The Wall Street giant worked with UK scale-up Quantum Motion on the research, which explored how intricate multi-qubit operations can be applied within pricing algorithms. Traditional computers struggle to price options accurately when processing large amounts of data quickly or exploring a large number of possible scenarios. Goldman Sachs worked with Quantum Motion to develop an efficient algorithm, including researching the necessary software and hardware capabilities, to enable quantum computations fast enough to give the bank a winning advantage.Quantum Motion presented a method in which the complex algorithms at the heart of quantum software - called oracles - can be broken down into many small tasks that run simultaneously. This increases the number of qubits that need to be operating in parallel, but correspondingly reduces the time required to run the algorithm. The firm notes that the improvement in runtime can be vital for applications - such as those in financial services - where the time, usually in the scale of seconds, is critical for delivering quantum advantage.“The strategy at Quantum Motion is to deliver a scalable, integrated quantum architecture capable of building systems of sizes yielding real value. The components of our quantum chips are at the same minute scale as conventional transistors, which gives the potential for vast numbers of qubits on a single chip,” says James Palles-Dimmock, CEO of Quantum Motion. “Working alongside end-users, such as Goldman Sachs, enables our researchers to understand the quantum hardware requirements, often stretching to many millions of physical qubits, that are needed to run quantum algorithms that can deliver transformative impact for business.”