Skip to main content

An implementation of ILP decoders for quantum error correction.

Project description

ILP Decoder

An implementation of the ILP decoder for circuit-level noise.

Included is a ilp_decoder.ILPDecoder class that can be configured using a stim.DetectorErrorModel and decodes shot data, directly outputting predicted observables (without sinter), as well as a ilp_decoder.ILPSinterDecoder class, which subclasses sinter.Decoder, for interfacing with sinter.

Installation

To install from pypi, run:

pip install ilp_decoder

To install from source, run:

pip install -e .

from the root directory.

Usage

Here is an example of how the decoder can be used directly with Stim:

import stim
import numpy as np
from ilp_decoder import ILPDecoder

num_shots = 100
d = 3
p = 0.001
circuit = stim.Circuit.generated(
    "surface_code:rotated_memory_x",
    rounds=d,
    distance=d,
    before_round_data_depolarization=p,
    before_measure_flip_probability=p,
    after_reset_flip_probability=p,
    after_clifford_depolarization=p
)

sampler = circuit.compile_detector_sampler()
shots, observables = sampler.sample(num_shots, separate_observables=True)

decoder = ILPDecoder.from_circuit(circuit)

predicted_observables = decoder.decode_batch(shots)
num_mistakes = np.sum(np.any(predicted_observables != observables, axis=1))

print(f"{num_mistakes}/{num_shots}")

Sinter integration

To integrate with sinter, you can use the ilp_decoder.ILPSinterDecoder class, which inherits from sinter.Decoder. To use it, you can use the custom_decoders argument when using sinter.collect:

import sinter
from ilp_decoder import ILPSinterDecoder, sinter_decoders

samples = sinter.collect(
    num_workers=4,
    max_shots=1_000_000,
    max_errors=1000,
    tasks=generate_example_tasks(),
    decoders=['ilp'],
    custom_decoders=sinter_decoders()
    print_progress=True,
)

Advanced ILP solvers

You can use whatever ILP solver supported by cvxpy instead of the default one highs, though highs should be one of the fastest open-source solver available. If you have access to gurobi, you may gain some extra speed boost (not tested).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ilp_decoder-0.2.1.tar.gz (71.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ilp_decoder-0.2.1-py3-none-any.whl (12.8 kB view details)

Uploaded Python 3

File details

Details for the file ilp_decoder-0.2.1.tar.gz.

File metadata

  • Download URL: ilp_decoder-0.2.1.tar.gz
  • Upload date:
  • Size: 71.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.6.10

File hashes

Hashes for ilp_decoder-0.2.1.tar.gz
Algorithm Hash digest
SHA256 7f6602bde892768aaca46b3c24efaff1f76df94c4535a71cec33da5c03e0ede3
MD5 6fab51e5baeb1ab7260fb4d3d896249f
BLAKE2b-256 41314bad0487c06fd4ea2ac0dff550a978e5c1c3d365ff674bb244ec0fdc28e9

See more details on using hashes here.

File details

Details for the file ilp_decoder-0.2.1-py3-none-any.whl.

File metadata

File hashes

Hashes for ilp_decoder-0.2.1-py3-none-any.whl
Algorithm Hash digest
SHA256 fa9591af44dec8af9253ffcc21cc4d53d2e1c46be3a50bd5bd9efaab1f04482e
MD5 3f7bee46900af836bc2fbc980686f476
BLAKE2b-256 83bbb534d497e4fda2a62925135a6fcdb0349cabf9fe38b109ec5e8d5c9981d5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page