



01551nas a2200181   4500008004100000245006000041210005800101300000800159490000600167520101600173100002701189700002401216700002301240700002701263700002001290700002201310856003701332       2021                        eng d00aA Threshold for Quantum Advantage in Derivative Pricing0 aThreshold for Quantum Advantage in Derivative Pricing  a4630 v53 a<p>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 7.5k logical qubits and a T-depth of 46 million and thus estimate that quantum advantage would require a logical clock speed of 10Mhz. 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.&nbsp;</p>1 aChakrabarti, Shouvanik1 aKrishnakumar, Rajiv1 aMazzola, Guglielmo1 aStamatopoulos, Nikitas1 aWoerner, Stefan1 aZeng, William, J.  uhttps://arxiv.org/abs/2012.03819