Future of work

Main findings

This research demonstrates that quantum computing can be harnessed to solve complex economic models, introducing new algorithms that run on existing quantum hardware and show promising performance compared with traditional methods.

Key takeaways

• Quantum computers can be used to solve complex economic problems by leveraging principles of quantum mechanics. 

• Quantum approaches could help overcome the constraints of classical (non-quantum) computing methods.  

• Although there are still significant accessibility and resource barriers, quantum computing shows enormous potential for solving complex, real-world problems.

Plain language summary

Our current computing systems impose constraints on what are known as “dynamic programming problems,” or DPPs. To oversimplify, these are problems that involve making a sequence of decisions over time, where each decision affects future outcomes. DPPs are common in areas like macroeconomics, which often involve large datasets and complex, evolving systems.

But recent advances in hardware and software have opened up the possibility of more computationally efficient problem-solving—and one of the most promising approaches is quantum computing. Quantum computing uses principles like superposition and entanglement to explore many possible solutions at once, potentially solving certain problems much faster than standard computing can.

The researchers ran their study on quantum annealers, a type of quantum computer with strong potential for solving economic problems. The aim was to address a key limitation of current methods: as problems grow larger, the time required to solve them increases dramatically.

The researchers found that their algorithms delivered accurate solutions with excellent execution times. They noted that, in contrast to current computing methods, execution times increased much more slowly as problem size grew. Their research is an important demonstration of the ability to use quantum computers to solve large and complex economic problems.

While quantum computing still faces significant challenges—including high costs, substantial resource requirements, and the need for extreme operating conditions—this study helps lay the groundwork for applying it to real-world economic problems.

Why the results matter

While quantum computers face significant barriers because of the intense resources needed to develop, run, and maintain them, this study demonstrated the value of quantum computing—specifically quantum annealers—for economic problem-solving. While it may be some time before these technologies are available to more researchers and the public, the study shows the promise of exploiting quantum properties to expand the scope and scale of analyses involving LMI.