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 following optional decoders, which are not included as dependencies by default:

• MWPF - through bloqade.decoders.MWPFDecoder (MWPF)
• Tesseract - through bloqade.decoders.TesseractDecoder (Tesseract)
• MLE (Gurobi) - through bloqade.decoders.GurobiDecoder, finds the most likely error pattern via mixed-integer programming
• MLD (Table Lookup) - through bloqade.decoders.TableDecoder, builds a lookup table from sampled data

Sinter-compatible adapters for MLE and MLD are also available through bloqade.decoders.sinter_interface.

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]

For MLE (a full Gurobi license is needed for larger problems, but small models work with the size-limited license bundled with gurobipy):

pip install bloqade-decoders[mle]

For MLD:

pip install bloqade-decoders[mld]

You can combine multiple extras:

pip install bloqade-decoders[mwpf, tesseract, mle, mld, sinter]

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]])

MLE / MLD Decoders

The GurobiDecoder takes a DEM directly (note: must use decompose_errors=False):

from bloqade.decoders import GurobiDecoder

decoder = GurobiDecoder(dem)
corrections = decoder.decode(syndromes)

The TableDecoder can be constructed directly with a DEM and pre-computed counts, or from a stim circuit which handles the sampling for you:

from bloqade.decoders import TableDecoder

# from a circuit (samples shots to build the lookup table)
decoder = TableDecoder.from_stim_circuit(circuit, num_shots=100_000)

# or from pre-sampled detector-observable shots
decoder = TableDecoder.from_det_obs_shots(dem, det_obs_shots)

corrections = decoder.decode(syndromes)