Goldman Sachs Tries Quantum Applications for Financial Services

Investment bank Goldman Sachs and quantum computing company Quantum Motion have developed a quantum algorithm that could deliver an advantage over classical methods in pricing complex options.

Options pricing can be risky as it is based on variable market dynamics, volatility and time-sensitivity, which makes it highly complex and risky.

The process involves processing large amounts of data quickly and predicting the outcome of many possible scenarios in real time, which is challenging for classical computers but well suited to quantum computers.

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 offer an advantage.

The companies published research on their work, involving how intricate multi-qubit operations can be applied within pricing algorithms, which is currently undergoing peer review.

“People often don’t realize that even though quantum computers may sound like magic, in reality, it’s not enough to have just any machine,” said Quantum Motion corporate scientific officer Simon Benjamin.

“To have a real impact in sectors such as finance and pharmaceuticals, which involve exploring a huge space of possibilities and demand accuracy, quantum computers need to have a large number of qubits available at once and all of them capable of fast operations.”

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Most of today’s quantum computers only offer a small number of logical qubits, those that carry out the calculations rather than those needed for error correction.

This means software developers need to optimize their algorithms for fewer qubits, which can impact speed and the potential for delivering an advantage over classical computing.

Quantum Motion broke down the complex algorithms that make up quantum software – called oracles – into many small tasks that run simultaneously. This increases the number of qubits that need to be operating in parallel but reduces the time required to run the algorithm.

The technique devised by Quantum Motion could be relevant for many other applications, including those in chemistry and materials science. It could also help fast-track the development of future semiconductor-based quantum computers.

“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,” said Quantum Motion CEO James Palles-Dimmock.

“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.”

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