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The recently developed Projective Quantum Eigensolver (PQE) has beendemonstrated as an elegant methodology to compute the ground state energy ofmolecular systems in Noisy Intermdiate Scale Quantum (NISQ) devices. Theiterative optimization of the ansatz parameters involves repeated constructionof residues on a quantum device. The quintessential pattern of the iterationdynamics, when projected as a time discrete map, suggests a hierarchicalstructure in the timescale of convergence, effectively partitioning theparameters into two distinct classes. In this work, we have exploited thecollective interplay of these two sets of parameters via machine learningtechniques to bring out the synergistic inter-relationship among them thattriggers a drastic reduction in the number of quantum measurements necessaryfor the parameter updates while maintaining the characteristic accuracy of PQE.Furthermore the machine learning model may be tuned to capture the noisy dataof NISQ devices and thus the predicted energy is shown to be resilient under agiven noise model.

Machine Learning Aided Dimensionality Reduction towards a Resource Efficient Projective Quantum Eigensolver