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https://github.com/cqcl/phayes

Easy and efficient Bayesian quantum phase estimation

https://github.com/cqcl/phayes

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Repository

Easy and efficient Bayesian quantum phase estimation

Basic Info
  • Host: GitHub
  • Owner: CQCL
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 998 KB
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  • Stars: 4
  • Watchers: 3
  • Forks: 2
  • Open Issues: 4
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Created about 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License

README.md

phayes

phayes is a python package for easy and efficient quantum phase estimation.

Extensive details on Bayesian quantum phase estimation can be found in the accompanying paper, Yamamoto et al, 2023.

Quantum phase estimation Wiebe et al, 2015, O'Brien et al, 2018, van den Berg, 2021 (and quantum amplitude estimation Suzuki et al, 2019) can be implemented as an instance of Bayesian inference. Shots are generated from a quantum circuit with likelihood $$p(m \mid \phi, k, \beta) = \frac12\left(1 + (1 - q)\cos(k\phi + \beta - m \pi)\right),$$ where $m \in {0,1}$ is the binary shot produced by the quantum device, $\phi$ is the unknown underlying phase, $q$ is a noise parameter or error rate. $k$ and $\beta$ are circuit parameters that are chosen by the user (or phayes).

Starting with a uniform prior over $\phi$, phayes uses Bayesian inference to hone in on the true value (with uncertainty quantification) through repeated measurements.

Install

pip install phayes

Bayesian updates

The core functions are phayes.get_k_and_beta and phayes.update, which determine the experiment parameters and then update the posterior distribution in light of a new measurement (or series of measurements)

```python from jax import numpy as jnp import phayes

num_shots = 100

posteriorstate = phayes.init() for _ in range(numshots): k, beta = phayes.getkandbeta(posteriorstate) m = getshot(k, beta) posteriorstate = phayes.update(posteriorstate, m, k, beta) `` Here the functiongetshot` executes the quantum circuit above and returns a binary shot (or multiple shots) according the likelihood $p(m\mid \phi, k, \beta)$.

There's more

The probability density function can be visualised easily

```python prior_state = phayes.init() m = jnp.array([0, 1, 1, 0, 0, 1]) k = jnp.array([1, 4, 3, 8, 5, 10]) beta = jnp.array([1.4, 0.6, 1.2, 1.1, 1.9, 0.3])

posteriorstate = phayes.update(priorstate, m, k, beta)

import matplotlib.pyplot as plt linsp = jnp.linspace(-jnp.pi, jnp.pi, 1000) pdf = phayes.pdf(linsp, posterior_state) plt.plot(linsp, pdf) ```

phayes also has a host of other useful functions

python posterior_mean = phayes.circular_mean(posterior_state) posterior_circular_variance = phayes.circular_variance(posterior_state) posterior_holevo_variance = phayes.holevo_variance(posterior_state)

Example notebooks can be found in the examples folder.

Precision

By default JAX uses 32-bit precision, for phase estimation experiments you may well want to enable 64-bit precision by adding the following to the top of your script python from jax.config import config config.update(“jax_enable_x64”, True)

Citation

@software{phayes, author={Duffield, Samuel}, title={phayes: A python package for easy and efficient Bayesian quantum phase estimation}, year={2023}, url={https://github.com/CQCL/phayes} }

Owner

  • Name: Cambridge Quantum
  • Login: CQCL
  • Kind: organization
  • Location: Cambridge, UK

Quantum Software and Technologies

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Dependencies

setup.py pypi
  • jax *