Convergence of quantum and AI could accelerate business innovation

Quantum computing is no longer relegated to the realm of science fiction—it’s a technological reality and a critical investment opportunity for businesses.

Artificial intelligence, machine learning and large language models are increasingly common topics of discussion in both professional and personal circles. Tools like ChatGPT boast nearly 900 million weekly active users, while programs like Microsoft Copilot are embedded in numerous apps that are part of people’s everyday lives.

So where does quantum fit in? Although the government of Canada has invested over $1 billion in quantum computing, discourse around it seems as silent as the second “t” in Toronto.

While the technology may not be widely available currently for commercial use, businesses should evaluate quantum’s potential utility so they avoid playing catch-up like many organizations have had to do as AI becomes ubiquitous.

What is quantum?

Quantum is a technology that harnesses the laws of quantum mechanics to solve problems too complex for traditional computers.

Traditional computers use bits as the fundamental unit of information; bits have a binary value of either 0 or 1. Conversely, quantum computers use qubits, which can be a 0, 1 or a superposition of both.

The advantage of superposition is that it allows quantum computers to process and calculate information simultaneously, performing much faster than traditional computers.

Why does it matter?

Quantum computers’ problem-solving speed could enable monumental breakthroughs in a variety of areas, including drug development and financial modelling.

Without quantum, this data would effectively be worthless due to the extended time needed to analyze it with traditional computing.

How does it fit with AI?

Quantum and AI need each other.

AI systems are designed to process large datasets, make predictions and solve problems, but building models and powering them come with steep costs.

These include data centres, graphics processing units (GPUs), research and development, and other expenses—most of which involve traditional computing.

Most AI systems run on GPUs that use bits, but unlike traditional computers, they can still process relatively complex datasets.

Quantum computers, meanwhile, run on quantum processing units that use qubits—allowing for superposition.

Despite their different hardware and data units, AI and quantum computing are converging because quantum can accelerate key AI bottleneck tasks such as optimization and sampling.

AI will support quantum computing, once it becomes commercially viable, because its methods improve quantum control and error mitigation. This will enable workflows that solve problems neither technology can efficiently handle alone.