TensorNetwork Optimizing Camera Placement for Emergency Prevention and Response Solution

The goal of this project is to apply a tensor-network (TN) approach to a combinatorial optimization problem of industrial interest, the Camera Placement problem, and to estimate the scaling towards realistic problem sizes.
Hyper-spectral cameras can be deployed to monitor a territory to identify signatures of an imminent extreme natural event, such as floods, earthquakes, and wildfires. Depending on the nature of the risk, this kind of systems can also be equipped with sensors to monitor air parameters and seismic activity. The utility of these monitoring systems extends to the emergency response phase, in which it allows to collect useful information to the rescuers, ensuring efficient and swift Search & Rescue operations. Since this kind of high-resolution and hyper-spectral cameras is very expensive, a limited number of them is available to be deployed. Usually, this number is much smaller than the number of candidate sites.
Tasks
Solve a small-scale problem instance using exact methods, such as brute-force search or state-of-the-art solvers, as a benchmark Translation to a TN-based ground state search using variational ground-state search and imaginary-time evolution
Implementation and validation of the code w.r.t. the benchmark
Evaluation of selected performance metrics, including (but not limited to):
Convergence of the algorithmic hyper-parameters (variational steps, temperature, number of steps...)
Estimation of the time-to-solution (TTS) vs the number of sites (qubits) $N$
Comparison of the solution quality attainable with TNs for the constrained and unconstrained problems
Optional: comparison of the TN performances with those obtained with commercial solvers (f.i. GUROBI, CPLEX...)
Materials
[2] Jupyter notebook for the problem generation (i.e. the Hamiltonian)
[3] Example of how to use qtealeaves to perform a ground state search or an imaginary time evolution with spin-glass problems;
Dependencies
In addition to qtealeaves, the pandas package is required to run the examples.