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July 22, 2021

It is no secret that Quantum Computing is leading the charge for speed and efficiency. While large-scale adaption and application are yet to come through, we have taken significant strides in that direction. Since the late nineties, limits for computing have transcended newer heights, and Quantum Computing is clearly poised to shift gears to 100 trillion times faster than the world’s fastest supercomputer. The computing speed is poised to help financial services firms to leap forward by curbing the current limitations to the consumption of ever-growing data. This Quantum Computing movement has been spearheaded by extensive research within the growing start-up community and has been backed by industry leaders.

Key Enablers of the Evolution: Start-ups, Industry Backing

Tech firms globally have been engaged in the groundwork with the principal focus on understanding and refining it for application. Over 30 global technology leaders have collaborated with over 100 leading academic institutions, more than 50 government/non-profit organizations, and more than 200 start-ups to build

Quantum Computers. According to current estimates, research in the area would have seen global investments of about USD 64.98 billion by 2030. The collaborations began with 2 Qubit capacity and now have hit 128 Qubits. The research has gathered steam with a key differentiating factor – large-scale involvement from the start-ups. Given the current trend, we could see over 200 start-up companies take the lead in the Quantum Computing race ahead of the global technology leaders still engaged in research with the release of commercial quantum computers a pipe dream. For instance, start-up firm Rigetti Computing claims to have created 128 Qubit computer.

Quantum Computing & Financial Services

While we await the large-scale availability of Quantum Computing across various industries to facilitate faster decision-making and drive efficiency, the technology holds promise of undeniable gains for the financial services industry. Given, financial services firms make transactions using complex algorithms that are often time-intensive, computing speed and accuracy in real time is paramount. Quantum Computing with Quantum Algorithm can potentially help handle complex processes involving historical data to produce accurate results. Two major use cases - ADM using Quantum Computing (Automated Decision Making), HFT using Quantum Computing (High-Frequency Trading) – are set to offer a huge breakthrough as these require extreme computing power for real-time processing with no errors.

ADM: These decisions are based on factual data with variable credit, collateral, and liquidity constraints in financial services. To achieve this, machine learning algorithms are being used with a large number of training data sets, which is often a time-consuming process. Using Quantum Algorithm, a quantum computer can provide a quantum model of data sets and process them in milliseconds to ensure faster and accurate decisions.

HFT: This involves extremely complex quantitative buy-sell strategies for a large number of orders within milliseconds. This, in turn, needs extreme computing power to analyze the ever-fluctuating trends and conditions in multiple markets and exchanges. That translates to price changes in real time. Quantum Computing trading will enable faster calculations and identify the interdependence between trades using Quantum dot Register, which will minimize the risk on a trade, execute the orders on time and drive the profit potential.

Quantum Computing + AI and ML: Fasten your Seatbelts

While the potential of Quantum Computing in driving speed and accuracy is yet to be wholly realized, there also are some challenges to the application. Key among these is the amount of energy needed to power the technology that can be extremely draining on the resources. A good route to take for optimum utilization would be to combine Quantum Computing with AI and ML. Tying one end of the rope of Quantum Computing with Artificial Intelligence and Machine Learning will ensure the optimum utilization of the computing power, less error prone, and more powerful operational and computing efficiency in financial services.

Quantum AI algorithms can be used to analyze financial risk. The risk assessment computation using Monte Carlo simulation is an overnight task. The quadratic speedup provided by Quantum Computing can achieve this in near real time. University of Bristol’s Quantum Engineering Technology Labs (QETLabs) developed an AI-based Quantum Algorithm for Quantum information processing and quantum sensing. Whereas, Quantum ML can cut through the complexity in trading optimization using the valuation adjustments model for derivatives, including Credit (CVA), Debit (DVA), Funding (FVA), Capital (KVA), and Margin (MVA). As the volume of data increases, the complexity of computing and time required to analyze, compute, identify and interpret the data also goes up. In addition, quantum data is noisy, hence, it needs ML Algorithms to interpret the data correctly.

Conclusion

It is clear that start-ups are gearing up to transform the banking and financial services from the current state to a quantum state while most industry leaders remain engaged in research. Going forward, Quantum Computing will be the technology to help effectively address the challenges in near real-time and handle the financial data in a secure way. Additionally, while Quantum Computing can perform a majority of the complex process, we do see added benefits when it is combined with Artificial Intelligence and Machine Learning. Given the speed of innovation real-world applications may be a reality sooner than later.

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Karthikeyan Rengasamy is a Chief Architect for TCS Banking and Financial Services unit with over 19 years of experience in IT Architecture Solution Design. His skillset cuts across the technology platforms from legacy to cloud and beyond. Karthikeyan holds a Masters’ degree in Business Administration and a Bachelors’ in Electronics and Communication Engineering.

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