The idea of quantum computing has been discussed in scientific quarters for some years yet the technology remains in its infancy. But the race to become the first to market with a viable commercial offering remains a heated contest.
Commercial use cases of the technology appear some way off. In a data driven world, the need for advanced processing power is becoming a real concern globally. And as the volume of data is set to rise, the need for greater computing power to process all of this data is only going to increase.
A stark contrast between classical and quantum computers is the method of processing information. While a classical computer will solve a problem by processing every possible solution one at a time, quantum computers focus on solving problems through probabilities. Quantum computers rely on finding patterns and correlations between data, to come to a solution. As opposed to classical computers which compute information in bits that take the value of 0 or 1, quantum computing uses bits or qubits meaning that bits can exist as 1 and a 0 at the same time.
Recently there have been key moves forward in deploying a use case for the technology in financial markets by the technology giants IBM, Google, and Microsoft.
On October 23, Google announced the results of its quantum supremacy experiment. It created a 54-qubit processor to perform testing with the aim of demonstrating the design of a quantum system that was both powerful and programmable. Google said the processor “performed the target computation in 200 seconds, and from measurements in our experiment we determined that it would take the world’s fastest supercomputer 10,000 years to produce a similar output.”
In reaction to Google’s announcement, IBM said “that an ideal simulation of the same task can be performed on a classical system in 2.5 days and with far greater fidelity.” IBM argues that the definition of quantum supremacy is to “describe the point where quantum computers can do things that classical computers can’t” and “this threshold has not been met.”
But it isn’t just the technology giants competing to crack the quantum computing world. In Early September a former Bristol professor, Jeremy O’Brien, raised $230m for his Quantum computing company, PsiQuantum, according to data provider Pitchbook.
While commercial use cases may be someway off, there are endless possibilities of ideas that have been sparked by the advanced computer processing power.
JP Morgan is investigating the use of quantum computing in option pricing. The bank hopes that the use of quantum computing could reduce costs and speed up the amount of simulations necessary to calculate accurate option prices.
Willis Tower Watson joined Microsoft’s quantum network in May to use quantum algorithms to create risk management and financial services solutions. The initiative is also assisting Willis Tower Watson clients to allocate capital more resourcefully, Ben Porter, director of business development for quantum computing at Microsoft told a panel at Money 2020 US.
On October 15, Barclays and IBM announced that they had successfully tested securities settlement cycles using quantum algorithms, with the aim of increasing the number of transactions settled or to maximise the total value of settled transactions. While it was acknowledged that the algorithms used in the research were “exploiting only very little of the structure of a problem,” the researchers said that by restricting the algorithms to problems with certain variables, it may be possible to get algorithms that could outperform current solutions.
And national governments are wading into the space with significant investments into research of the technology. At the end of 2018, the US government enacted the National Quantum Initiative Act with the aim of investing $1.2bn over five years into quantum research. In June, the UK government announced an investment of £1bn into the National Quantum Technologies Programme. China has filed nearly twice as many patents for quantum computing technologies than the US, according to the New York Times.
Quantum computing is also expected to unleash the full potential of artificial intelligence. Efficient use of machine learning relies on masses of data. The more granular and categorised that data is, the more potential that can be gained from machine learning. With their high processing power, quantum computers are expected to provide this higher categorisation.
In March, IBM Research in collaboration with MIT-IBM Watson AI Lab said they had been testing a quantum algorithm that has the “potential to enable machine learning on quantum computers in the near future.” The research also included the potential use of quantum computers in feature mapping – the ability to break down data to its most essential features and prioritise those that are most important. It is hoped quantum computers will be able to create new classifiers and identify patterns in the data that are currently not visible to classical computers.
Google have also been experimenting with the development of a mix between quantum and classical machine learning techniques on near-term quantum devices.
Hurdles to overcome
Quantum computers have some way to go. According to IBM Research, Quantum computers’ qubits currently cannot last more than a few hundred microseconds in a quantum state – the ability to provide the probabilities of outcomes for each possible measurement on the system. This is crucial to the development of quantum computers as the qubits must stay in a quantum state for as long as possible to perform calculations.
There are also concerns around the potential of quantum computing empowered encryption breakers. With advanced processing power, quantum computers may be able to make encryption technologies used by classical computers obsolete. This has spurred the industry to investigate quantum-proof security.
There are also challenges around the tools that are used to control qubits, which can result in stochastic errors that negatively impact the performance of quantum circuits. According to Google, quantum computing controls depend on “the development of a quantum control function and an efficient optimisation method based on deep reinforcement learning.”
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