05/25/21

Quantum Algorithm for Credit Valuation Adjustments

  • Francisco Javier F. Alcazar
  • Amara Katabarwa
  • Yudong Cao
  • Guoming Wang
  • Marta Mauri

We have collaborated with BBVA to perform an in-depth study of how much quantum computing resource it takes to obtain a practical benefit for a specific use case in finance that is one step beyond pricing complex derivatives; Credit Valuation Adjustments (CVA).

The CVA use case is both ubiquitously significant and computationally challenging. The core of the computational challenge, which is the cost of Monte Carlo simulation, is by no means unique to the use case that we are pursuing. It is a general challenge in many quantitative finance problems having to deal with risk analysis. This type of risk analysis has become increasingly important as more banks comply with the regulations such as those posed by the Basel Committee of Banking Supervision (BCBS) to minimize the systemic risk of the overall financial ecosystem.

Together with BBVA, we present a quantum algorithm and detailed resource estimation. We propose novel circuit designs that can significantly reduce the resource needed for solving the CVA problem but more importantly, we identify the remaining challenges that need to be addressed towards practical quantum advantage.

 

External Co-Author:

 

Author
Francisco Javier F. Alcazar
Zapata Author

Francisco Javier F. Alcazar , Ph.D.

Quantum Application Scientist
Author
Amara Katabarwa
Zapata Author

Amara Katabarwa , Ph.D.

Quantum Application Scientist
Author
Yudong Cao
Zapata Author

Yudong Cao , Ph.D.

CTO & Founder
Author
Guoming Wang
Zapata Author

Guoming Wang , Ph.D.

Quantum Research Scientist
Author
Marta Mauri
Zapata Author

Marta Mauri

Quantum Software Engineer