  Quantum computers are currently accessible through a cloud-based platform
that allows users to run their programs on a suite of quantum hardware. As the
quantum computing ecosystem grows in popularity and utility, it is reasonable
to expect more companies, including untrustworthy or untrustworthy or
unreliable vendors, to begin offering quantum computers as hardware as a
service at various price or performance points. Since computing time on quantum
hardware is expensive and the access queue may be long, users will be enticed
to use less expensive but less reliable or trustworthy hardware. Less trusted
vendors may tamper with the results and or parameters of quantum circuits,
providing the user with a sub-optimal solution or incurring a cost of higher
iterations. In this paper, we model and simulate adversarial tampering of input
parameters and measurement outcomes on an exemplary hybrid quantum classical
algorithm namely, Quantum Approximate Optimization Algorithm (QAOA). We observe
a maximum performance degradation of approximately 40%. To achieve comparable
performance with minimal parameter tampering, the user incurs a minimum cost of
20X higher iteration. We propose distributing the computation (iterations)
equally among the various hardware options to ensure trustworthy computing for
a mix of trusted and untrusted hardware. In the chosen performance metrics, we
observe a maximum improvement of approximately 30%. In addition, we propose
re-initialization of the parameters after a few initial iterations to fully
recover the original program performance and an intelligent run adaptive split
heuristic, which allows users to identify tampered/untrustworthy hardware at
runtime and allocate more iterations to the reliable hardware, resulting in a
maximum improvement of approximately 45%.
