Research Codebase Noise Mapping for Quantum Architectures (NMQA). Formerly known as: Quantum Simultaneous Localisation and Mapping (QSLAM).
References:
[1] Gupta, R. S., Milne, A. R., Edmunds, C. L., Hempel, C., & Biercuk, M. J. (2019). Adaptive scheduling of noise characterization in quantum computers. arXiv preprint arXiv:1904.07225. Accessed https://arxiv.org/abs/1904.07225
[2] Gupta, R. S., & Biercuk, M. J. (2019). Convergence analysis for autonomous adaptive learning applied to quantum architectures. arXiv preprint arXiv:1911.05752. Accessed https://arxiv.org/abs/1911.05752
[3] Gupta, R. S., Govia, L. C. G., & Biercuk, M. J. Interpolation and architectural impacts of spectator qubits for efficient quantum computer calibration and tuneup (forthcoming 2020).
Contains the following Python packges:
qslam : Python package to implement NMQA-QSLAM algorithm.
Supports References [1], [2], [3].
paduaq : Python package for Lagrange 2D interpolation at Padua points.
Supports Reference [3].
clfanalysis : Python package for image classification for pre-processing
single qubit measurements from trapped ion camera data.
Supports Reference [1].
Contains the following directories for associated research analysis:
nmqa1 : Compares NMQA-QSLAM to Naive Approach using simulated data.
Codebase stored as a series of Python (.py) + Artemis (.pbs) scripts.
Supports Reference [1].
nmqa2 : Implements convergence analysis for NMQA-QSLAM.
Codebase stored as a series of Python (.py) + Artemis (.pbs) scripts.
Supports Reference [2].
nmqa3: Compares NMQA-QSLAM to Naive Approach with Padua interpolation.
Codebase stored as a series of Python (.py) + Artemis (.pbs) scripts.
Supports Reference [3].
expt_qslam : Compares NMQA-QSLAM to Naive Approach using experimental data.
Codebase stored as a series of Python (.py) + Artemis (.pbs) scripts.
Supports Reference [1].
expt_data : Converts trapped ion images into measurement database.
Codebase stored as a series of Jupyter Notebooks (.ipynb)
Supports Reference [1].
statedetectn : Analyses qubit state detection via image classification
on trapped ion camera data.
Codebase stored as a series of Jupyter Notebooks (.ipynb).
Supports Reference [1].