecoder, BlossomDecoder

check_to_qubits = [[0, 1], [1, 2], [2, 3], [3, 4]]
n_qubits = 5
syndrome = np.array([0, 1, 0, 0], dtype=np.uint8)

# Using Union-Find Decoder
fast = UnionFindDecoder(check_to_qubits, n_qubits)
print(fast.decode(syndrome))

# Using Blossom Decoder
mwpm = BlossomDecoder(check_to_qubits, n_qubits)
print(mwpm.decode(syndrome))

Batch Decoding with GPU Acceleration

For scenarios requiring batch decoding, both CPU and GPU options are available:

from qector_decoder_v3 import BatchDecoder, CUDABatchDecoder

checks = [[0, 1], [1, 2], [2, 3], [3, 4]]
syndromes = np.random.randint(0, 2, size=(4096, 4), dtype=np.uint8)

# CPU Batch Decoding
cpu = BatchDecoder(checks, n_qubits=5)
corrections = cpu.parallel_batch_decode(syndromes)

# GPU Batch Decoding
if CUDABatchDecoder.is_available():
    gpu = CUDABatchDecoder(checks, n_qubits=5)
    corrections = gpu.batch_decode(syndromes)

Advanced Features

• Batch Decoding Accuracy: Weighted decoders can manage accuracy trade-offs when processing large data sets. Pass edge_weights to ensure that the GPU kernels produce reliable outputs for specific applications.
• Integration with Sinter and Other Tools: Supports synergy with tools like Sinter for enhanced workflows in quantum error correction contexts.

Conclusion

The QECTOR Decoder is robust, flexible, and designed to meet the demands of modern quantum error correction applications. With features like advanced batch processing, various decoding options, and automatic fallback mechanisms, it significantly enhances the efficiency of quantum computing error management.