The promise of quantum-powered AI

The application of quantum computing to AI has the potential to disrupt a diverse array of industries. Learn why that potential is so promising and how it could affect companies.

Technology history is filled with disruptive pairings — cellphones plus wireless internet, for example — but the combination of quantum computing and AI might become the most disruptive duo of all.

The major potential of combining quantum computing with AI rests on the former’s potential to accelerate the latter’s capabilities.

Quantum-powered AI could translate into breakthrough solutions for complex problems across a wide range of industries and scientific fields that are beyond the current capabilities of classical computers and traditional AI techniques, said Scott Likens, global AI and innovation technology leader at PwC.

According to Likens, business and IT leaders should have quantum computing and AI developments on their radar today since the pairing offers a number of benefits, including the following:

• Speed: AI on classical computing is limited by hardware. Training recent large language models — which depend on massive data sets — can take days or weeks, or even longer. AI supported by quantum hardware could shorten the time required to train these models.
• New mechanics: Machine learning (ML) powers most AI systems. The current ML algorithms work well at predicting — as seen with generative AI — but need help scaling with other types of computational problems. Quantum systems might unlock new patterns of problem-solving since they are based on the principles of quantum mechanics, such as superposition, entanglements and interference, which depend on vastly different processes than current digital computers.
• Quantum machine learning algorithms: There is also significant progress on new quantum-inspired ML algorithms that are exponentially faster than classical computers. These ML algorithms rely on new techniques rooted in quantum mechanics to further enhance the processing ability for machine learning.
• Energy efficiency: Quantum machine learning algorithms that can be trained faster will likely consume less energy than classical computers, as AI and ML training and storing data presently use a massive amount of energy. The process of querying these algorithms in production, called inference, might also require less energy either on classical or quantum computers. Inference occurs when an ML algorithm processes live data points to calculate an output, such as a single numerical score. For organizations striving to achieve environmental, social and governance goals, the combined technologies could potentially help meet net-zero emissions commitments.
• Operational improvements: Quantum computers process information in parallel on the same set of qubits, while existing classical computers need to spread these parallel computations across separate chips. The innate parallel processing of quantum computers promises exponential increases in optimization tasks that could enhance resource allocation, supply chain management and financial modeling.

Organizations hesitant to invest in a single quantum computer should know that there are ways for quantum and classical computing to interact, although combining the processes is still in its early stages.

Businesses will eventually be able to combine quantum and classical approaches for faster computation and analysis for better-optimized solutions, Likens said.

The dangers of quantum-powered AI

For all the excitement around the AI-quantum computing convergence, the potential pairing holds dangers as well. The integration could lead to many new problems and challenges for business and society.

As AI systems become more capable, their complexity might reach a point where people can no longer understand — or control — them, which in turn would lead to ethical and safety issues, Likens said. The complexity of quantum systems might also exacerbate the lack of transparency and interpretability in AI algorithms. These issues also contribute to concerns about bias.

“Balancing innovation with ethical and security considerations will be crucial as these technologies evolve,” he said.

With the rise of businesses seeking value creation in AI, there’s a growing trend that workers fear becoming obsolete. AI powered by quantum computing could be another aspect for employees to worry about soon.

Quantum computing and AI could lead to mass unemployment as the systems become more capable than humans across various domains, said Chirag Dekate, vice president analyst at Gartner.

The quantum-powered AI organization

A widespread convergence of quantum computing and AI might be near term as both areas undergo rapid transformation.

Significant recent developments have occurred in underlying quantum computing hardware, algorithms, software, and the infrastructure for interconnecting quantum and classical computers, Likens said.

Newer quantum-inspired algorithms show promise for enhancing predictions, generating new content and better decision-making, and this could translate into advances in important areas, he said. Quantum simulations in healthcare have the potential to speed up drug discovery and analysis using quantum optimization techniques. In addition, quantum AI can benefit areas such as cybersecurity and finance by handling high-dimensional data.

Furthermore, developers are refining the infrastructure that allows both classical and quantum computers to smoothly interact, Likens said.

Quantum AI excitement

In the future, quantum computing could potentially be as commonplace as classical computing.

We can look forward to an era where quantum computing is as accessible as our smartphones today to allow us all to make decisions, solve problems and build things in a quantum-native way, Dekate said.

IT leaders should consider the overall value of implementing quantum-powered AI to achieve organizational goals.

“Imagine the quantum economy that arises from all of this,” Dekate said.