QC Ware, a quantum computing-as-a-service company, is working in collaboration with Goldman Sachs to gain in-depth knowledge on the near term impact of quantum computers and on the development of new algorithms that will enable quantum computers to outperform concurrent classical computers for computational finance applications. The primary goal is to stay current and develop in-house quantum expertise to gain a “quantum advantage” once the technology is ready for commercial use.
“During the past year, researchers at QC Ware and Goldman Sachs have worked on analyzing the effect of noise on the accuracy of quantum algorithms for approximate counting,” said Paul Burchard, lead researcher for R&D at Goldman Sachs, in a statement. “The research confirmed that the current state-of-the-art quantum algorithms for Monte Carlo sampling and approximate counting will eventually lead to more efficient simulation, but that these algorithms are sensitive to noise in current quantum hardware. As a result, implementing these algorithms on near term quantum hardware will depend on techniques analogous to importance sampling that reduce the circuit depth of these algorithms.”
“QC Ware’s work with Goldman Sachs is essential to gaining a better understanding of how quantum computing algorithms can eventually be used in finance and how to make the practical use of quantum computing a reality faster,” said Matt Johnson, CEO of QC Ware , in a statement.
“QC Ware believes that quantum computing will significantly impact the future of finance,” said Wim van Dam, head of Quantum Algorithms at QC Ware, in a statement. “Current quantum computers are limited in the number of qubits and the circuit depth that they support. We are focused on applying QC Ware’s expertise in meeting this challenge by delivering access to QC Ware’s Forge cloud service to test near-term quantum applications and help build in-house quantum computing skills.”