Quantum Computing for Portfolio Optimization: Where Quantum Meets Finance

A new white paper from SC Quantum and qBraid looks at portfolio optimization through a quantum lens

(SC Quantum is an IYQ partner.)

Portfolio optimization is a critical part of finance. It helps investors decide how to allocate capital while managing risk. Pensions, funds, and advisors use it every day to build and rebalance portfolios. For years, classical computing has supported this work, but that approach is starting to hit its limits in more complex environments.

The new white paper from qBraid and SC Quantum explores how quantum computing could support this work moving forward. It focuses on portfolio optimization, where quantum algorithms are already being tested in both research and industry settings.

How Quantum Could Help

Quantum algorithms are being designed to take on problems that are hard for classical systems to handle. In finance, that includes portfolio decisions with many assets, constraints, and market variables. Some of the most active research centers on methods like VQE and QAOA. These approaches aim to produce good solutions faster and at larger scales.

The paper walks through how these methods work and where they might be most useful. It also gives a realistic view of the hardware today and the challenges that still need to be addressed.

Industry is Already Moving

Several firms have started testing quantum tools for portfolio work. IBM developed a quantum optimizer using VQE and post-processing to improve output. AWS and Goldman Sachs ran a full analysis of the Quantum Interior Point Method, outlining what would be needed to make it practical. J.P. Morgan worked on a modified version of the HHL algorithm and used it to solve small portfolio problems on a trapped-ion system.

These examples show early signs of progress and give a sense of what future applications could look like.

Why This Matters in South Carolina

South Carolina is home to a range of financial institutions, including the state’s public pension system. The South Carolina Retirement System Investment Commission manages $40 billion across public and private markets. Like other institutional investors, RSIC has to balance long-term growth with short-term risk and liquidity. That work gets harder as markets change and data sets grow.

Banks, credit unions, and advisors across the state face similar challenges. They are already using optimization tools to manage portfolios under capital rules, risk thresholds, and return targets. As new methods become available, South Carolina’s financial ecosystem is well positioned to explore how they could help.

Get the White Paper

This is the second white paper in a series from qBraid and SC Quantum focused on how quantum technologies are being applied in real-world settings. It is written for people working in finance, tech, and anyone curious about how quantum tools might fit into familiar problems.