Optimizing radiotherapy plans for cancer treatment with Tensor Networks

We present a novel application of Tensor Network methods in cancer treatment as potential tool to solve the dose optimization problem radiotherapy. In particular, Intensity-Modulated Radiation Therapy (IMRT) technique - that allows treating irregular and inhomogeneous tumors while reducing radiation toxicity on healthy organs is based beamlets intensities. The aims maximize delivery therapy avoiding at risk prevent their damage by radiation. Here, we map into search ground state an Ising-like Hamiltonian, describing system long-range interacting qubits. Finally, apply Tree algorithm find ground-state Hamiltonian. anatomical scenario exemplifying prostate treatment. A similar approach can be applied future hybrid classical-quantum algorithms, paving way for use quantum technologies medical treatments.