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

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