Are you a travel industry executive with an imagination and penchant for scientific weirdness? Then part II in a series by theoretical physicist Joerg Esser is for you

Getting one’s head around what quantum computing is all about is, as I said last week in Part 1, a good place to start. As good as any, if you have an imagination and are interested in ‘weirdness’!

Quantum computing, you see, enables exponential increases in speed by harnessing the weirdness of quantum mechanics.

It was back in 1982 that Richard P. Feynman first suggested the idea of quantum computing.

"Nature isn't classical and if you want to make a simulation of nature, you'd better make it quantum mechanical," he wrote. Feynman claimed that classical computing is not suitable for modelling complex molecules. This still holds true today. For example, pharmaceuticals can only be tested by means of risky, real-life testing, not by simulations.

Feynman went on to say that quantum computing has the potential to broadly outperform classical computing. Why? Because in quantum mechanics everything is possible at the same time, while in the classical computing world everything is binary.

One of the key challenges for quantum computing, then, is to build robust systems at scale.

**The science behind it **

The key unit of quantum information is the qubit (‘**qu**antum **bi**nary dig**it**’). A classical bit can be in one state, either 1 or 0, whereas a qubit can be in two states simultaneously – a concept known as ‘superposition’ in quantum mechanics. Therefore, a string of two *qubits* can be in up to four states simultaneously (<00>, <01>, <10>, and <11>), each with a certain probability.

Two classical bits can only represent one of those four states at any given moment. In general, *n* *qubits *can be in two to the power of *n *states, as opposed to just one state for a string of *n* bits. This is what makes quantum computing faster than classical computing. It is expected that *n* in the magnitude of around 50 will be needed to outperform classical computers for general purposes.

However, that is only if the qubits keep their quantum properties perfectly. Clearly, quantum mechanics goes beyond anything we can imagine.

Clearly, quantum mechanics goes beyond anything we can imagine

Quantum effects are averaged out by thermal fluctuations, radiation and the sheer quantity of particles making up everyday objects. Preserving quantum behaviour at scale is a massive undertaking.

In 2012, Serge Haroche and David Wineland won the Nobel Prize for Physics for inventing ways to trap and manipulate particles while maintaining their quantum properties. This basically requires keeping particles away from heat and radiation, which is why such heavy machinery needs to be built around chips today. Particles need to be kept in a vacuum at temperatures far below -200 °C (-420 °F).

Many challenges must still be overcome before quantum computing becomes mainstream and delivers on its full potential

Many challenges must still be overcome before quantum computing becomes mainstream and delivers on its full potential. Research is on going in two areas.

1. Dealing with probabilistic states requires new ways of coding and new algorithms for processing information.

2. Fundamental progress is needed on building robust quantum systems at scale – systems that can hold sufficient amounts of *qubits* under suitable conditions, such as on a silicon chip at room temperature.

*Watch out for part III when consultant Joerg Esser, a former group director at Thomas Cook and a regular speaker and moderator on the EyeforTravel* *event trail**, considers how **quantum computing can improve machine learning*

**June 2018, London**