Classiq CEO Nir Minerbi said quantum computing innovations pave the way for advanced financial portfolio optimization.
Posted: February 9, 2024 11:48 AM Updated: February 9, 2024 11:48 AM
Correction and fact check date: February 9, 2024, 11:48 AM
briefly
Classiq Technologies CEO Nir Minerbi explores the potential of quantum computing in finance, especially portfolio optimization.
Quantum computing software platform Classiq Technologies and the Citi Innovation Lab, the fintech development center of New York-based banking group Citibank, recently joined forces to test the potential of quantum computing in finance and understand its impact on solving business problems, particularly portfolio optimization. Cooperation announced.
Portfolio optimization is the process of selecting the optimal combination of assets such as stocks, bonds, and other financial instruments to achieve the best possible return for a given level of risk. Citi and Classiq use the Quantum Approximate Optimization Algorithm (QAOA) for portfolio optimization on Amazon Braket, a fully managed AWS cloud service.
“Enhancements include advanced modeling, design and application simplification of quantum algorithms for portfolio optimization. This could potentially lead to improved financial strategies by enabling more complex and nuanced risk-return analyzes that are computationally feasible through quantum computing,” Nir Minerbi, CEO of Classiq, told Metaverse Post.
In the current phase of quantum computing technology, known as the Noisy Intermediate-Scale Quantum (NISQ) era, quantum computers face limitations due to noise and number of qubits. This limits the capacity of quantum applications.
Therefore, the team explored transformative quantum algorithms, specifically QAOA. The focus was to use the QAOA quantum algorithm for portfolio optimization to investigate how adjusting the penalty factor of the algorithm (when introducing constraints to the problem) affects the performance of the algorithm.
“This study shows that adjusting the penalty factor used when introducing constraints in an optimization problem has a significant impact on algorithm performance. Specifically, there is an optimal range for penalty values over which the probability of obtaining a valid solution increases,” Minerbi said.
“For equality constraints, there is a maximum penalty value that reduces the likelihood of finding a valid solution. “This highlights the importance of fine-tuning the penalty factor in QAOA to improve portfolio optimization results,” he added.
Classiq’s SDK Transforms Quantum Portfolio Optimization
Classiq’s SDK simplifies quantum algorithm modeling by focusing on high-level functional models rather than low-level operations. According to Nir Minerbi, this abstraction will allow researchers and financial institutions to quickly and easily design and apply quantum algorithms for portfolio optimization, potentially accelerating the adoption and impact of quantum computing in finance.
He added that the Classiq engine, an algorithmic quantum circuit solving technology, powers the SDK.
By analyzing the results, the team gained key insights into optimizing the solution within portfolio constraints. It has been observed that adjusting the penalty factor has a significant impact on the efficiency of the optimization process.
“For inequality constraints, the probability of obtaining a valid solution increases continuously with the penalty factor, while for equality constraints there is an optimal penalty value,” Classiq’s Nir Minerbi told Metaverse Post.
It highlights the importance of fine-tuning algorithmic parameters and advocates exploring heuristic methodologies to improve the performance of quantum algorithms in financial applications.
“Amazon Braket played a critical role by providing on-demand access to simulators and quantum processing units (QPUs) to run complex quantum algorithms for portfolio optimization,” Minerbi explained. “This access enables practical experimentation and research on quantum computing applications in finance and accelerates the development and testing of quantum algorithms.”
Minerbi said of the next steps: “The next immediate steps include continuing to improve and test QAOA and other quantum algorithms for portfolio optimization, as well as potentially exploring new quantum computing technologies and scaling up options in the process. “It may involve evaluating,” he said. and using quantum computing for other financial use cases.”
“The potential impact includes solving previously intractable financial optimization problems, leading to more efficient markets, improved risk management, and potentially revolutionary changes in financial strategies and operations,” he added.
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Kumar is an experienced technology journalist specializing in the dynamic intersection of emerging fields including AI/ML, marketing technology, cryptocurrency, blockchain, and NFTs. With over three years of experience in the industry, Kumar has established a proven track record in crafting compelling narratives, conducting insightful interviews, and providing comprehensive insights. Kumar’s expertise lies in producing impactful content, including articles, reports and research publications for prominent industry platforms. With a unique skill at combining technical knowledge and storytelling, Kumar excels at communicating complex technical concepts in a clear and engaging way to diverse audiences.