Zapata Case Studies Accelerating Monte Carlo Simulations with Quantum Computing: A BBVA and Zapata Case Study

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	Accelerating Monte Carlo Simulations with Quantum Computing: A BBVA and Zapata Case Study Zapata

Accelerating Monte Carlo Simulations with Quantum Computing: A BBVA and Zapata Case Study

Zapata

Applicable Industries	Finance & Insurance
Applicable Functions	Product Research & Development
Use Cases	Leasing Finance Automation
Challenge	BBVA, a Spain-based financial institution with over €662 billion in assets and 83 million customers across more than 30 countries, relies on complex calculations for risk analysis and pricing of financial products. Post the 2008 Financial Crisis, regulations necessitated banks to evaluate credit risk and stress-test financial scenarios. Typically, this risk analysis is performed using Monte Carlo simulations, a highly complex, expensive, and time-consuming process that must account for all possible credit default scenarios. Any enhancement in the performance of these simulations would directly affect the daily operational costs, financial product pricing, and risk analysis. Read More
About Customer	BBVA is a leading financial institution based in Spain. It is one of the largest banks in the world, boasting over €662 billion in assets and serving 83 million customers in more than 30 countries. Like most banks, BBVA relies on intricate calculations for risk analysis and setting the price of financial products. Since the 2008 Financial Crisis, new regulations have necessitated banks like BBVA to assess credit risk and stress-test financial scenarios. To meet these requirements, BBVA typically uses Monte Carlo simulations, a complex and time-consuming process that must account for all possible credit default scenarios. Read More
Solution	BBVA collaborated with Zapata to develop innovative quantum circuits that offer a speedup over existing quantum algorithms for Monte Carlo simulations. These simulations are used by BBVA in credit valuation adjustment (CVA) and derivative pricing. This collaboration significantly reduced the resources required for a quantum computer to conduct Monte Carlo simulations by orders of magnitude. It also considerably reduced the number of qubits required for a practical quantum advantage in quantitative finance. Essentially, this research has expedited the timeline for when financial institutions could expect to gain real value from quantum computers. The team is now looking at how they can leverage these results to tackle other computationally complex problems in finance, benchmarking novel quantum approaches with existing classical algorithms to identify potential advantages. Read More Log in to view content
Contents

Applicable Industries

Finance & Insurance

Applicable Functions

Product Research & Development

Use Cases

Leasing Finance Automation

Challenge

BBVA, a Spain-based financial institution with over €662 billion in assets and 83 million customers across more than 30 countries, relies on complex calculations for risk analysis and pricing of financial products. Post the 2008 Financial Crisis, regulations necessitated banks to evaluate credit risk and stress-test financial scenarios. Typically, this risk analysis is performed using Monte Carlo simulations, a highly complex, expensive, and time-consuming process that must account for all possible credit default scenarios. Any enhancement in the performance of these simulations would directly affect the daily operational costs, financial product pricing, and risk analysis.

About Customer

BBVA is a leading financial institution based in Spain. It is one of the largest banks in the world, boasting over €662 billion in assets and serving 83 million customers in more than 30 countries. Like most banks, BBVA relies on intricate calculations for risk analysis and setting the price of financial products. Since the 2008 Financial Crisis, new regulations have necessitated banks like BBVA to assess credit risk and stress-test financial scenarios. To meet these requirements, BBVA typically uses Monte Carlo simulations, a complex and time-consuming process that must account for all possible credit default scenarios.

Solution

BBVA collaborated with Zapata to develop innovative quantum circuits that offer a speedup over existing quantum algorithms for Monte Carlo simulations. These simulations are used by BBVA in credit valuation adjustment (CVA) and derivative pricing. This collaboration significantly reduced the resources required for a quantum computer to conduct Monte Carlo simulations by orders of magnitude. It also considerably reduced the number of qubits required for a practical quantum advantage in quantitative finance. Essentially, this research has expedited the timeline for when financial institutions could expect to gain real value from quantum computers. The team is now looking at how they can leverage these results to tackle other computationally complex problems in finance, benchmarking novel quantum approaches with existing classical algorithms to identify potential advantages.

Impact #1	The collaboration between BBVA and Zapata has led to the development of novel quantum circuits that provide a speedup over existing quantum algorithms for Monte Carlo simulations. This has not only reduced the resources and qubits required for these simulations but also accelerated the timeline for when financial institutions could expect to gain real value from quantum computers. The team is now exploring how to expand on these results to address other computationally complex problems in finance, benchmarking novel quantum approaches with existing classical algorithms to detect potential advantages. This could potentially revolutionize the way financial institutions handle risk analysis and pricing of financial products.

Impact #1

The collaboration between BBVA and Zapata has led to the development of novel quantum circuits that provide a speedup over existing quantum algorithms for Monte Carlo simulations. This has not only reduced the resources and qubits required for these simulations but also accelerated the timeline for when financial institutions could expect to gain real value from quantum computers. The team is now exploring how to expand on these results to address other computationally complex problems in finance, benchmarking novel quantum approaches with existing classical algorithms to detect potential advantages. This could potentially revolutionize the way financial institutions handle risk analysis and pricing of financial products.

Benefit #1	Significant reduction in the resources required for a quantum computer to conduct Monte Carlo simulations.
Benefit #2	Considerable decrease in the number of qubits required for a practical quantum advantage in quantitative finance.
Benefit #3	Expedited the timeline for when financial institutions could expect to gain real value from quantum computers.

Benefit #1

Significant reduction in the resources required for a quantum computer to conduct Monte Carlo simulations.

Benefit #2

Considerable decrease in the number of qubits required for a practical quantum advantage in quantitative finance.

Benefit #3

Expedited the timeline for when financial institutions could expect to gain real value from quantum computers.

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Overview

Accelerating Monte Carlo Simulations with Quantum Computing: A BBVA and Zapata Case Study

Operational Impact

Quantitative Benefit