/QC Ware and Goldman Sachs announce quantum algorithm breakthrough (via Qpute.com)
Quantum computing firm QC Ware, Goldman Sachs in asset pricing breakthrough

QC Ware and Goldman Sachs announce quantum algorithm breakthrough (via Qpute.com)


QC Ware, the Palo Alto-headquartered, privately held quantum computing firm whose investors include Airbus Ventures, along with financial services giants Citigroup, Goldman Sachs and DE Shaw, has announced a major breakthrough in the algorithmic calculations used to price financial assets. In an announcement on Thursday, QC Ware said that algorithms it developed with Goldman Sachs have outperformed classical Monte Carlo simulations historically used to model financial market prices and risks. The company says these algorithms will be usable on near-term hardware that should be commercially available in the next 5-10 years.

Monte Carlo methods involve complex calculations that are time- and computationally intensive, and (because of this) are typically performed by investment firms only once overnight. In volatile, fast-changing market conditions, this may result in algorithms yielding out-of-date results.

Quantum computing, the emerging computer science, is thought to enable faster computation of huge datasets, performing Monte Carlo simulations 1000 times faster than classical methods. The problem is that these algorithms can only be executed using error-corrected quantum hardware, which is thought to be another 10 to 20 years away in terms of hardware development.  Current quantum devices have prohibitively high error rates and can only perform a few calculation steps before returning inaccurate results. Over the past year, QC Ware and Goldman Sachs have been tackling the issue of how to cut the timeline for quantum hardware in half, while also gaining speed.

The companies opted to increase simulation speed from the classical Monte Carlo simulation to a factor of 100, rather than 1000, so these calculations can be performed on hardware available much sooner. QC Ware says this could enable market simulations to be executed throughout the day, closer to real-time.

“The Goldman Sachs and QC Ware research teams took a novel approach to designing quantum Montecarlo algorithms by trading off performance speed-up for reduced error rates,” QC Ware’s Head of International Algorithms Iordanis Kerenidis said. “Through rigorous analysis and empirical simulations, we demonstrated that our Shallow Monte Carlo algorithms could result in the ability to perform Montecarlo simulations on quantum hardware that may be available in 5 to 10 years.”

“Quantum computing could have a significant impact on financial services, and our new work with QC Ware brings that future closer,” said Goldman Sachs’ Head of Quantum Research William Zeng. “To do this, we introduced new extensions to a core technique in quantum algorithms. This exemplifies the fundamental contributions that our group looks to make in the field of quantum technology.”

Zeng said the Goldman team remained focused on developing “the best technology for the firm and our clients.”

Air Force!

Earlier this week. QC Ware announced that it is working with the U.S. Air Force Research Laboratory (AFRL) to use one of QC Ware’s quantum machine learning algorithms (q-means) to understand the mission objective of unmanned aircraft by observing the craft’s flight path. It is believed that q-means (used in clustering and classification) can be applied to multiple AFRL mission applications.

The project is part of a larger AFRL effort to engage with expert researchers in industry, academia, and the Department of Defense to apply quantum information science to Air Force and Space Force concerns and ensure they remain the most advanced and capable force in the world.

“AFRL is pleased to partner with QC Ware on the development of quantum machine learning algorithms. Early and continued investment in quantum software firmly aligns with AFRL’s quantum strategy. As quantum computing hardware continues to rapidly advance and become more practical, we believe that these types of algorithms will readily find applications to real-world Air Force scenarios,” said Dr. Mike Hayduk, Deputy Director of the AFRL Information Directorate.


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