We discuss harnessing future scalable computing resources and cloud infrastructure for both quantum AI and pharmaceutical innovation, with a specific focus on applications in AES encryption, pharmaceutical molecular computations, and the quantum circuit representation of Shor’s algorithm. Along the way, we also discuss circuit optimization. For the deep AES encryption problem, we encode the problem in a one-layered neural network, with the known key serving as a barrier near the minimum. Circuit substitutions are applied at intervals, and we thereby harness polynomial complexity quantum algorithms. For pharmaceutical applications, we discuss how the tensor product of parameterized quantum electronic circuits applied to fermionic normal ordering via truncated binary encoding and rotation rounding can aid the discovery of new pharmaceutical compounds. We introduce a machine learning prediction as a first step and quantitatively show how the probability of success varies in a nine-level atomic chip. For the third illustrative application, we discuss quantum realizations of Shor’s algorithm. We focus on estimation and phase correction operations, as well as Fourier transforms. Guidelines are laid out for implementation on topological quantum platforms. We also discuss tailoring Shor’s algorithm for multiplication with the minimal number of required qubits. Finally, we summarize cloud quantum computing among the algorithmic techniques and summarize our findings with potential benefits to different stakeholders. We believe that Hamiltonian/Ising design may then provide the key to matching qubit error rates to Gaussian boson sampling.