Skip to main content

Systematic realisation of quantum systems directly from transfer function.

Project description

SImBA - Systematic Inference of Bosonic quAntum systems

License: MIT Documentation Status

>>Documentation<<

Compute graphical quantum system representations from transfer functions

from simba import transfer_function_to_graph, tf2rss, adiabatically_eliminate
from sympy import symbols

s = symbols('s')
gamma_f, lmbda = symbols('gamma_f lambda', real=True, positive=True)
tf = (s**2 + s * gamma_f - lmbda) / (s**2 - s * gamma_f - lmbda)

transfer_function_to_graph(tf, 'active_coupled_cavity.png', layout='dot')
Active coupled cavity realisation

Calculate system transfer functions from the inferred system

split_network = tf2rss(tf).to_slh().split()
h_int = split_network.interaction_hamiltonian
h_int.expr.simplify()

Compute transfer functions between any degrees of freedom of the system

print(split_network.state_vector)
tfm = split_network.tfm
tf = tfm.open_loop('a_1', 'aout_1').simplify()
gamma_1, _ = split_network.aux_coupling_constants
adiabatically_eliminate(tf, gamma_1)

Installation

Install via pip install quantum-simba

See notebooks for examples.

To clone the dev environment run,

$ conda env create -f=environment.yml
$ conda activate simba

To install simba for development purposes,

$ pwd
... (simba code directory containing setup.py)
$ pip install -e .
(then to run test suite)
$ py.test

To build documentation locally,

$ pwd
... (simba code directory containing setup.py)
$ ./make_docs.sh

Motivation

Often when designing detectors for high-precision measurements, we are most interested in the frequency domain behaviour of such systems. For example, when computing the measurement shot noise of a gravitational wave detector, the noise spectrum is determined solely by the transfer function from the internal degree of freedom perturbed by the passing gravitational wave to the measurement output of the detector.

Until recently there has been no way to infer the layout of a quantum system directly from its transfer function, but now due to recent research in the quantum control community this is possible.

This software makes the process of finding a given physical realisation from the input-output transfer function completely automatic. Once this has been computed, it can then be used, for example, to deduce physical realisations, or more interestingly, to compute the most sensitive possible detector for a given numbers of internal degrees of freedom by minimizing the quantum Cramer-Rao bound.

TODO

  • Example notebooks
  • Write paper
  • Implement scattering matrix
  • Include examples on this page

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

quantum-simba-0.9.6.dev0.tar.gz (20.1 kB view details)

Uploaded Source

Built Distribution

quantum_simba-0.9.6.dev0-py3-none-any.whl (22.7 kB view details)

Uploaded Python 3

File details

Details for the file quantum-simba-0.9.6.dev0.tar.gz.

File metadata

  • Download URL: quantum-simba-0.9.6.dev0.tar.gz
  • Upload date:
  • Size: 20.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.40.2 CPython/3.7.3

File hashes

Hashes for quantum-simba-0.9.6.dev0.tar.gz
Algorithm Hash digest
SHA256 e3d0c8fc2757e64095b7133430faa278b527d89b960a4b34e559439cb44465fb
MD5 ea8362faeb08d937582358e4d2a3842c
BLAKE2b-256 8a2b32ad04ebb23e7bafa963f0af1ada8486871376d767cca0f7c5a3c0d68a14

See more details on using hashes here.

File details

Details for the file quantum_simba-0.9.6.dev0-py3-none-any.whl.

File metadata

  • Download URL: quantum_simba-0.9.6.dev0-py3-none-any.whl
  • Upload date:
  • Size: 22.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.40.2 CPython/3.7.3

File hashes

Hashes for quantum_simba-0.9.6.dev0-py3-none-any.whl
Algorithm Hash digest
SHA256 2e1d6ff8df292505c9a7f54f39857d2037c2f5017265fff298006e2f3e5d4115
MD5 6415e74daa93db0d01c10fe1da3b3495
BLAKE2b-256 fd6f5bc03f7fa7ec70c13131a06502c5146702dc74a24d484e03c93ab580dc86

See more details on using hashes here.

Supported by

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