Generative AI (GenAI) and quantum computing have each experienced their fair share of publicity lately. Both technologies could usher in a new era of computing and unlock unprecedented possibilities. As such, it should come as no surprise they are strongest together.

Classical computing can already accomplish impressive feats with GenAI, so quantum computing could take it to new heights. However, this development does not only go one way. You can also use generative models to improve efficiency in quantum computing.

Higher Performance on Fewer Qubits

One of the biggest current barriers to quantum computing is its complexity and cost. The more qubits a quantum semiconductor has, the faster and more accurately it can deliver process data. However, a single qubit can cost between $1,000 to $2,000making quantum computing expensive and inaccessible.

GenAI can help by enabling quantum computers to accomplish more with less hardware. This improvement is already evident in GenAI’s impact on classical machines. Some models have trained other AI algorithms three times faster than conventional methodsshrunk processing requirements, and provided more resource-efficient coding languages.

Applying this same efficiency-minded programming to quantum computing could reduce the number of qubits you need to perform high-intensity tasks. It would also streamline development timelines, making quantum computing more time-efficient and cost-effective.

Bridging the Classical-to-Quantum Programming Gap

Another common challenge with quantum computing is it requires unique software. Programming quantum applications is an entirely different process than classical coding. These barriers could mean slow quantum development times and talent gaps. However, GenAI can bridge the divide.

Many generative models translate plain text into code. The same underlying technology converts conventional code into quantum-ready programming. Large language models teach themselves quantum coding faster than humans can learn it. They then act as an intermediary, allowing you to build quantum applications without familiarity with the unique constraints and considerations.

IBM has already experimented with these kinds of AI interfaces. Their solution can generate quantum circuits from simple languagedramatically lowering the barrier to entry for quantum computing.

Making Quantum Computing Scalable

GenAI could also help you overcome quantum computing’s scalability barriers. While quantum computers’ performance rises with more qubits, so do errors. The magnitude of computing mistakes rises as the overall computing power does, but GenAI can help in a few areas.

First, AI models dedicate resources to error correction. Just as GenAI can find and fix flaws in AI training datasets, GenAI determines how to run quantum computers more efficiently and resolve errors for more reliable results.

Generative design tools also deliver new ways to build more resource-efficient quantum semiconductors. Similar benefits apply to computers’ cooling mechanisms, power consumption, and resource allocation. One study highlighted how AI successfully minimizes resource costs and improves scalability through improvements like this.

Remaining Concerns With Quantum Computing and AI

As with any innovation, combining GenAI and quantum computing comes with some downsides, such as security concerns. Cyberattacks are reaching record highs — 1,862 data breaches in 2021a 68% increase from 2020. Making quantum computing more accessible through AI could increase the number and intensity of these incidents.

Most troublingly, quantum computers could break through current encryption standards. While that’s not a pressing concern in today’s environment where quantum resources are hard to come by, AI could change that. As GenAI makes it easier to access and use quantum computers, more attackers could use them for nefarious purposes.

Getting ahead of the risk is key to preventing substantial damage. The National Institute of Standards and Technology has already recognized four quantum-resistant cryptography standards you can use to keep data safe from quantum-driven attacks. AI can help in this area, too. Using AI to monitor networks, scan for vulnerabilities, and suggest improvements produces the necessary protections before attackers take advantage of them.

Another issue is the cost and reliability of GenAI itself. While these models can enhance quantum computing’s accuracy and efficiency, they face their own barriers in these areas.

As GenAI scales, it will become more accessible and affordable, just like quantum computing. Minimizing expenses before the shift is a matter of applying it in small, targeted doses instead of changing too much at once. The same approach will improve its reliability, as you learn how to utilize these tools effectively.

GenAI and Quantum Computing Push Each Other Further

Quantum computing and GenAI are as complementary as technologies get. Using them together will lead to higher scalability, efficiency, and reliability in next-generation computing tasks. While challenges remain, experimenting with these joint use cases today can prepare you for the AI-and-quantum-driven future.