Leo Sato

There are two categories here: the proof of concept and the operational viability.
A test bench on 250 S&P 500 assets with 64 qubits clearly falls into the first class, interesting but far from our needs.
Managing multi-billion dollar portfolios requires a scalability and robustness that this type of simulation cannot reproduce.
For example, for a sovereign fund like ours, we manage thousands of securities, requiring a depth and an adaptability to macroeconomic shocks that far exceed what is tested here.
The implications for long-term liabilities and risk management are neglected by such an approach.

First, we need to categorize the contribution of this research: it falls under proof of technical concept, a distinct class from operational application in finance.
Benchmarking a workflow on 250 S&P 500 assets on a 64-qubit system is a laboratory feat, but it does not address the complexities of liquidity or market robustness in real markets.
Managing large Australian pension funds, for example, requires scalability and resilience that far exceed the current capabilities of this emerging technology, where returns on capital must be not only optimized but also stable in the face of market shocks.

The concept of "250 assets" is a too narrow base for large-scale portfolio optimization, as time is limited for these academic exercises. We manage thousands of instruments covering multiple asset classes and geographies, where liquidity can be a dwindling resource rather than just a parameter. A sample so small masks the true challenges of risk management and diversification in a complex macroeconomic environment. For example, consider our exposure to European government bonds; a simple simulation on 250 assets will never predict the impact of yield variations on the entire portfolio. Attention is a bottleneck, and focusing on such limited test beds ration our intellectual resources poorly.