The quantum hype cycle
Physicists love a good graph, and in recent weeks I’ve been thinking a lot about the one shown at left. According to its inventors, the Gartner hype cycle is a “structured, qualitative analytical tool” to help investors determine how long it will take before a new technology enters widespread use. The cycle’s much-imitated curve also captures the idea that R&D is neither a smooth nor a linear process. Before an emerging technology can reach the catchily-named “plateau of productivity”, the hype-cycle framework suggests it must first slog through the “trough of disillusionment”.
Quantum computing made its first appearance on the hype cycle almost two decades ago. Back in the year 2000 analysts at Gartner placed computers that use bits in quantum superpositions, rather than classical 0s and 1s, at the left-most edge of their hype curve. The analysts also reckoned that quantum computers would not find mainstream applications for at least another decade.
For the next few years, quantum computing dropped off the hype-cycle graph entirely, superseded by such marvels as personal fuel cells (five to 10 years away in 2001, apparently) and something called “portal ubiquity” that should have arrived by 2013. After brief appearances in 2005 and 2006, quantum computing disappeared again, re-emerging in 2009 in the same position as it started.
In the 2010s, however, something started to shift. Although quantum computing’s predicted time to productivity remained stuck at “more than 10 years”, expectations began to rise. By 2014, the technology had climbed up the hype curve, and was now rubbing shoulders with similarly exciting novelties such as brain-computer interfaces. A further milestone came in 2018 when quantum computing shed the “more than 10 years away” label. According to Gartner’s analysts, widespread applications are now scheduled to arrive sometime between 2023 and 2028 – presumably on the back of a personal-fuel-cell-powered lorry.
Predictions are difficult, especially about the future, and it is certainly possible to be too cynical about technological progress. As this Physics World Focus on Computing shows, there are some exciting developments taking place in computing on both sides of the classical/quantum divide. New machine-learning strategies are creating fresh possibilities in fields from materials science to particle physics. A rise in the number of Internet-connected machines promises to make manufacturing more efficient. Tools for modelling and simulating scientific phenomena have expanded far beyond their original capabilities. And as for quantum computing, advances in hardware and software may yet prove the optimists right. I’m keeping my fingers crossed.