In the NISQ era of quantum computing, as system sizes are progressively increasing, there are major concerns about the degradation of performance with increasing complexity. These can largely be reduced to the problems of crosstalk and correlations between system components, of uncertainties and drift in system parameters, and of multi-parameter optimization across multi-qubit state spaces in a fixed uptime duty cycle. In this presentation, we address inroads towards a comprehensive, scalable approach for control theoretic solutions to maintaining (given architecture) performance that encompasses: a method to incorporate arbitrary couplings into an effective Hamiltonian frame with superexponential speedup compared to standard approaches [B. Li, T. Calarco, F. Motzoi, arXiv:2112.00039 (2021)]; a control theoretic approach to tracking uncertainties in quantum circuits giving tight error bounds [M. Dalgaard, C. Weidner, F, Motzoi - Phys. Rev. Lett. 128, 150503 (2022)]; and a machine learning framework for symbolic optimization given particular Hamiltonian and associated uncertainties with a single meta-optimization permitting simultaneous tuneup of all qubits within the architecture belonging to the same class of Hamiltonians [F. Preti, T. Calarco, F. Motzoi, arXiv:2203.13594 (2022)].