Quantum machine learning (QML), a subfield of quantum computing that combines the principles of quantum mechanics with machine learning (ML) algorithms, hold transformative potential in banking and finance, offering opportunities to enhance decision-making processes, better mitigate risks, and uncover new business opportunities. <\/p>\n
But despite these many promises and opportunities, there are still several challenges and risks that need to be addressed, including the complexity of quantum algorithms, the high costs associated with the development and implementation of quantum computing and QML, and regulatory and ethical challenges in integrating these technologies in the financial industry, a new report by the Institute of Financial Services Zug IFZ at the Lucerne School of Business says. <\/p>\n
Quantum computing is a type of computing technology that harnesses the principles of quantum physics to perform computations. In contrast to classical computing where information is processed using bits represented as 0s and 1s, quantum computing uses quantum bits or qubits that can exist in a state of superposition, simultaneously representing both 0 and 1. <\/p>\n
In addition to superposition, another unique principle of quantum computing is entanglement where the state of one qubit is directly influenced by the state of the other, even if they are physically separated. <\/p>\n
These properties allow quantum computers to solve certain types of problems much more efficiently than classical computers. <\/p>\n
On the other hand, AI technology relies on algorithms and data to create systems that emulate human intelligence and which are capable of performing tasks such as visual perception, speech recognition, decision-making, and language translation. ML is a subset of AI focusing on gives computer systems the ability to learn from data without explicit being programmed. <\/p>\n
The Institute of Financial Services Zug IFZ report, titled Quantum Computing and Artificial Intelligence in Finance and released in December 2023, explores the relationship between quantum computing and ML in the financial sector, highlighting both opportunities and challenges in this technological convergence. <\/p>\n
According to the report, when quantum computing and AI\/ML are combined, these technologies can unlock unparalleled potential for financial services, a sector thats characterized by substantial data volumes, intricate problems, and critical decision-making. This integration promises to revolutionize how the financial services sector handles complex challenges and data-intensive processes, enhancing speed, precision, and intelligence, it says. <\/p>\n
In the financial sector, QML, which refers to the use of algorithms run on quantum devices to process and analyze large volumes of data, is able to perform certain calculations exponentially faster than classical computers, potentially offering many benefits in use cases ranging from fraud prevention and creditworthiness calculations, to more efficient pricing strategies and optimized portfolio management strategies. <\/p>\n
In fraud prevention, quantum algorithms can enhance fraud detection systems by efficiently and promptly analyzing large volumes of financial transaction data and identifying patterns indicative of fraudulent activities. In credit scoring, QML can aid in assessing credit-worthiness by analyzing diverse data sources to provide more accurate risk assessments for individuals and businesses. <\/p>\n
Quantum algorithms can also be utilized to expedite the calculation of financial product prices, enabling more efficient pricing strategies and uncovering arbitrage opportunities. Finally, in portfolio management, trading and hedging, QML can be employed to develop advanced strategies and optimize portfolio management by processing market and financial data and identifying patterns that can inform decision-making processes as well as determining optimal investment opportunities. <\/p>\n
But despite these opportunities, the report notes that quantum computing is still in its early stages of development and that several challenges are hampering the finance industry from fully harnessing the potential of quantum computing and QML. <\/p>\n
These challenges primarily relate to scalability and reliability. High-performance quantum computers require hundreds of thousands of qubits for practical use, and while the industry is actively developing new and scalable hardware, it will still take a few years before a service is available in the required quantities, the report says. <\/p>\n
Additionally, the use of quantum computing is still complicated and not user-friendly today, implying that further innovations in the area of quantum-related software are required. <\/p>\n
Finally, the issue of reliability is a sticking point in the operation of a quantum computer thats associated with the issue of decoherence. Decoherence effects arise when a quantum system interacts with its environment and the superposition is lost. It can introduces errors and limits the depth and complexity of quantum computations that can be reliably performed. <\/p>\n
Interest in quantum computing has risen sharply over the past year. In 2022, investors poured US$2.35 billion into quantum tech startups, surpassing 2021s record for the highest annual level of quantum tech startup investment, findings from a McKinsey analysis show. <\/p>\n
Volume of raised investment in the indicated year, US$ million, Source: McKinsey and Company, April 2023 <\/p>\n
Deloitte expects the financial services industrys spending on quantum computing capabilities to grow 233x from just US$80 million in 2022 to US$19 billion in 2032, reflective of the sectors confidence in the technologys future commercial potential. <\/p>\n
Spending on quantum computing capabilities from the financial services industry (US$ million), 2022-2032, Source: Deloitte Center for Financial Services analysis, July 2023 <\/p>\n
According to McKinsey, the financial services industry stands as one of the four sectors likely to see the earliest economic impact from quantum computing, potentially gaining up to US$700 billion in value by 2035 thanks to the technology. <\/p>\n
Estimated value at stake for quantum computing in four key industries, Source: 2023 Quantum Technology Monitor, McKinsey and Company, April 2023 <\/p>\n
Switzerland welcomed in 2022 its first quantum hub. Called QuantumBasel, the center is located in the uptownBasel innovation campus and provides customers and researchers with workshops, training sessions, and access to quantum systems to further their understanding of quantum computing and drive progress towards commercial applications. Funded by the family of Dr. Thomas Staehelin and Monique Staehelin, QuantumBasel is set to house the countrys first commercially viable quantum computer starting in 2024. <\/p>\n<\/p>\n
Featured image credit: edited from freepik <\/p>\n
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New IFZ Paper Explores the Opportunities and Challenges of Quantum Computing and AI in Finance - Fintech Schweiz ... - Fintechnews Switzerland<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Quantum machine learning (QML), a subfield of quantum computing that combines the principles of quantum mechanics with machine learning (ML) algorithms, hold transformative potential in banking and finance, offering opportunities to enhance decision-making processes, better mitigate risks, and uncover new business opportunities. But despite these many promises and opportunities, there are still several challenges and risks that need to be addressed, including the complexity of quantum algorithms, the high costs associated with the development and implementation of quantum computing and QML, and regulatory and ethical challenges in integrating these technologies in the financial industry, a new report by the Institute of Financial Services Zug IFZ at the Lucerne School of Business says. Quantum computing is a type of computing technology that harnesses the principles of quantum physics to perform computations. Continue reading