Quantum computing has been gaining attention in recent years for its potential applications in various fields, including finance. However, quantum computers are still in the early stages of development, and they cannot yet outperform classical computers for most tasks. That’s why researchers have been exploring hybrid classical-quantum algorithms that combine the strengths of both types of computers to solve complex problems.
In finance, one of the areas where hybrid algorithms show promise is portfolio optimization. Portfolio optimization aims to find the optimal allocation of investments for a given level of risk or return. This problem becomes more challenging as the number of assets increases because there are exponentially many possible portfolios to consider.
Classical methods for portfolio optimization rely on heuristics or approximations that may not always yield satisfactory results. In contrast, quantum-inspired algorithms like Quantum Approximate Optimization Algorithm (QAOA) can exploit the unique properties of quantum mechanics to efficiently search through vast solution spaces and find better solutions.
Researchers at IBM recently demonstrated how a hybrid algorithm combining QAOA with classical machine learning techniques could provide superior performance compared to traditional approaches for portfolio optimization. The team used historical data from 50 stocks to build a model that predicts future returns based on past prices and volatility. They then trained QAOA to optimize portfolios using this model while considering transaction costs and constraints such as minimum and maximum weights for each asset.
The results showed that their approach yielded higher returns than other methods while maintaining similar levels of risk. Moreover, they achieved these results with much smaller portfolios than what was required by traditional methods, which translates into lower transaction costs and faster execution times.
Another area where hybrid algorithms hold promise is option pricing, which involves calculating the fair value of financial derivatives such as options contracts based on underlying assets’ prices and volatility. Option pricing requires solving partial differential equations (PDEs) that describe how an option’s price changes over time under different scenarios.
Classical numerical methods for solving PDEs can be computationally expensive and require large amounts of memory. Hybrid algorithms like Quantum Monte Carlo (QMC) leverage the power of quantum computers to sample from probability distributions that represent solutions to PDEs, making them more efficient than classical methods.
Researchers at Cambridge Quantum Computing recently demonstrated how they could use a hybrid QMC algorithm with classical machine learning techniques to price options faster and with higher accuracy than traditional methods. Their approach allowed them to handle more complex option types while reducing computational costs by up to 90%.
Overall, hybrid classical-quantum algorithms are still in their early stages, but they show great promise for solving some of finance’s most challenging problems. As quantum computers continue their development and become more widely available, we can expect these algorithms’ performance to improve further and find even broader applications in finance and beyond.