Applications for Quantum Computing in Portfolio Management

By: Elena Vlitas

Managing a balanced portfolio providing satisfactory returns is a lucrative skill sought after by individuals and corporations alike. The emergence of digital technologies and advancements in computational power has enabled traders to make faster and more informed decisions over the past decade. Portfolio management fits into quantum computing’s unique ability to harness superior optimization power, such that it may revolutionize the landscape of portfolio investment in the next decade and beyond.

Portfolio management is a delicate art of balance; one must effectively compose and continuously adjust a portfolio catered to an investor’s financial goals and risk tolerance. Just as equally, portfolio management is a science which requires precise data, careful calculation, and fundamental analysis of underlying assets. Values and returns of assets such as stocks, bonds, commodities, real estate, and more, are vulnerable to dynamic external factors such as economic conditions, geopolitics, consumer tendencies, and so much more. This requires portfolio managers to have a wide range of up-to-date data to inform their highly consequential decisions.

Traditionally, portfolio managers have used methods such as Mean portfolio Theory and Mean Variance Analysis to optimize their portfolios. However, with the rapid increase in the quantity of financial data available and the increasing complexity of the financial markets, it has become more challenging for these traditional models to effectively and accurately balance risk-return tradeoffs.

There are numerous other challenges and complexities that exist within the scope of portfolio management which quantum computational systems have the potential to address. The vast quantities of changing data has led to data complexity which makes it difficult to effectively process it all and make up-to-date predictions. Market volatility adds another layer of complexity to portfolio optimization as changes in one sector can greatly affect those of other sectors.

Because quantum computing takes a fundamentally different approach to information processing, it can overcome these issues. Bits (0s and 1s) are used in classical computers to process data sequentially. On the other hand, qubits — which can simultaneously represent 0 and 1 — are used in quantum computing. This provides exponential speedups for some issues, especially optimization scenarios, as it allows quantum systems to process several options simultaneously. Entanglement, which enables qubits to be coupled so that the state of one instantaneously affects the state of another, regardless of distance, is another essential component of quantum computing. Because of their interconnection, quantum systems are far more efficient than classical systems at performing intricate calculations on big datasets. With this computational power, quantum computers can complete optimizations in a fraction of the time compared to a classical computer. This is particularly useful for determining correlations, solving quadratic optimization, and predicting risk across assets.

A traditional approach to testing portfolio compositions is the Monte Carlo simulation to estimate risks and pricing. The Monte Carlo method can be enhanced using the variational quantum eigensolver (VQE), making it more effective than the same simulation run on a classical computer. This method is commonly known as the Variational Monte Carlo (VMC), which uses the wave function of subatomic particles as a basis to solve complex optimization problems.

Despite the opportunities, various barriers and risks currently exist to using quantum computers for portfolio management. First, as a novel technology, there are very few programmers with the skill and expertise to create quantum portfolio optimization algorithms. Also, quantum computers are prone to errors, and more effective error-correcting mechanisms are still underdeveloped at this stage of the technology. Error rates are high, so while optimization upsides can be fruitful, downsides could be detrimental if unnoticed. Currently, quantum computing comes at a high cost, so these optimization technologies may not be as accessible for small firms, relative to top financial institutions creating a disadvantage for some investors.

Various financial institutions and tech companies are beginning to explore and adopt quantum technologies for their portfolio management. A leader among these companies is D-Wave Systems. D-Wave is a computer software company whose clients include financial services firms as well as some in other industries such as Lockheed Martin, Google, and NASA. Huge players Goldman Sachs and JPMorgan have been early to explore quantum technologies to improve their portfolio strategy and increase simulation speeds.

While still in its early stages, it is critical for all professional and retail investors to become familiar with quantum computing and understand how it can be leveraged to improve investment performance. Quantum computing is poised to play a significant role in managing data complexity and investment risk once adoption challenges are addressed. In the art of balancing risk and reward, quantum technologies might support the next generation of Picassos.