Who has an interest in considering a benchmark on a 64-qubit system as sufficient proof for large-scale investment decisions? The gain for researchers is academic validation, but for a hedge fund manager, it's risk-adjusted performance.
If everyone relies on small-scale benchmarks, the risk of a false positive becomes high, because incentives are misaligned between proof of concept and practical application.
For example, an optimized portfolio in the laboratory without considering transaction costs or market liquidity for thousands of assets would lead to a significant defection of the strategy in real-world conditions.

The single point of failure lies in the commercial viability beyond academic publication. Just because researchers are working on it doesn't mean a technology is market-ready or outperforms traditional methods. A huge gap separates fundamental research and a robust solution for investment portfolios managing billions. For example, an algorithm on an experimental quantum computer does not automatically translate into a reliable application for BlackRock or Vanguard. It requires error tolerance and scalability that current quantum systems simply cannot yet provide.

The single point of failure of this approach is the transferability from a laboratory environment to a real market. A test bench of 250 assets from the S&P 500, although admirable academically, does not account for the daily noise of markets. The stability and error correction necessary for the portfolio management of a Swiss pension fund, with its mandates of capital preservation, are far from being achieved with a 64-qubit system. This type of quantum optimization is a classic case where the attack surface of practical constraints is ignored.