One of the major applications of quantum chemistry is the simulation of electronic spectra. This can be achieved either by directly computing a manifold of excited states or by employing time-dependent approaches, where the spectrum is obtained from the autocorrelation function. The former approach works reasonably well for small organic molecules but becomes increasingly difficult as the density of states increases, which makes tackling large complexes of heavy metals challenging. The latter approach relies on propagation of a superposition of excited states, which requires the use of resource-intensive multiconfigurational ansatzes. This introduces errors due to wavefunction compression, which accumulate with the time evolution and introduce errors in the spectra. Quantum computers, alternatively, are made of quantum bits (qubits) that enable wavefunction encoding and propagation without compression. However, quantum operations introduce noise that provides an upper limit on the total number of operations (circuit depth). The current roadmap for quantum computing by IBM predicts fault-tolerant quantum computer with 200 qubits and feasible circuit depths of 10^7. The main goal of this project is to develop quantum algorithms for simulation of electronic spectra on a quantum machine that fits these projections. For this purpose, we aim to develop minimal model of the electronic structure that can be encoded on the quantum machine, and that captures the excited state structure. This will be performed using approximate classical methods based on perturbation theory that will yield a minimal set of orbitals that describe relevant excitations, and an approximate ground state wavefunction that can be encoded on the quantum circuit. The autocorrelation function will then be computed from the unitary time evolution and sampled using Hadamard test. These algorithms will be applied to a selected set of lanthanide complexes used as fluorescent chemosensors.