Vice President of AI & Quantum Computing, Paul Smith-Goodson gives his insights on the collaboration between JPMorgan Chase and QC Ware on Quantum finance breakthrough.
Present-day quantum computers are middle-to-late stage prototypes equipped with a limited number of noisy qubits that produce many errors and operate with low fidelity. By contrast, truly useful finance applications all have complex computations that need huge numbers of high-fidelity qubits and some form of practical error correction in order to run them.
This project was the first collaboration between JPMorgan Chase and QC Ware. I had the opportunity to discuss the new deep hedging algorithm and how it was developed with two key members of the research team: “We are very happy with the results,” Dr. Pistoia said. “Of course, we cannot use this algorithm in today’s production environment because the hardware is not yet up to speed. However, the essence of our work is to become quantum ready. So we build the algorithms and then when the hardware catches up, we have the algorithms ready. And, we're also happy because in this case we have a higher-quality solution.
Because the model's objective is to minimize risk, it is important for it to have access to the most-probable futures as well as futures with only a small probability. Whatever the actual future turns out to be will determine whether the model’s valuation of the asset results in a profit or a loss. The distributional actor-critic algorithm uses two elements, actor and critic, to determine the best trading strategy. The actor represents the trading strategy and decides what action to take—either buy, sell or hold—for any given market condition. The critic evaluates the actor’s decisions by estimating the distribution of potential returns, while also considering the risk and uncertainty of the transaction.
Dr. Kerenidis put the technique in perspective. “What we found out from this research is that quantum is actually natively very good in a distributional approach because quantum states can naturally hold large distributions,” he said. “This was the right framework to use, and because it's so natively quantum, quantum reinforcement learning, and in particular the distributional actor-critic algorithm, is very effective and produces very good results.
In this instance, the quantum hedging researchers used Black-Scholes as a baseline model for comparison. Since the assumptions of Black-Scholes sometimes differ from actual market conditions, Dr. Kerenidis said the team added frictions in the market such as transaction costs. “When we say that we have good results, that means we can do more than 10% better than Black-Scholes,” he said. “We found that our quantum methods can improve over Black-Scholes’ basic strategy.
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