Hi,
Your blog is very interesting. However, the approach you described is a classical one: most current portfolio optimization software uses the Markowitz Mean-Variance framework to determine the optimal allocation of capital among preselected assets. This method works well for large portfolios and for investors with a strong finance background, as they typically know in advance which assets to include based on their analysis.
However, this approach has limitations. It requires asset managers to spend significant time and effort selecting the assets beforehand, and it cannot explore all possible combinations of assets, which means the resulting portfolio might not be globally optimal. In classical computing, an exhaustive search of all possible combinations is called brute force, but this quickly becomes impractical: for example, considering just 10 assets with simple inclusion/exclusion yields 2¹⁰ = 1,024 combinations, and the number grows dramatically if allocation weights are included.
This is why quantum portfolio optimization (the approach used by our platform) is innovative: it leverages quantum computing to explore many combinations simultaneously, enabling the optimization of large portfolios in real time, even for users without deep financial expertise.
Your blog is very interesting. However, the approach you described is a classical one: most current portfolio optimization software uses the Markowitz Mean-Variance framework to determine the optimal allocation of capital among preselected assets. This method works well for large portfolios and for investors with a strong finance background, as they typically know in advance which assets to include based on their analysis.
However, this approach has limitations. It requires asset managers to spend significant time and effort selecting the assets beforehand, and it cannot explore all possible combinations of assets, which means the resulting portfolio might not be globally optimal. In classical computing, an exhaustive search of all possible combinations is called brute force, but this quickly becomes impractical: for example, considering just 10 assets with simple inclusion/exclusion yields 2¹⁰ = 1,024 combinations, and the number grows dramatically if allocation weights are included.
This is why quantum portfolio optimization (the approach used by our platform) is innovative: it leverages quantum computing to explore many combinations simultaneously, enabling the optimization of large portfolios in real time, even for users without deep financial expertise.
Oh thank you so so much for sharing your research on related topic. I will read it and let you know more :). Thank for drop me feedbacks :). Please have a very great day.
Thanks for your interest! I’m happy to explain how it works: in the current MVP, users select stocks manually, and the system optimizes the portfolio dynamically based on the selected period. In the full product, users will choose an investment universe, and the system will automatically fetch assets and update the optimization in real-time.
The core quantum approach allows exploring many portfolio combinations simultaneously—as shown in the table of asset combinations in our platform—which makes the optimization faster and more reliable than classical methods.
I’m keeping the code private for now, as it’s part of the core product. For context, our startup is incorporated in Switzerland and fully compliant, so you can be assured this is a serious, trustable project. I’m happy to answer technical questions about the approach or share high-level algorithm insights.
Thanks for your great question! . In the current MVP, users select the stocks manually, but the optimization does update dynamically if you change the analysis period — so the portfolio adapts to the data for the selected range. In the full product, users will simply choose an investment universe, and the system will automatically fetch relevant assets and update the optimization in real time or historical time based on user input. The quantum approach allows us to explore many combinations simultaneously, which makes the optimization faster and more reliable than traditional methods.
However, this approach has limitations. It requires asset managers to spend significant time and effort selecting the assets beforehand, and it cannot explore all possible combinations of assets, which means the resulting portfolio might not be globally optimal. In classical computing, an exhaustive search of all possible combinations is called brute force, but this quickly becomes impractical: for example, considering just 10 assets with simple inclusion/exclusion yields 2¹⁰ = 1,024 combinations, and the number grows dramatically if allocation weights are included.
This is why quantum portfolio optimization (the approach used by our platform) is innovative: it leverages quantum computing to explore many combinations simultaneously, enabling the optimization of large portfolios in real time, even for users without deep financial expertise.