Stories of Quantum Computing revolutionising modelling are everywhere, and the parallels from quantum to Monte Carlo seem tempting. But how will stochastic modelling work, and how close are we to having working models? Is this the technology to place your bets on?
The fourth article in our occasional series looks into this new opportunity.
Quantum computing is a new field of computer science that uses quantum mechanics to solve complex problems. Classical computers use “bits” that take a binary state – 0 or 1 – to represent information. Think of them as similar to a coin at rest that can be thrown to result in either heads or tails landing upwards. By contrast, quantum computers use quantum bits (qubits) that can exist in multiple states simultaneously, like a spinning coin. Qubits can also be entangled, meaning the state of one can directly influence the state of another.
This enables the development of a whole new set of algorithms beyond the capabilities or performance of traditional computers.
However, the compute step time of quantum computers is in the order of milli- or micro-seconds compared to the nanoseconds of traditional computing. With parallelisation of the more plentiful older technology, a classical system can be seven to eight orders of magnitude faster than a quantum step.
Prototype quantum computers have been built, and these are accessible over cloud platforms. However, while these computers are theoretically capable of delivering, they are hindered by high noise levels (random errors), making superior use of them unattainable. To address this issue, quantum computers often require several magnitudes more physical qubits than the logical qubits presented to the user.
The algorithms can be built and tested on traditional computers too. There are software development kits that enable quantum programming and quantum simulators that simulate quantum computers on high-end traditional computers.
To date, quantum computing is being used to solve problems with complex interactions, including systems optimisation, drug research and materials research. It can also be used in cryptography, to both create and break encryption methods.
Research in the last five years showed that quantum computers can calculate a value at risk (VaR) on an asset portfolio with a quadratic speed-up compared to traditional computers. That is, if traditional computers are N2 steps, then quantum is in N steps. However, this is a very simplified case.
Further details can be found in this paper - Quantum internal models for Solvency II and quantitative risk management
One conclusion of this paper is that the case study requires approximately 36 billion physical qubits to perform useful computation using today’s noisy qubits and known error correction schemes. The largest quantum computers today have around 6,000 qubits. By comparison, this is a problem that can be solved on a personal laptop within minutes/seconds.
In addition, as with graphical processing units (GPUs), quantum computers currently have significant limitations where it is necessary to input or output large volumes of data.
Practical applications of quantum computing are therefore many years away, and most likely to first emerge with algorithms that are exponentially faster than traditional approaches.
This summary was correct as of December 2025.
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