Optimizing the order of polarization projections for n-qubit state tomography.
This repository provides supplementary material to the following paper by Radim Hošák, Robert Stárek, and Miroslav Ježek: The optimal strategy for photonic quantum tomography, Opt. Express 26, 32878 (2018), preprint: arXiv 1809.07521 [quant-ph].
See TOMO_TSP.md for the basic information on polarization tomography and its specification as TSP.
- Python 3
- Python 3 modules:
- NumPy
- Matplotlib (optional, for plotting)
- Concorde TSP solver
See the wave_plate_tsp.ipynb Jupyter notebook for a detailed guide on how to formulate and solve the TSP. The notebook solves the tomography TSP for polarization-encoded photonic qubits, asuuming one single-photon detector per qubit (the six-state scheme).
The TSP solutions for n-qubit (n up to five) polarization tomography, generated by Concorde, are stored in the files nq_6s.sol, with n being a number from 1 to 5. The files contain a list of indices that dictate the TSP-optimized order of the polarization projections. For the one-qubit case, the projections H, V, D, A, R, L correspond to indices 0, 1, 2, 3, 4, 5, 6, respectively. For higher-qubit cases, the order of the indices follows this logic: HHH, HHV, ..., HHL, HVH, HVV, ..., HVL, ..., HLL, VHH, VHV, ..., VHL, ..., LLL.