Here but not here: what businesses need to know about quantum computers

That academic theory of QC is usually explained by saying that where a normal computer operates using bits of information, a quantum computer uses quantum bits or “qubits”. A normal bit is 1 or 0, on or off: a qubit is much more complicated.

When it is measured it will be either 1 or 0; before that, it exists in a quantum superposition of those two states. The quantum superposition is usually described using “complex numbers”, mathematics based on the so-called “imaginary unit”, the square root of minus one.

Another way of visualising this is that normal bits are like coins lying on a table. They are either heads or tails up: they can be flipped over. A qubit, however, is like a coin spinning in the air. It can interact with other spinning coins, affecting how they spin, but none of them are heads or tails up until the quantum operations are complete.

Theoreticians can describe what qubits will do in a network of quantum logic gates, even if they don’t have any actual machinery capable of carrying out the process.

As a result, algorithms can be, and have been, developed for QC machinery even before there was any – rather in the way that Ada Lovelace famously wrote some of the first conventional computer programs for Charles Babbage’s proposed 19th-century mechanical computer, the Analytical Engine, even though it was never actually built.

Thus we know many of the things that QC could achieve. Its effects, when it becomes available at appropriate scale, will be enormous. Quantum computers will find a use anywhere there is a large and complicated problem to be solved. That could be anything from predicting the financial markets, to improving weather forecasts, to cracking encryption systems.

Privacy advocates already fear that quantum computing could one day crack today’s secure encryption and the many things built on it. Those with a stake in cryptocurrency may naturally be concerned, according to GlobalData analyst Sam Holt.

“Bitcoin and other cryptos use an ‘elliptic curve’ signature scheme where public and private encryption keys are used to verify transactions,” Holt explains to Verdict.

“Older signature tech doesn’t hash (fingerprint) the public key and this can therefore be known by anyone. Around 25% of bitcoins are stored using this older tech, and are vulnerable.

“At the moment, it remains difficult for bad actors to find out the private key. As early as 2027, however, quantum computers could be at the point where they could use the public key to break the encryption. It could take only one quantum-crypto-heist for investors to lose confidence.”

Before this happens though, fellow GlobalData analyst Mike Orme forecasts post-quantum cryptography (PQC) will have been developed using classical computers.

“It won’t take quantum computers to develop PQC (so) there doesn’t seem to be a case for dumping Bitcoin,” Orme believes. “But there is a case for governments and enterprises to think seriously about shifting out of current RSA-encrypted systems.”

Quantum computing’s capacity for number crunching may make it a lucrative option when it comes to cryptocurrency mining – but it’s not yet at a suitable stage.

Today’s most advanced mining technology is extremely fast compared to the current clock speed of what quantum computers can offer now or in the short term, and it’s likely to stay that way for the next decade at least.

For a quantum computer to work in many of the applications which have already been worked out for it, it would need hundreds of thousands, even millions of qubits. The highest we can manage today is around a hundred. The process of a qubit calculation is so sensitive, that the apparatus around it has to block out various forms of interference, especially that of heat.

The supply chain for this kind of tech can’t yet be called a chain, and expertise is scarce.

But there is, nonetheless, already a QC market. GlobalData’s recent thematic report on quantum computing notes the QC market size in 2020 to have been somewhere in the range of $80m-$500m (the exact figure is hard to pin down).

Where is this money coming from? One source is Canadian QC company D-Wave, which has been selling quasi-quantum computers since 2011 for $20m each, notably to US national labs. These computers are based on the quantum annealing method, meaning they are suited to solving optimisation problems, but incapable of handling more advanced algorithms and problems.

Most revenue in quantum computing lies in cloud-based quantum service businesses from IBM, Google, Microsoft, Alibaba, Amazon and others. These quantum-as-a-service providers rent time on prototype quantum processors and simulators, often built using conventional compute power, to the rapidly swelling band of researchers and developers from government, major corporates and start-ups navigating through the quantum world.

These developers know there is money to be made on the software and application side, especially when it comes to algorithms.

While it will be years until fully-fledged versions of quantum algorithms can be run on full-size quantum computers, there is scope to develop algorithms for intermediate-scale quantum devices in areas such as logistics optimisation. Such algorithms are likely to work in hybrid systems where some qubits are combined with classical computers in the next five years.

Quantum simulators, meanwhile, which essentially mimic quantum computers but run on classical computers, are becoming increasingly popular as a way of testing quantum computation without the need for an actual quantum computer.

The last few years have seen some road tests of quantum power, literally: a reduction of car waiting times by 20% in a large-scale traffic simulation, for example. This was achieved by Microsoft in partnership with Toyota Tshuso and Jij, a Japanese quantum algorithm start-up. Algorithms based on a realistic QC model were run on classical computers to reduce the waiting time for drivers at red lights, saving about five seconds on average for each car.

In 2019, Volkswagen and D-Wave optimised routes in real-time for a fleet of municipal buses running between stops in Lisbon, considering potential traffic jams and passenger numbers. While hardware development in QC may be stuck in a metaphorical traffic jam, it’s a different story for QC software.