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by Jennifer Gibbs

Artificial intelligence gets most of the headlines, but a second technology is moving quickly in the background. Quantum computing and AI are often lumped together as "futuristic tech," but they are very different things. AI is software that learns from data to automate or support decisions. Quantum computing is hardware built on the principles of quantum physics and designed for calculations that would take traditional computers millennia. The more interesting question is what happens when the two start working together.

Quantum Computing 101

Classical computers process information as bits, and each bit is either a 0 or a 1. Quantum computers use "qubits," which can represent many possible states at once. In theory, that lets a quantum machine work through huge numbers of potential solutions in parallel. A related property called entanglement links qubits together so they can carry out computations jointly.

The technology is real, but it is still early. Today's quantum devices are limited and prone to errors. They have outperformed classical systems only on narrow benchmark tasks. The machines are also delicate. They typically run inside refrigerators kept just above absolute zero, because heat causes errors in the qubits. For that reason, most users are expected to reach quantum computers remotely through the cloud rather than own one.

A Two-Way Relationship

The link between quantum computing and AI runs in both directions.

Quantum could supercharge AI. Training AI models takes enormous computing power, and even the most advanced chips can struggle to keep up. The research suggests that the parallel processing power of quantum computers may eventually meet that demand. Quantum hardware could let AI models consider far more options at once, search possible solutions faster, and tune themselves more quickly. Researchers are also looking at quantum computing as a way to clean the data used to train AI more effectively, which would lead to more accurate models. Quantum methods may also help with AI's energy problem. One Cornell proposal suggests a quantum-based optimization framework could cut the data center burden of AI workloads by up to 12.5%.

AI is already helping quantum. AI is being used today to stabilize and control quantum systems. It adjusts settings automatically, spots glitches early, and helps the hardware run for longer stretches. AI is also being applied to the field's biggest obstacles, including the design of quantum algorithms and error correction. The results can be dramatic. TIME reported that AI was "instrumental" in new research from Google and the startup Oratomic. Experts told TIME that research could "significantly" shorten the time needed to build a quantum computer capable of threatening encryption.

Early Results Worth Watching

Some practical gains are already showing up. Researchers at the University of Toronto and Insilico Medicine combined quantum computing, generative AI, and classical methods to design molecules that target KRAS, a cancer-driving protein long considered "undruggable." One co-investigator said approaches like this could shorten the preclinical phase of drug discovery "by years." Beyond medicine, possible applications include materials science, weather modeling, finance, and logistics. Many of these remain speculative, but early proofs of concept have been promising.

The Security Question

For financial institutions and their advisers, the most immediate issue may be cybersecurity. Shor's algorithm, developed in the 1990s, could eventually let a powerful quantum computer break asymmetric (public key) encryption. The CSA notes that this type of encryption protects about 90% of our data. Security experts are especially concerned about "harvest now, decrypt later" attacks. In these attacks, bad actors collect encrypted data today so they can decrypt it once quantum tools are ready. The good news is that "quantum hard" encryption codes designed to resist quantum attacks are already available. Computer scientists are also building post-quantum cryptography to protect data over the long term.

Keeping Perspective

Timelines are still uncertain, although methods on a problem. In IBM has stated that 2026 will be the first year a quantum computer outperforms all classical-only August 2026, IBM and its collaborators announced three demonstrations that they say show this "quantum advantage," with IBM's quantum processors outperforming all known classical algorithms on specific tasks. In one experiment, the team says it mathematically proved that no classical computer can efficiently reproduce the patterns its quantum computer generated. Outside experts called the work meaningful progress, but cautioned that the tasks were niche problems tailored to IBM's hardware, that the results are hard to verify independently, and that commercial usefulness is still years away.

Either way, the two technologies are best seen as partners, not rivals. Most predict that classical computing is likely to stay dominant, with quantum hardware used selectively to speed up specific tasks.

The practical lesson is to stay prepared without falling for the hype. Most enterprises will not be running quantum-AI systems tomorrow. But the choices organizations make now about AI governance, cryptographic agility, and data controls will shape how ready they are when the technology matures.

The opinions expressed are those of the authors and do not necessarily reflect the views of the firm or its clients. This article is for general information purposes and is not intended to be and should not be taken as legal advice.