  Quantum annealing has emerged in the last few years as a promising quantum
computing approach to solving large-scale combinatorial optimization problems.
In this paper, we formulate the problem of correctly placing pressure sensors
on a Water Distribution Network (WDN) as a combinatorial optimization problem
in the form of a Quadratic Unconstrained Binary Optimization (QUBO) or Ising
model. Optimal sensor placement is indeed key to detect and isolate fault
events. We outline the QUBO and Ising formulations for the sensor placement
problem starting from the network topology and few other features. We present a
detailed procedure to solve the problem by minimizing its Hamiltonian using
PyQUBO, an open-source Python Library. We then apply our methods to the case of
a real Water Distribution Network. Both simulated annealing and a hybrid
quantum-classical approach on a D-Wave machine are employed.
