  We give an upper bound on the resources required for valuable quantum
advantage in pricing derivatives. To do so, we give the first complete resource
estimates for useful quantum derivative pricing, using autocallable and Target
Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We
uncover blocking challenges in known approaches and introduce a new method for
quantum derivative pricing - the re-parameterization method - that avoids them.
This method combines pre-trained variational circuits with fault-tolerant
quantum computing to dramatically reduce resource requirements. We find that
the benchmark use cases we examine require 8k logical qubits and a T-depth of
54 million. We estimate that quantum advantage would require executing this
program at the order of a second. While the resource requirements given here
are out of reach of current systems, we hope they will provide a roadmap for
further improvements in algorithms, implementations, and planned hardware
architectures.
