Quantum Computers Help Pick Better Stock Portfolios

In a world where investors face overwhelming choices, a new study shows how quantum computers can help identify promising stock portfolios from thousands of options. Researchers analyzed 3,171 U.S. common stocks using a mix of quantum and classical computing techniques, aiming to find efficient portfolios that could beat the market. This approach tackles a problem so vast that even the fastest supercomputers cannot explore all possibilities, making it a prime candidate for quantum s.

The key finding is that combining quantum annealing with classical solvers produced portfolios with better performance metrics than holding all stocks. Specifically, the researchers used the Chicago Quantum Net Score (CQNS) to evaluate portfolios, where lower scores indicate more attractive investments. For a 134-stock portfolio, the best CQNS score was approximately -3.14 × 10^-3, compared to a score of zero for a portfolio holding all 3,171 stocks. This means the selected portfolio was more efficient in balancing risk and return.

To achieve this, the team employed a two-step ology. First, classical solvers—including a genetic algorithm, simulated annealing, and Monte Carlo simulations—narrowed down the stock universe. Then, they used quantum annealing on D-Wave's Advantage computer, which has 5,760 qubits, and a simulated bifurcation machine (SBM) to refine the selections. The SBM, coded in-house, mimics physical processes to solve optimization problems quickly, completing 10,016 iterations in just 232 seconds on standard hardware. This hybrid approach allowed them to handle the immense solution space of over 10^954 possible portfolios.

, Detailed in figures from the paper, show that s consistently found portfolios with lower CQNS scores. For instance, in a two-stock example, Axos Financial (AX) and Seres Therapeutics (MCRB) were selected. Over 14.5 trading days, AX increased by 6.8% while MCRB fell by 9.5%, resulting in an average portfolio drop of 1.4%—less than half the S&P 500's 5.5% decline during the same period. Figure 3 illustrates how these stocks moved in opposite directions, straddling market indices and providing a hedge against volatility. The quantum annealing runs, though limited by embedding s, still found valid solutions, such as a three-stock portfolio with a CQNS score of -1.69 × 10^-3.

This research matters because it demonstrates a practical application of quantum computing in finance, where speed and efficiency can lead to better investment decisions. For everyday investors, it means that advanced algorithms could one day help manage risk in diversified portfolios without requiring expert knowledge. The ability to process vast datasets quickly could also benefit other fields like logistics or drug , where optimization is key.

However, the study has limitations. The researchers note that they cannot guarantee a universally optimal solution due to the problem's scale, and the quantum annealing faced issues like high chain break rates and long wait times—up to 10 minutes between runs. Embedding success was variable, with only 11 out of 15 attempts succeeding for 134 stocks, and the SBM did not replicate top performance on large problems as it did on smaller ones. These hurdles highlight that while promising, quantum s are still evolving and require further refinement for widespread use.