BBVA pursues the financial sector’s ‘quantum advantage’

BBVA is examining various lines of research to determine how quantum technologies could represent an advantage over traditional tools in different financial use cases. To this end, the bank launched a strategy that involves forging alliances, strengthening its internal capacity around this technology, evaluating the available tools, and developing proof of concepts in collaboration with research centers, traditional businesses and startups alike.

The BBVA Research and Patent team has shared the results of a project that ran over the course of last year and which aimed to study the applications of quantum computing in the financial sector. Specifically, they disclosed the results of BBVA-sponsored proofs of concepts (PoCs) that followed six lines of investigation with the objective of identifying those use cases where quantum technology delivers a greater advantage over traditional computing techniques. The PoCs also assessed which solutions available on the market could be used to this end.

The project is still in an exploratory phase, but the results so far point to a set of advantages this technology has compared to tools currently used to resolve certain complex problems — such as investment portfolio optimization — quickly, accurately, and efficiently. “Although this technology is still in an early stage of development, its potential to impact the sector is already a reality. Our research is helping us identify the areas where quantum computing could represent a greater competitive advantage, once the tools have sufficiently matured. We believe this will be, for certain concrete tasks, in the next two to five years,” explains Carlos Kuchkovky, BBVA global head of research and patents.

Specifically, the technology deals with complex financial problems that currently require intensive computational calculations — calculations that can take days to complete — and which have to take into account a considerable number of dimensions or variables in order to make the best decision. For example, in the case of investment portfolio optimization, the tests undertaken by the BBVA researchers indicate that the use of these tools could represent huge progress in terms of speed compared to traditional techniques when there are more than 100 variables. Given the pace with which quantum hardware is developing, it is anticipated that these benefits could be soon be produced when applied to even fewer variables.

“These numbers are very fluid, they will change as the capacity of the tools available on the market develops, and they are making very fast advances”, the BBVA executive says. In addition to portfolio optimization, the BBVA researchers also gathered relevant knowledge about the usefulness of this technology applied to other areas such as the simulation of financial scenarios, currency arbitrage, and credit scoring processes.

The team, alliances, and collaboration

Kuchkovsky's team began exploring quantum technologies in mid-2018 as part of its work investigating disruptive technologies and trends that could significantly impact the financial sector in the coming years. Since then, they have built an internal, multidisciplinary team of quantum technology experts, who have already begun working closely with the bank's different business areas in order to define the priority areas where this technology could deliver the greatest value.

In 2019, the team agreed a strategic alliance with CSIC and created a joint working team that is currently collaborating on a line of scientific research that is producing promising results and from which work is being done on the development of the quantum algorithms themselves. Over the course of last year, the BBVA team also kicked off six proofs of concepts that study five financial use cases in collaboration with four companies: startups, Zapata Computing and Multiverse, the technology firm Fujitsu, and the consulting firm Accenture.

“The quantum technology ecosystem is evolving very quickly and we believe that the collaboration with various partners, public and private alike, is key in order to translate the benefits of the technology into tangible progress, for the sector and for society at large,” Kuchkovsky explains.

The ‘quantum advantage’

Quantum computing has the potential to solve problems that classical computers cannot. All of this is thanks to qubits (as opposed to ‘old-world’ bits). Qubits exponentially increase the computing capacity compared to classical computing. If the bits can perform calculations based on two possibilities (1 and 0), qubits can run calculations on all the possible combinations between 1 and 0 in parallel. This is why they are able to tackle problems that have exponentially growing complexity, where the scale of the problem to be solved increases — in other words, problems that have a growing number of variables that have to be factored into the solution.

In the world of finance, these traits are particularly useful when running optimization calculations or simulating financial scenarios, calculations that have to factor in numerous variables and dimensions in order to create accurate models. These are cases where the amount of data to process also increases exponentially as new variables to be factored are added. “Currently some of these problems can take weeks of work using traditional computers. There are even some that we have not been able to solve,” says Escolástico Sánchez, leader of the research and development discipline at BBVA.

"We believe that the collaboration with various partners, public and private alike, is key in order to translate the benefits of the technology into tangible progress"

The technology, however, is still in an early phase of development; the potential of these computers is far from being fully realized, and the tangible benefits for the business world are still some years to come. While true quantum computers have yet to hit the mainstream, cloud-based software systems that use quantum-inspired algorithms are facilitating the execution of the type of exponential calculations that this technology will be useful for. BBVA has tested both types of technology (pure quantum hardware and inspired algorithms or ‘quantum annealing’ algorithms).

These first steps are placing BBVA squarely at the vanguard of financial institutions researching this field. The outcome of its work has produced various scientific studies that will soon be published in specialized scientific journals.

Lines of research and proofs of concepts

1.Development of quantum algorithms (CSIC)

In addition to hardware improvements, another major challenge facing the business deployment of these technologies is the fact that new algorithms must be adapted to the new computing logic, algorithms that can feed the systems once they are functional and primed to solve concrete tasks. BBVA is working on this front in collaboration with CSIC. Their collaboration on this line of scientific research is producing important advances.

The joint team of researchers has developed algorithms that help select the most relevant variables of a broad set of data, like choosing the assets when building an investment portfolio, for example. These developments have been tested to improve stock index tracking, an investment technique that seeks to replicate the behavior of a stock index by selecting some of the assets within it.

The algorithms that have been developed could be applicable to other fields as well, such as the design of logistics networks or variable filtering in machine learning models. Samuel Fernández Lorenzo, BBVA’s head of quantum algorithm research explains, “With our work we have been able to come closer than ever to using quantum computers or quantum-inspired algorithms for real world applications.”

2. Static Portfolio Optimization (Fujitsu)

The optimization of investment portfolios consists of choosing assets that, when combined, can help customers earn greater returns based on factors such as investor and risk profiles. One way to make this process more efficient is to group a portfolio’s assets into subsets with common risk factors. However, as assets are added to a portfolio — along with the factors that need to be taken into account for their classification — the possible combinations that can be produced multiply exponentially, and in turn so do the number of calculations required to get an optimal outcome.

In collaboration with Fujitsu, and working with BBVA Asset Management, the research team has carried out a proof of concept to determine whether these calculations can be performed more efficiently thanks to quantum technologies. Specifically, the Fujitsu Digital Annealer, a quantum-inspired hardware system that uses traditional algorithms to simulate the technology’s characteristics, was used in the PoC. It was determined that better results could be obtained with this kind of equipment compared to traditional approaches when there are more than 100 assets or factors to be introduced into the calculation.

3. Dynamic portfolio optimization 

Dynamic portfolio optimization relies on a significant number of variables in order to determine the best combination of assets. This means that the portfolio’s performance over time, the possible trading fees, and potential impacts on the market price of high volume buying and selling can be calculated. Various tests with different technology providers have been carried out in order to identify how quantum technologies can be used to address this challenge.

• Accenture: During this test, a quantum annealing solution from technology provider, D-Wave was used to demonstrate that there are benefits to using quantum techniques over traditional methods when hundreds of assets and/or factors are involved in the calculation. The promising results have convinced the team to continue their investigation of this case with other technologies.
• Multiverse: The Spanish startup, Multiverse, collaborated on the second test  using two different technological solutions to address the same problem. Both quantum-inspired algorithms and pure quantum hardware from IBM (with associated limitations) were used to perform the tests. The testing is still underway, but the results are promising and will shortly be published in a scientific paper.

4. Credit scoring process optimization

Also in collaboration with Accenture and D-Wave, a proof of concept was undertaken to determine if quantum computing could accelerate the output of credit scoring results compared to existing data analysis systems. The results of this exercise indicate that there could be benefits in cases where there are more variables than are normally used in this type of problem.

5. Currency arbitrage optimization  

Currency arbitrage — the looking to profit from buying and selling currencies — is another problem that may lend itself to quantum computing. The window of opportunity for these types of opportunities is very small and powerful processors are required in order to identify and take advantage of this opportunity.

In order to verify if the process efficiency could be improved using quantum technologies, another proof of concept was designed together with Accenture and using the D-Wave technology. The outcome in this case points to possible benefits when the operation is dealing with at least a dozen assets.

6. Derivative valuations and adjustments 

Monte Carlo simulations, which use random sampling to simulate the performance trend of different variables, is one of the ways the financial sector calculates the price of derivative products.  Derivatives are complex financial products whose values depend on the price performance of other assets. Determining the price of these products is not always straightforward, and in some cases the calculations can be computationally costly.

The BBVA Corporate and Investment Banking (CIB) unit in partnership with the U.S. startup,  Zapata Computing, launched a proof of concept to evaluate the use of quantum algorithms applied to the Monte Carlo method in order to determine the price of a derivative instrument with its counterparty risk adjustment. The objective of the test is to analyze if there are benefits to using these techniques, what computing resources would be necessary to yield the improvement, and how do the results scale against the dimensions of the problem.

Building the foundations

BBVA's next steps — in addition to progressing with the current lines of research — will be to find new, more disruptive use cases and to deepen the collaboration with the bank's business units. Moreover, BBVA researchers are interested in exploring how this technology can be applied to in order to improve machine learning algorithms in addition to its potential to improve energy efficiency and help advance toward a more sustainable society.

“At BBVA we believe that quantum technology will be key to solving some of the major challenges facing society this decade. Addressing these challenges dovetails with BBVA’s strategic priorities, such as fostering the more efficient use of increasingly greater volumes of data for better decision-making as well as supporting the transition to a more sustainable future,” Kuchkovsky adds.