  In recent years, the field of quantum computing has significantly developed
in both the improvement of hardware as well as the assembly of various software
tools and platforms, including cloud access to quantum devices. Unfortunately,
many of these resources are rapidly changing and thus lack accessibility and
stability for robust algorithm prototyping and deployment. Effectively
leveraging the array of hardware and software resources at a higher level, that
can adapt to the rapid development of software and hardware, will allow for
further advancement and democratization of quantum technologies to achieve
useful computational tasks. As a way to approach this challenge, we present a
flexible, high-level framework called algo2qpu that is well-suited for
designing and testing instances of algorithms for near-term quantum computers
on the cloud. Algorithms that employ adaptive protocols for optimizations of
algorithm parameters can be grouped under the umbrella of "adaptive hybrid
quantum-classical" (AHQC) algorithms. We demonstrate the utility of algo2qpu
for near-term algorithm development by applying the framework to implement
proof-of-principle instances of two AHQC algorithms that have applications in
quantum chemistry and/or quantum machine learning, namely the quantum
autoencoder and the variational quantum classifier, using Rigetti Computing's
Forest platform.
