How quantum computing and machine learning boost each other

Preparing for a future driven While some took issue with how Google structured its processing test and questioned the legitimacy of its claim, the announcement is at least a sign of progress in quantum and an indicator of what might be coming. The exponential increase in processing power that is theoretically possible with quantum computing has implications for drug discovery, cybersecurity and general AI to name a few areas.

Quantum computing and machine learning will enable models that reflect complex conditions far better than today’s models are capable of doing, Langione said. This will be a particular boon in use cases like financial portfolio optimization, fluid dynamics simulations and material design.

That type of advanced RD will be the biggest beneficiary of quantum-driven machine learning, said Ahmed El Adl, AI consulting and intelligent solutions leader at Accenture. He said traditional computing does a fine job of powering most of today’s common machine learning and AI applications. In those use cases, quantum computing won’t add much value.

“We don’t need quantum computing for chatbots or natural language processing,” he said. “But, when we talk with the RD organizations about the invention of new products, they tell us we’ve reached the limits of computational capabilities today. For life-changing applications, that’s where we need quantum computing.”

Advanced quantum computing could even open the door to general AI, El Adl said. In his view, true intelligence is made up of three areas: learning, knowledge representation and reasoning. Today’s AI is essentially synonymous with machine learning, so the first part is covered. But the abilities to generalize knowledge to new situations and understand the contextual meaning of things — El Adl’s last two criteria — are problems far too complex for today’s computers. If quantum computers are able to process this kind of complexity, the implications could be substantial across nearly all industries.

“If we want to make real breakthroughs in AI, we need more computing power,” El Adl said. “Once we make this breakthrough, I do not see any industry that will not be positively affected.”