Omar Lopez

Imagine the scene: a brilliant developer presents his new quantum toy, a model that perfectly optimizes a portfolio of 250 assets from the S&P 500. It's a beautiful laboratory story, but the real world story is much more complex, with unpredictable characters and unexpected twists. What works on a test bench with a development system may not survive the market storm, where human dynamics and exogenous shocks dictate the true narrative arc. For example, even the most sophisticated models could not anticipate unforeseen cyberattacks that suddenly paralyze entire sectors of the economy. Human intuition and adaptability remain key elements of a resilient portfolio.

Three characters in this market: engineers, traders, and regulators, each with their own narrative arc in the face of quantum optimization. Engineers proudly unveil a system capable of managing 250 assets from the S&P 500, an undeniable technical feat in the laboratory. Yet, the real challenge does not lie in the size of the portfolio but in the unexpected, the moment when the market story shifts, as during the flash crash of 2010. Regulators, for their part, question the performance of such technologies when liquidity disappears or a crisis of confidence shakes the foundations, like a seismic shockwave.

Scene one: Imagine a young researcher, full of enthusiasm, testing his new quantum toy. He takes 250 shares of the S&P 500, a carefully selected, almost sterile sample, but the market is never sterile. Just because the system works on a test bench doesn't mean it can handle unexpected shocks, monetary policy changes, or the irrationality of actors. For a sovereign fund like ours, proof is not just a technical demonstration, but a proven ability to navigate complexity and turmoil. It's like saying a racing driver is ready for war simply because he has won a race on a closed circuit.

The history of investing shows us that relying solely on a limited 'benchmark' is a precarious method for large-scale portfolio optimization.
Historically, models that failed in real-world conditions often did so after promising laboratory tests, such as the famous quantitative management funds before the 2008 crisis that did not anticipate liquidity shocks.
The old playbook emphasizes rigorous validation of strategies through complete economic cycles and diverse market conditions, including bear markets.
Claiming the viability of quantum optimization based on only 250 assets of an index is insufficient evidence; where are the tests on thousands of assets, exposed to geopolitical risks and various regulatory constraints?