Modern computers are incredibly versatile, but even the most potent ones struggle with certain types of calculations and modelling
Now, imagine an entirely different kind of computer, with head-spinning power, using mind-bending quantum mechanics to bring barely believable capabilities to life. A super super computer that can tackle calculations that the most powerful conventional machines would need decades to process – in a split second. This contraption – which resembles a baroque chandelier that could have hung at Versailles – is a quantum computer.
They probably won’t replace today’s computers – don’t expect your next laptop to be a quantum device – but they will be able to tackle certain boxed and highly complex tasks that force traditional computers to throw in the towel. If there are near-endless possible answers to a clearly defined problem, a quantum computer will find the solution much quicker than any conventional computer.
Quantum computers are powered by “qubits” (i.e., quantum bits), which, due to the strange properties of quantum mechanics, can exist in something called “superposition,” which – in simplified terms – means they exist in both 0 and 1 states simultaneously. Imagine flipping a coin. It’ll eventually land on either heads or tails. But if you spin it, you could say that – before it settles – it is both heads and tails at the same time – or, rather, there is a possibility that it can be either of the two. It is in superposition. In order to operate at scale, qubits need to be entangled – wired together in superposition. “Quantum entanglement,” explains IBM, “allows qubits, which behave randomly, to be perfectly correlated with each other.”
Alas, superposition is fickle, and when “decoherence” forces a qubit out of superposition, it no longer possesses quantum properties. The solution is called “error correction,” and quantum computing pioneers like IBM, Microsoft and Google are hard at work making it happen.
For a more comprehensive explanation of quantum computing, check out this primer. And don’t miss this irresistible video featuring IBM scientist Talia Gershon explaining quantum computers to five individuals – from an eight-year-old to a theoretical physicist from Yale.
Possibilities for quantum use cases include predictive analytics and advanced modeling, which could help streamline and optimize large-scale transit operations and fleet maintenance, energy exploration, disaster prevention and recovery, as well as climate change mitigation. Also on the radar: chemistry simulations of molecules and atoms whose complex behavior is driven by quantum mechanics and simply too hard to handle for conventional machines. Meanwhile, automakers, including Volkswagen, are investigating quantum computing in search of improved battery chemistry for electric vehicles.
In oil refining, massively big machines, called “hydrocrackers,” are used to “upgrade low-quality heavy gas oils … into high-quality, clean-burning jet fuel, diesel and gasoline.” Extremely complicated and costly to maintain, hydrocrackers may sit idle several months each year, but implementing a predictive modeling application has enabled hydrocracker operators to shave off months of downtime for these behemoths. The idea: Make all acute repairs when the machine is down and use technology to predict what might break next – and fix it preemptively. Adding quantum-driven AI as the “brain” for the hydrocracker could further minimize downtime because the quantum computer could calculate exponentially more scenarios than current technology.
In another example of the immense potential of the technology, bright minds from the University of Glasgow’s School of Physics & Astronomy recently announced that they have adapted a quantum algorithm called Grover’s algorithm to drastically cut down the time it takes to identify and analyze gravitational wave signals.
One of the most interesting use cases is artificial intelligence. Indeed, adding quantum power to AI could be what takes present-day “Narrow AI” to the next level – “General AI.” The quantum-AI hydrocracker brain described above is a possible example of General AI. Quantum computing could also propel machines toward sentience within specific fields. Imagine computers perfectly empathizing and emulating emotions, with the ability to respond to complex signals, like expressions, eye movement and body language. Perhaps one day, quantum computing could drive us all the way to that barely fathomable third level of AI – “Super AI” — where machines outperform humans in every way.
Today’s quantum machines are scientific marvels, and they are evolving rapidly. “By [2025],” IBM says, “we envision that developers across all levels of the quantum computing stack will rely upon our advanced hardware with a cloud-based API.” The hope is that “by 2030, companies and users are running billions, if not a trillion quantum circuits a day.” Big Blue, whose most powerful machine currently packs 126 qubits, expects to have an 1121-qubit version in 2023.
Quantum computing is fascinating, promising and just cool. Still, we may need to slow the hype machine down a tad as significant challenges must be overcome before the technology can be commercialized. Functional, stable, production-scale quantum machines could be up to a decade away. But once they materialize, we can start writing software for the quantum stack and begin to realize all these tantalizing quantum computing use cases.
About the Author
Wolf Ruzicka is Chairman at EastBanc Technologies. Wolf is a technology industry veteran with more than 25 years of experience leading enterprise business strategy and innovation. He joined EastBanc Technologies in 2007, originally as CEO. During his tenure, Wolf also served as President of APIphany, a division of EastBanc Technologies, through its acquisition by Microsoft. Wolf’s vision and customer-centric approach to digital transformation is credited for helping establish EastBanc Technologies as a leader delivering sophisticated solutions that enable customers to win in today’s digital economy. Follow Wolf on LinkedIn.
