https://github.com/dwavesystems/shimming-tutorial
https://github.com/dwavesystems/shimming-tutorial
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/dwavesystems/shimming-tutorial
- Owner: dwavesystems
- License: apache-2.0
- Created: 2023-04-05T18:59:09.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-04-24T20:22:31.000Z (about 1 year ago)
- Last Synced: 2025-10-30T22:49:44.906Z (8 months ago)
- Language: Python
- Size: 27.3 KB
- Stars: 5
- Watchers: 5
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Shimming Tutorial
This repository contains supplementary code for
Tutorial: Calibration refinement in quantum annealing
https://doi.org/10.3389/fcomp.2023.1238988
Install the dependencies with
```bash
pip install -r requirements.txt
```
Code examples demonstrate orbits for selected models (executed locally),
calculation of embeddings (exectued locally),
and iterative shimming results for several models (executed by default on
Leap-hosted Quantum Processing Units).
Examples have configurable parameters, to see these use the `--help`
command line option. This includes the flexibility to reuse cached
embeddings and data, modify the model, target processor, or embedding
heuristic for increased performance. To reduce the total number
of iterations (programmings, thence QPU access time) or learning rate(s).
For example,
```
python example3_2_tafm_forward_anneal.py --solver_name MockDWaveSampler --num_iters 20 --num_iters_unshimmed_flux 4 --num_iters_unshimmed_J 8
```
runs with only 20 total samplesets accelerating data acquisition (albeit
not to convergence as shown in the paper). In addition we specify the
solver as MockDWaveSampler allowing local execution with artificial noise,
allowing intuition.
The original shimming tutorial uses an Advantage generation processor
by default. This tutorial is adaptable to other processors and uses the default,
generally-accessible processor.
It should be noted that defaulted parameters such as the learning rates,
number of reads and iterations are expected to be functions of the noise and
energy scales of the target processor, and may require adjustment in
order to demonstrate comparable phenomena to the paper.
## Orbit examples (run locally)
For generation of Figure 4
```
python -m example0_1_orbits
```
For generation of Figure 9
```
python -m example2_1_frustrated_loop_orbits
```
For generation of Figure 11
```
python -m example2_3_buckyball_orbits
```
For generation of orbits related to Figures 13-16
```
python -m example3_1_tafm_get_orbits
```
## Ferromagnetic loop example
For generation of Figure 6: **Balancing qubits in a FM chain with flux-bias offsets**.
Requires 300 sequential QPU-job submissions.
```
python -m example1_1_fm_loop_balancing
```
For generation of **Balancing qubits and couplers in a FM chain with flux-bias offsets and coupler adjustments** related to Figure 7.
Requires 300 sequential QPU-job submissions.
```
python -m example1_2_fm_loop_correlations
```
## Frustrated loop example
For generation of Figure 10: **Shimming a frustrated loop**.
Requires 300 sequential QPU-job submissions.
```
python -m example2_2_frustrated_loop_anneal
```
## Triangular antiferromagnet example
For generation of the following figures:
- Figure 13: **Shimming an embedded cylindrical triangular antiferromagnet**.
- Figure 14: **Shimming an isotropic, infinite triangular antiferromagnet**.
- Figure 15: **Shimming an isotropic, infinite triangular antiferromagnet, starting with halved boundary couplers**.
- Figure 16: **Complex order parameter ψ**.
Generation of embeddings may require several minutes.
Data is collected in 3 blocks each requiring 800 sequential QPU-job submissions.
One figure is presented per block (interrupting data collection until the
figure is closed); along with one summary figure.
```
python -m example3_2_tafm_forward_anneal
```