At a glance
• Quantum advantage experiments cover specific sampling tasks: the Google and USTC experiments ran random sampling benchmarks unrelated to AI training, and classical algorithms have kept narrowing the gap.
• Quantum machine learning's advantage is still a possibility: methods that use quantum states as a feature space have been proposed and demonstrated, but the paper itself calls this a "possible path" to quantum advantage.
• Today's leading strategy is a hybrid loop: in variational quantum algorithms (VQAs) the quantum device estimates a cost function and a classical optimizer updates the parameters, yet trainability, accuracy and efficiency remain open challenges.
• More qubits can make training harder: for randomly initialized circuits, "barren plateaus" where gradients are effectively zero grow exponentially worse with the number of qubits.
• Large-scale computation depends on error correction: logical-qubit experiments are under way, while governments fund quantum research and standardize quantum-resistant cryptography.

1. The starting point: AI's electricity demand and the hopes placed on quantum computers

As AI grows, so does the electricity needed to run it. According to the International Energy Agency (IEA), data centres used around 415 terawatt-hours (TWh) in 2024, about 1.5% of the world's electricity consumption, and that figure is set to more than double to around 945 TWh by 2030. The IEA names AI as the most important driver of that growth.

Against this backdrop, the claim that "quantum computers will break AI's limits" comes up often. Yet in his 2018 paper on Noisy Intermediate-Scale Quantum (NISQ) technology, physicist John Preskill wrote that noise in quantum gates will limit the size of circuits that can be executed reliably, and that a 100-qubit quantum computer "will not change the world right away." This article follows what the papers and official announcements actually show, rather than the expectations.

AI-reconstructed illustration explaining the topic of combining quantum computing and artificial intelligence
Recreated illustration · Not an actual photograph — * AI-reconstructed illustration · Not an actual on-site photograph — a conceptual illustration of combining quantum computing and artificial intelligence

2. What the quantum advantage experiments actually showed

In 2019 Google researchers reported in Nature (Arute et al.) that their 53-qubit superconducting Sycamore processor took about 200 seconds to sample one instance of a random quantum circuit a million times. Their benchmarks at the time indicated that the equivalent task would take a state-of-the-art classical supercomputer approximately 10,000 years, and they described the result as an experimental realization of quantum supremacy "for this specific computational task." The same paper noted that simulation methods had improved steadily and said the authors expected lower simulation costs than reported to eventually be achieved, while also expecting hardware improvements on larger processors to keep outpacing them.

The challenge came quickly. IBM researchers argued in arXiv:1910.09534 that, by using secondary storage, such circuits could be simulated with high fidelity on Oak Ridge National Laboratory's Summit supercomputer in a matter of days. Later, Pan, Chen and Zhang reported in arXiv:2111.03011 that they generated one million uncorrelated samples for the 53-qubit, 20-cycle Sycamore circuit in about 15 hours on a cluster of 512 GPUs, at a target fidelity of about 0.0037.

Researchers at the University of Science and Technology of China (USTC) pursued similar experiments. Their superconducting processor Zuchongzhi 2.1 has 66 qubits and performed random circuit sampling at a scale of up to 60 qubits and 24 cycles in about 4.2 hours; the team estimated that simulating the same task classically would take about 4.8×10⁴ years (Zhu et al., arXiv:2109.03494). That paper's abstract opens by saying quantum hardware must be upgraded "to withstand the competition of continuously improved classical algorithms and hardwares." The group's photonic quantum computer Jiuzhang observed up to 76 output photon clicks in a Gaussian boson sampling experiment (Zhong et al., arXiv:2012.01625).

In short, these experiments show a contest between quantum devices and classical computation on specific sampling tasks. They are not results in which an AI model was trained faster.

3. What quantum machine learning is trying to do

A widely cited starting point for quantum machine learning is the 2019 Nature paper by Havlíček and colleagues (arXiv:1804.11326). The authors note that kernel methods such as support vector machines (SVMs) are ubiquitous, but run into limits when the feature space becomes large and kernel functions become expensive to estimate. They explain that a core element of quantum speed-ups is exploiting an exponentially large quantum state space through controllable entanglement and interference.

The paper proposed and experimentally implemented two methods on a superconducting processor. The quantum variational classifier uses a variational quantum circuit to classify a training set, in analogy to conventional SVMs. The quantum kernel estimator uses the quantum computer to estimate the kernel function and then optimizes the classifier. Both methods use the quantum state space as the feature space of the classification problem.

The wording matters. The paper says that a quantum-enhanced feature space that is only efficiently accessible on a quantum computer provides "a possible path" to quantum advantage. What it offers is a direction still to be verified, not an established advantage.

4. Today's leading strategy: variational quantum algorithms (VQAs)

A review by Cerezo and colleagues in Nature Reviews Physics (2021, arXiv:2012.09265) sums up the situation. Simulating complicated quantum systems or solving large-scale linear algebra problems is very hard for classical computers, and quantum computers promise a solution, but fault-tolerant quantum computers will likely not be available in the near future. Current devices have limited numbers of qubits, and noise limits circuit depth.

The leading response to these constraints is the variational quantum algorithm. A parameterized quantum circuit is trained with a classical optimizer: the quantum device estimates a cost function, and a classical computer uses that value to update the circuit parameters. Gradients can also be estimated on the quantum hardware itself with the "parameter-shift rule," which runs the circuit again with shifted parameters. This is a different structure from the popular picture in which "the quantum computer calculates and a GPU runs backpropagation." The Variational Quantum Eigensolver (VQE), which targets ground-state energies of molecules, and the Quantum Approximate Optimization Algorithm (QAOA), which seeks approximate solutions to combinatorial problems, both fall within this framework.

The review says VQAs appear to be "the best hope" for obtaining quantum advantage, while stating plainly that challenges remain in their trainability, accuracy and efficiency.

5. The wall that blocks training: barren plateaus

The best-known trainability problem is the "barren plateau." A paper by McClean and colleagues in Nature Communications (2018, arXiv:1803.11173) notes that random circuits are often proposed as initial guesses for parameterized quantum circuits, and shows that the exponential dimension of Hilbert space and the complexity of estimating gradients make this choice unsuitable for hybrid algorithms run on more than a few qubits.

Specifically, for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small in the number of qubits. The authors relate this to the 2-design characteristic of random circuits and conclude that solutions must be studied. Adding qubits does not by itself improve training; without careful initialization and circuit design, training can stall altogether.

6. Hardware: three approaches and the published numbers

Superconducting qubits are used by Google's Sycamore and by IBM. IBM's hardware and roadmap page lists Heron r1 with 133 qubits, Heron r2 and r3 with 156 qubits, and Nighthawk with 120 qubits, with the Heron family using tunable couplers. IBM describes Quantum System Two as its flagship system and the cornerstone of "quantum-centric supercomputing."

Trapped-ion qubits hold ions in place with electromagnetic forces. IonQ's technology overview page explains that the ions are isolated from the environment to minimize noise and decoherence, and that because the qubits are not connected by physical wires, every qubit can interact with every other directly. Quantinuum announced on April 16, 2024 a two-qubit gate fidelity of 99.914(3)% across all qubit pairs on its commercially available H1-1 system. Both are company statements and should be read as such.

Neutral-atom qubits use arrays of uncharged atoms. A Nature paper by Bluvstein and colleagues (Harvard and others), published online in December 2023, reported a programmable processor based on logical qubits operating with up to 280 physical qubits in reconfigurable neutral-atom arrays. The team improved a two-qubit logic gate by scaling surface code distance from 3 to 7, created logical GHZ states fault-tolerantly, and realized sampling circuits with up to 48 logical qubits.

7. Error correction, and the hopes for chemistry simulation

Bluvstein and colleagues open their paper by stating that suppressing errors is the central challenge for useful quantum computing and that large-scale processing requires quantum error correction. They add that the overhead of "logical" qubits, whose information is encoded across many physical qubits for redundancy, poses significant challenges to large-scale logical quantum computing. This fits the Cerezo review's view that fault-tolerant machines are not a near-term prospect.

Molecular and materials chemistry is often named as a potential "killer application" for quantum computers. However, an analysis by Lee and colleagues (arXiv:2208.02199) gathered the evidence for the most common task in quantum chemistry, ground-state energy estimation, and concluded that evidence for an exponential quantum advantage across chemical space has yet to be found. The authors say quantum computers may still prove useful for quantum chemistry, but that it may be prudent to assume exponential speed-ups are not generically available for this problem.

8. What governments and institutions are doing

Quantum-resistant cryptography. On August 13, 2024, the U.S. National Institute of Standards and Technology (NIST) released three finalized encryption standards designed to withstand attack by a quantum computer: FIPS 203 (ML-KEM), and FIPS 204 (ML-DSA) and FIPS 205 (SLH-DSA) for digital signatures. They are the first completed standards from NIST's post-quantum cryptography standardization project.

United States. The National Quantum Initiative (NQI) was established by the National Quantum Initiative Act in 2018 and later amended by the National Defense Authorization Act for Fiscal Year 2022 and the CHIPS and Science Act of 2022. It is carried out by federal agencies and includes 14 National Quantum Information Science (QIS) Centers and a quantum industry consortium.

European Union. The Quantum Technologies Flagship was launched in 2018, following the Quantum Manifesto of 2016, with an expected EU budget of €1 billion over 10 years. It funds quantum computing, quantum simulation, quantum communication, and quantum sensing and metrology. Two prominent projects from its initial phase were OpenSuperQ, based on superconducting circuits, and AQTION, a trapped-ion system.

Japan. RIKEN, working with Fujitsu and other Japanese institutes, put a domestically built 64-qubit superconducting quantum computer online via the cloud for researchers at the end of March 2023 (RIKEN). From October 5 of that year, RIKEN and Fujitsu began offering joint-research companies and institutions a hybrid platform combining a newly developed 64-qubit quantum computer with Fujitsu's 40-qubit quantum computer simulator (RIKEN).

South Korea. On June 27, 2023, the Ministry of Science and ICT announced the government's first Quantum Science and Technology Strategy, setting goals to invest about 3 trillion won by 2035 and reach 85% of leading countries' technology level, train 2,500 core researchers, secure a 10% share of the global quantum industry and foster 1,200 quantum companies (Korea.kr, in Korean). The Korea Research Institute of Standards and Science (KRISS, in Korean) plans to offer the 20-qubit quantum computing system it developed in 2024 to industry through a cloud service, and has announced plans to develop a 50-qubit system.

9. Takeaway: a time for verification, not replacement

Checked against the sources, the claim that "quantum computers will transform AI" breaks into three parts. First, quantum advantage experiments are real but limited to specific sampling tasks, and the contest with classical computation continues. Second, quantum machine learning has proposed interesting methods, but practical advantage remains a "possible path," and training problems such as barren plateaus have been identified. Third, the error correction needed for large-scale computation is at the stage of logical-qubit experiments.

So the useful question today is not "when will quantum computers replace GPUs" but "who has verified that a quantum method beats classical methods on which problem, and under what conditions." When reading a new announcement, checking what the task was, whether the classical baseline was state of the art, and whether the figures come from a peer-reviewed paper or a company statement helps separate progress from hype.

Sources and references