Skip to main content

Fast Approximate Block Encoding Circuits

Project description

Fast Approximate BLock Encodings (FABLE)

FABLE can synthesize quantum circuits for approximate block-encodings of matrices. A block-encoding is the embedding of a matrix in the leading block of of a larger unitary matrix.

FABLE is a quantum representation for dense, unstructured matrices. The gate complexity of FABLE circuits scales linear in the number of matrix elements, which is optimal for the unstructured case. FABLE includes a circuit compression algorithm that can significantly reduce the gate complexity and works particularly well if there is certain structure available in the matrix to be block encoded.

We provide two reference implementations of the FABLE algorithm:

  • a Python implementation built on top of Qiskit
  • a MATLAB implementation built on top of QCLAB

Qiskit - Python Implementation

FABLE can be installed from PyPI as follows:

pip install fable-circuits

After installation, it can be loaded and used as follows:

from fable import fable
import numpy as np
from qiskit import Aer
simulator = Aer.get_backend("unitary_simulator")


# generate a random matrix and block encode it
n = 3
N = 2**n
A = np.random.randn(N, N)
circ, alpha = fable(A, 0)
result = simulator.run(circ).result()
unitary = result.get_unitary(circ)
np.linalg.norm(alpha * N * unitary.data[0:N, 0:N] - A)/np.linalg.norm(A)

QCLAB - MATLAB Implementation

In order to run the MATLAB implementation of FABLE:

  1. Install QCLAB
  2. Clone FABLE and add fable-qclab directory to your MATLAB path.

After installation, FABLE can be run for a target matrix A as either:

logging = true ;
[circuit, OA, alpha, info] = fable( A, 'cutoff', 1e-4, logging ) ;
[circuit, OA, alpha, info] = fable( A, 'percentage', 80, logging ) ;

The first option ('cutoff') ignores coefficients smaller than 1e-4 in absolute value, the second option ('percentage') applies an 80% compression and only retains the 20% largest coefficients. The 'percentage' and logging options are only available in the MATLAB version of FABLE.

Reference

Cite the following reference for FABLE:

FABLE: Fast Approximate Quantum Circuits for Block-Encodings, Daan Camps, Roel Van Beeumen, 2022.

Developers - Lawrence Berkeley National Laboratory

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

fable-circuits-0.1.tar.gz (4.2 kB view details)

Uploaded Source

Built Distribution

fable_circuits-0.1-py3-none-any.whl (4.7 kB view details)

Uploaded Python 3

File details

Details for the file fable-circuits-0.1.tar.gz.

File metadata

  • Download URL: fable-circuits-0.1.tar.gz
  • Upload date:
  • Size: 4.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.7

File hashes

Hashes for fable-circuits-0.1.tar.gz
Algorithm Hash digest
SHA256 f6620ec9c629463d1b8e377527f9e85f430df3dcfa06faba9b87f4ab59861030
MD5 9ae185ce8aac25fc264a728d5d546bb9
BLAKE2b-256 2848b66ccf70dd488af98ae5ffd295cca292451ca00948d993e85045d808b73d

See more details on using hashes here.

File details

Details for the file fable_circuits-0.1-py3-none-any.whl.

File metadata

File hashes

Hashes for fable_circuits-0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 563ee320ebb3e5d4707ecae863878b9b0d7a5c3c9854b8b52eecab003755e33d
MD5 473f09ab6cc8d3cfde6ef928bdcc02cf
BLAKE2b-256 900a1642cd774715e97bd45b7cf1bb9e24bbb33cb002fbd5e623a9792737d398

See more details on using hashes here.

Supported by

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