June 2018, London
Quantum computing: turning perceptions upside down
Contrary to some views that quantum computers make the world unsafe and hackable, there is the promise of excellent security for sensitive information, writes theoretical physicist Joerg Esser in the last of a series
In quantum computing, numerous players have dug their teeth into different areas of development. The result of this is that initial tests on hybrid models are already up and running, using quantum machine learning to enhance classical computing.
But achieving quantum supremacy, (where Google claims to be nearing the finish line – see Part 3), still requires significant progress in a number of areas. Today, while many different players are involved, most of them are focused on specific pieces of the overall puzzle.
As far as the number of qubits goes, fully functioning quantum computers have been built with four to five qubits, while fragile test systems reach ten to 20 qubits. The prototype presented at CES by IBM this month has 50 qubits, while that of Intel has 49 (the science behind qubits is explained in Part 2).
The race for qubits is reminiscent of the race for transistors half a century ago!
These achievements have garnered attention, as around 50 qubits is the theoretical threshold for quantum computers to outperform classical computers for general purposes. However, just for the record, this is somewhat misleading, as the theoretical threshold assumes perfectly robust qubits. Taking into account error rates and the difficulty of maintaining robust quantum properties, the required number of qubits under real-life conditions could be a few hundred or even a few thousand. In many ways, you could say that the race for qubits is reminiscent of the race for transistors half a century ago!
Microsoft and Google have set up general-purpose quantum computing R&D programmes but have not yet publicly demonstrated their hardware. Google has been running its Quantum AI Lab together with NASA and the Universities Space Research Association since 2013. The search giant says it is "particularly interested in applying quantum computing to artificial intelligence and machine learning”.
Why? Because, says Google, “this is because many tasks in these areas rely on solving hard optimisation problems or performing efficient sampling”.
Microsoft has been especially active in developing a programming language for quantum computing based on C#, as well as enabling easy access through Azure. Its focus seems to be on making quantum computing accessible to developer communities.
Alongside these tech giants, Berkeley-based start-up Rigetti Computing is driving the commercialisation of QML. The company emerged from Y Combinator as ‘space shot’ and is backed by big names in the tech space, such as Andreessen Horowitz and Vy Capital. Rigetti is already running unsupervised machine learning on its quantum computer system based on clustering algorithms.
At the same time, both IBM and Rigetti have introduced capable general-purpose cloud-based quantum computers for public and limited-access use. IBM's is a 20-qubit system and Rigetti's a 19-qubit system; each comes with a full-stack software development toolkit. IBM's Q Network aims to explore potential practical quantum applications based on its current 20-qubit system. Household names like JP Morgan, Daimler, Honda, Samsung and Volkswagen are reported to be among the first clients.
The key promise for business is that quantum computing will be able to extract the maximum meaning from big data
Ironically, while the general perception is that quantum computers make the world an unsafe, hackable place, the promise of excellent safety for sensitive information is also the very thing that drives research. The aim is to enable hack-proof communications via the so-called ‘quantum Internet’. The UK and European Union have recently launched joint research projects to establish the hack-proof transfer of information between major European cities based on quantum networks.
The promise for business is that quantum computing will be able to extract maximum meaning from big data. Generally, players are keeping relatively quiet about their achievements here. Tech giants such as Alibaba and Tencent are among those generating the least noise. Start-ups such as IonQ, Quantum Circuits and RIKEN are also increasingly investing in the development of hardware. However, none of these players has shown their work publically yet.
QML and advancing AI
What is becoming clear is that quantum machine learning will advance artificial intelligence to an unimaginable extent. And the good news for businesses is that all data-related problems will be solvable very soon. This they should know!For the time being, however, it is still difficult to predict exactly when specific applications of machine quantum learning in business will become mainstream.
However, there are signs that it could be a few years rather than a decade or more. That means that disruptive progress in extracting meaning from data will happen within what many businesses consider their strategic time horizon
There are signs that it could be a few years rather than a decade or more for machine quantum learning to become mainstream
Businesses should not wait for QML to hit the mainstream. As with all exponential developments, the tipping point will come suddenly after periods of slow progress. Companies need to embrace this change, and rework their value propositions and operating models accordingly.
Early movers will vastly outperform their competitors. This goes someway to explain why companies are communicating about their progress far more cautiously than about other emerging technology.
But this could start to change in the next few months. Stay tuned!
Catch up here on Part 1, Part 2 or Part 3. Or join us in London where Joerg Esser, a former group director at Thomas Cook, and now consultant to Roland Berger will be speaking about applications for blockchain technology