Replacing Markowitz: A Quantum Approach to Portfolio Optimization(soma.biz)
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Replacing Markowitz: A Quantum Approach to Portfolio Optimization
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Cool Project. Been working on something similar in my undergrad days[1]. Is there a paper / repository showing the math and tech behind it?
[1]https://philippdubach.com/2024/03/15/my-first-optimal-portfo...
[1]https://philippdubach.com/2024/03/15/my-first-optimal-portfo...
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.
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.
[deleted]
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.