AI is starving for more power. Can quantum computing help?

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Data centers are draining more electricity from global power grids than ever before because of generative artificial intelligence (genAI) and general AI processing needs. 

The compute capacity to train large language models, the platforms on which generative AI (gen) and AI run, is now roughly doubling every nine months, according to Epoch AI, an AI research institute. The International Energy Agency forecast that global data center electricity demand will more than double from 2022 to 2026, in large part because of AI and cryptocurrency.

That insatiable demand for energy has tech companies scrambling for alternative sources of energy as well as ways to reduce the energy needs of AI technologies.

One potential emerging solution to the AI-compute dilemma is quantum computing, which vastly surpasses today’s binary computing systems in processing capabilities and energy consumption. Studies have shown quantum computing can increase the performance of AI neural networks for tasks such as natural language processing and image analysis.

“Quantum computing definitely augments the power of AI. For example, AI and quantum computing used together can accelerate drug discovery and personalized pharmaceuticals by years. Quantum computing supports AI-based simulation of clinical drug trials so that the trials take one hour instead of ten years,” said Avivah Litan, a vice president analyst at Gartner.

For example, in February, Insilico Medicine and the University of Toronto announced they’d demonstrated the first instance of a generative model running on quantum hardware outperforming state-of-the-art classical models in generating viable cancer drug candidates.

What is quantum computing?

In classical computers, bits programmed as units of data have a possible value of one or zero — hence the term binary code. In quantum computers, data units are programmed with quantum bits, known as qubits, which can represent a one, a zero, or a combination of both zero and one at the same time. At a high level, that trait enables quantum computers to be faster and better at fundamental processing tasks than data processing on classical computing systems that use GPUs or CPUs.

For example, Google’s Quantum AI division built a supercomputer based on its Sycamore quantum processor. Each chip currently holds 70 qubits and can reportedly complete in seconds what would take a CPU- or GPU-based supercomputer of similar size decades to process.

From left to right, Google’s rendition of its Quantum computing platform and its Sycamore quantum processor.

Google

“Quantum artificial intelligence with better algorithms… are faster and more accurate,” CompTIA, a global, nonprofit IT association, stated in a blog.

Commercial quantum platforms, such as Microsoft Azure Quantum, AWS Braket, Google Cirq, and others, allow cloud providers to use quantum comuting as compute service offerings.

“Think of these platforms as quantum computing marketplaces whereby the cloud service providers have partnered with multiple quantum computing vendors to provide access to their hardware, software, QSDKs [Quantum software development kits], etc.,” said Heather West, a research manager with IDC.

“Most of these cloud service providers have not, and thus do not, provide access to their own quantum systems, the exception being Google. AI is not a part or related to these offerings,” she added.

As with any technology, along with the positives there are negatives associated with quantum computing. For example, quantum computing poses a serious threat to the cybersecurity systems relied on by virtually every company, according to CompTIA. The current standard for encryption algorithms, such as RSA or SSL/TLS, relies on the complexity in factoring large numbers into primes, and that’s the type of problem quantum computers are great at solving, CompTIA said.

Startups and established companies continue to accelerate their advances in the quantum computing space. Big tech companies such as Alibaba, Amazon, IBM, Google, and Microsoft have already launched commercial quantum-computing cloud services. Two years ago, Goldman Sachs said it planned to introduce quantum algorithms to price financial instruments as soon as 2026. Honeywell anticipates that quantum will form a $1 trillion industry in the decades ahead.

Quantum computing, meet genAI

Some say quantum computing is a natural partner for genAI and can reduce its energy demands.

For example, Sumitomo Mitsui Trust Bank in Japan is using quantum computing to run genAI-powered programs for financial simulation models of future market movements. The bank partnered with Zapata AI, a genAI company that was spun out of Harvard University’s quantum computing lab in 2017.

Christopher Savoie, Zapata AI’s CEO, sees linear algebra (quantum math) as the solution to perform all kinds of AI tasks, including chatbots such as ChatGPT.

“We’re throwing an obscene the amount of GPU energy at chatbots right now. Are we getting that much business value right now from it? We’re hitting a wall: when are we going to make money with that?” said Savoie, who is a molecular biophysicist.

Savoie pointed to the study done by Insilico Medicine and the University of Toronto, which Zapata participated in.

“When we used this quantum-based model… we were able to develop cancer drugs the other models didn’t,” Savoie said. “We used quantum models to determine what drugs would block this cancer protein and then non-quantum models. The quantum models found two capable drugs that we synthesized and showed they blocked the cancer protein.

“So, it’s qualitatively better,” he continued. “It’s cheaper, faster, and better — better in that we get faster answers. That’s important in drug discovery. You’re saving a lot of money for pharmaceutial companies if you get your answer the first run around. Or you have a more accurate modeling of trading behavior for a bank.”

Zapata AI’s Orquestra platform was specifically designed to run any AI or machine learning model, including more traditional neural networks as well as the company’s proprietary tensor networks.

Tensor networks can be used to model any quantum circuit and run it on today’s classical computers, giving users an on ramp to the potential benefits of future quantum computers, according to Zapata AI. Tensor networks also come with their own advantages for AI today, including more accurate, efficient, and expressive AI models.

“Every quantum circuit can be written as a tensor product, which means we can do things on GPUs that quantum computers will eventually be faster at doing. Zapata and others have shown that quantum math is better at getting better answers in the context of generative AI,” Savoie.

Specifically, Savoie said, quantum statistics can enhance genAI models’ ability to extrapolate missing information and generate new, high-quality information from big data. Generating genuinely new and high-quality data is very important for industrial use cases, he said. 

Early days yet

IDC’s West said quantum computing fits with complex problem solving, but it’s “not a big data solution.” Quantum computing will be useful for solving specific types of problems, she said.

In quantum computing, a qubit begins in a binary state of 0 or 1, but through a process known as annealing, the qubits become entangled, allowing them to represent many possible answers, always with minimum energy. The process occurs in microseconds.

“Quantum annealers are best suited for optimization problems,” West said. “The complex algebraic/factorization problems include some QML [quantum machine learning] problems, but not all AI problems will be suitable for quantum. Research is being conducted to determine how to integrate AI into [quantum computing] and [quantum] into AI to optimize the compute resources needed to solve some of these problems.”

In large part, quantum computing is in very early stages of development, West noted. That’s because the hardware still needs considerable improvements for gate-based models that allow for the​ execution of quantum‌ algorithms. By applying various gates ​sequentially, complex computations can be carried out.

“There are not any real-world applications for this type of system,” West said. “These systems are only useful for small-scale experimentation and debugging. Quantum [computing is] currently being used for solve some scientific and business optimization problems. It is still too early for the integration of AI. Right now, it is only a hypothetical and experimental.”

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