For most scientists, a quantum computer that can solve large-scale business problems is still a prospect that belongs to the distant future, and one that won't be realized for at least another decade.
But now researchers from US banking giant Goldman Sachs and quantum computing company QC Ware have designed new quantum algorithms that they say could significantly boost the efficiency of some critical financial operations – on hardware that might be available in only five years' time.
Rather than waiting for a fully-fledged quantum computer, bankers could start running the new algorithms on near-term quantum hardware and reap the benefits of the technology even while quantum devices remain immature.
Goldman Sachs has, for many years, been digging into the potential that quantum technologies have to disrupt the financial sector.
In particular, the bank's researchers have explored ways to use quantum computing to optimize what is known as Monte Carlo simulations, which consist of pricing financial assets based on how the price of other related assets change over time, and therefore accounting for the risk that is inherent to different options, stocks, currencies and commodities.
Because of the vast spectrum of possibilities, this is one of the most compute-intensive tasks in finance, which requires making large numbers of predictions about different market movements.
Quantum computing has long been identified as a potential avenue to speed up those risk assessments thanks to the extraordinary compute power that the technology is expected to bring about in comparison to classical approaches.
And many quantum algorithms exist already, which have been shown to increase the speed of Monte Carlo calculations by up to 1,000 times and could transform the way that financial markets operate – but only once those algorithms are deployed on to a quantum device that is capable of running the program, and of achieving accurate results.
It's not only a matter of counting qubits: for quantum computers to resolve calculations reliably, the devices will also have to be optimized to avoid errors. Current quantum processors have very high error rates, and according to QC Ware, it will be 10 to 20 years before the error-corrected quantum hardware that is necessary to efficiently run Monte Carlo simulations becomes available.
To achieve this objective, the team traded off some calculation speed in return for some hardware gains.
The scientists designed two new quantum algorithms that slash the speed up from 1,000 times to 100 times – but they also require a shallower circuit size, which is expected to be available in the next five to 10 years.
"The Goldman Sachs and QC Ware research teams took a novel approach to designing quantum Monte Carlo algorithms by trading off performance speed-up for reduced error rates," said Iordanis Kerenidis, head of algorithms at QC Ware.
"Through rigorous analysis and empirical simulations, we demonstrated that our Shallow Monte Carlo algorithms could result in the ability to perform Monte Carlo simulations on quantum hardware that may be available in 5 to 10 years."
The speedup, although more moderate than that of other quantum algorithms such as the QFT-free Monte Carlo, is still significant; and according to the scientists, the method will effectively cut the timeline to usability in half.
Goldman Sachs and QC Ware's efforts are reflective of an industry that is increasingly focusing on bringing about the benefits of quantum computing in the near term, despite the imperfections that are still holding quantum devices back.
The two algorithms designed by Goldman Sachs and QC Ware, therefore, are yet another move towards the goal of finding quantum algorithms that are compatible with the noisy intermediate scale - NISQ – devices that are characteristic of current times.