bloqade-decoders

The QEC user interface providing integration with popular open-source decoders for the Bloqade SDK.

By default, the following decoders from the ldpc package and their corresponding interfaces are immediately available for decoding use upon installation of this package:

• BP+OSD - through bloqade.decoders.BpOsdDecoder
• BP+LSD - through bloqade.decoders.BpLsdDecoder
• Belief Find - through bloqade.decoders.BeliefFindDecoder

Interfaces also exist for the Hypergraph Minimum-Weight Parity Factor MWPF decoder as well as the Tesseract decoder but the decoders themselves are not included as a dependency and are instead optional.

You can install them separately or specify you would like them included with the bloqade-decoders installation through the additional instructions below.

Installation

For access to the ldpc-package originating decoders and their respective interfaces, just do the following:

pip install bloqade-decoders

To add the tesseract decoder you can do:

pip install bloqade-decoders[tesseract]

Or for MWPF do:

pip install bloqade-decoders[mwpf]

and for both, you can do:

pip install bloqade-decoders[mwpf, tesseract]

Usage

The decoding interfaces are designed to align as closely as possible with the decoders themselves in terms of arguments. The only major difference is you're expected to pass in a Detector Error Model (DEM) to instantiate the interface.

Furthermore, all decoder interfaces are designed to accept the detector results of a single shot OR a batch of shots as a numpy ndarray of booleans, with the result being the observable correction (also as an ndarray of booleans).

from bloqade.decoders import BpOsdDecoder
import numpy as np
import stim

dem = stim.DetectorErrorModel("""
    error(0.1) D0
    error(0.1) D0 D1
    error(0.1) D1 L0
""")
# Pretend that circuit was executed twice,
# with two sets of detector results.
syndromes = np.array([[False, False], [False, True]])

# instantiate decoder, passing in desired arguments as you would
# the original decoder interface.
decoder = BpOsdDecoder(dem, bp_method="product_sum")

decoded_observable = decoder.decode(syndromes)
# decoded_observable should give you
# np.array([[False], [True]])