  With the rapid development of quantum technology, one of the leading
applications is the simulation of chemistry. Interestingly, even before full
scale quantum computers are available, quantum computer science has exhibited a
remarkable string of results that directly impact what is possible in chemical
simulation with any computer. Some of these results even impact our
understanding of chemistry in the real world. In this perspective, we take the
position that direct chemical simulation is best understood as a digital
experiment. While on one hand this clarifies the power of quantum computers to
extend our reach, it also shows us the limitations of taking such an approach
too directly. Leveraging results that quantum computers cannot outpace the
physical world, we build to the controversial stance that some chemical
problems are best viewed as problems for which no algorithm can deliver their
solution in general, known in computer science as undecidable problems. This
has implications for the predictive power of thermodynamic models and topics
like the ergodic hypothesis. However, we argue that this perspective is not
defeatist, but rather helps shed light on the success of existing chemical
models like transition state theory, molecular orbital theory, and
thermodynamics as models that benefit from data. We contextualize recent
results showing that data-augmented models are more powerful rote simulation.
These results help us appreciate the success of traditional chemical theory and
anticipate new models learned from experimental data. Not only can quantum
computers provide data for such models, but they can extend the class and power
of models that utilize data in fundamental ways. These discussions culminate in
speculation on new ways for quantum computing and chemistry to interact and our
perspective on the eventual roles of quantum computers in the future of
chemistry.
