How multiphysics simulation might accelerate the advent of quantum computing – and vice versa

Every quantum computer is unique – both in the way it’s designed and in how it behaves once implemented. First, designers must choose an approach to implementing qubits. Two of the most popular approaches are superconducting loops and trapped ions. Superconducting qubits are used by IBM, Google and Intel. The best-known company using trapped ions is Honeywell. 

Then, within a given approach, there are several design alternatives. Designers must choose materials – and they must choose the number of qubits, the arrangement of those qubits, how the qubits are initialised and how they are read.  

Once a quantum computer is implemented, it takes on a personality of its own, based on how noisy each of the qubits turn out to be. Several factors contribute to how noisy a qubit becomes – including the types of materials used, the fabrication process and the environment in which the qubit operates. 

Most industrialised countries are investing in research in quantum computing, and many of the large tech companies are doing so on their own. The US and China are the two countries that are furthest ahead in research and development – and three of the companies that currently lead the pack in one way or another are IBM, Google and Honeywell. 

The promise of quantum computing is that it can be used to solve certain types of problems in just a few minutes – problems that would take the world’s fastest supercomputers thousands of years to solve. Two such problems are optimisation problems (for example, airline scheduling) and simulating quantum phenomena in nature (such as the behaviour of biological molecules). Solving these two kinds of problem would be worth a fortune to several industries – logistics, finance and pharmaceuticals to name just a few.

Most of the time, when a disruptive technology comes along, developers put a huge amount of time into finding the optimal approach. Eventually, one of the approaches dominates and becomes what is sometimes called the dominant design. Once the dominant design is established, the market expects virtually all products to follow it. 

A dominant design for quantum computers is still a very long way off. In the meantime, research institutes and companies are busy experimenting with the different options. They all know that one day quantum computing will be a huge game changer – and nobody wants to be left behind. 

While quantum computing is still in what is known as the fluid phase – the period where a lot of experimentation is being done to find out what works best – a lot of money is put into to trying new designs. Moreover, the turnaround time – the time an idea is conceived to the time it can be tested – is very long. New hardware is developed and tested, changes are made based on the test results, and then a new version of hardware is built. Several iterations are usually required before a good design is found. 

In most fields, tools are available to allow researchers to simulate new designs, so they can try out new ideas very quickly without building hardware. But with quantum computing, the simulation is more complicated. A tool that simulates only one type of physics (for example, electromagnetics or fluid dynamics) is insufficient. Multiphysics modelling is required. Such models combine equations that make predictions about different phenomena independently, to come up with overall predictions given the interaction of the different effects.

One company that is trying to apply its know-how is Quanscient, a Finnish-based startup that started out with the fruits of several years of academic research in multiphysics modelling. Just last year, chief technology officer and co-founder Alexandre Halbach took what he learned from his research and applied it to the new company’s first customer. 

“Our first pilot customer was a Finnish startup developing intelligent eyeglasses,” said Juha Riippi, CEO of Quanscient. “If you look nearby, the lens is adapted to a closeup view – and then if you look far away, the lens is adapted to a longer view. It works by directing an electric field to the lens to change its shape based on the need.

“We helped this company by allowing it to simulate different design choices, which saved them time and money in product development. This required multiphysics. You have to simulate both how the electric field behaves and the mechanical reaction of the lens when an electrical field is applied.”