Artificial Intelligence (AI) has revolutionized industries by enabling complex data analysis, natural language processing (NLP), and automation. The 'ChatGPT moment'—when large language models (LLMs) demonstrated unprecedented capabilities—marked a turning point in AI’s evolution. However, as AI models become increasingly sophisticated, they demand greater computational power, pushing the limits of classical hardware. As the models grow and scale, they require massive datasets, huge compute, and even then, sometimes struggle with nuance, context, or “nonlocal” correlations in data. This is where quantum computing can become a game-changer, potentially driving the next paradigm shift.
Our latest manuscript, Quantum Large Language Model Fine-Tuning, introduces a hybrid quantum-classical deep learning architecture designed to enhance LLM fine-tuning, or the process of taking a pre-trained LLM and training it further on a new specific dataset for a new task to specialize its performance. By leveraging quantum computing’s unique capabilities, our hybrid approach can improve classification accuracy in tasks involving complex data correlations. Just as AI experienced its transformative moment, quantum computing could soon redefine what’s possible with AI in a way most people had never even considered.
Quantum-Enhanced Artificial Intelligence
Large Language Models (LLMs) like GPT or DeepSeek achieve impressive results via massive training on publicly available datasets—but their true potential often lies in fine-tuning. Fine-tuning adapts a general-purpose model to perform specialized tasks, such as domain-specific legal reasoning, code generation, or even understanding nuanced sentiment analysis using sparse datasets.
This is where quantum computing can play a transformative role–not by replacing classical AI, but by enhancing the fine-tuning process in ways that classical systems struggle to match. Just as DeepSeek demonstrated how targeted fine-tuning could dramatically elevate LLM performance, our quantum hybrid AI model aims to significantly enrich classical AI models’ representational power, especially when data is limited or subtle relationships in the data are hard to extract.
For example, let’s take sentiment analysis and prediction, particularly when there is sparse data (such as those found in niche movie reviews). In these cases, classical models can often struggle to generalize because of the lack of structure and data. Our quantum-enhanced model could uncover subtle correlations better than some classical methods, potentially leading to more accurate and robust predictions.
Our approach reflects our conviction that the most impactful near-term quantum applications will be hybrid, integrating quantum capabilities precisely where they deliver the most value. Rather than replacing classical systems, quantum resources can be strategically deployed to augment and enrich key parts of existing workflows. In this demonstration, our hybrid method shows promise for enabling more efficient and expressive fine-tuning, particularly in domains where data is limited, complexity is high, and accuracy is critical—unlocking new possibilities for AI across a broader range of applications.
A Novel, Quantum Hybrid Approach for Fine-Tuning Large Language Models
Our new paper presents a novel hybrid quantum-classical architecture for fine-tuning language models. A classical sentence transformer (a type of AI that understands sentence meanings, widely used in chatbots, search engines, and recommendation systems) is enhanced with a parameterized quantum circuit to improve classification accuracy. A parameterized quantum circuit is a tunable circuit with adjustable parameters that can act like weights in a neural network. The demonstration involved integrating a quantum layer as a classification head into pre-trained language models and evaluating its performance on sentiment analysis tasks like SST-2 (Stanford Sentiment Treebank 2, a popular benchmark used in sentiment analysis). In this example, the AI performance was compared against classical methods, analyzing the impact of qubit count and circuit depth through noisy simulations. The initial results show that certain quantum configurations can outperform classical approaches in accuracy.
A classification head is part of a machine learning model that’s responsible for making the final prediction based on all the learned features, common in image classification, text classification, and transformer models. The core of our novel approach lies in integrating a classical Sentence Transformer—a model optimized for natural language processing (NLP)—with a quantum layer functioning as a classification head. A transformer model is a type of machine learning model that understands relationships between parts of data—like words in a sentence—by paying attention to everything at once and then making a classification or prediction based on it. In this case, our hybrid model leverages both classical and quantum computing strengths:
Classical computing handles the initial data processing and feature extraction.
Quantum computing enhances expressivity in classification by exploiting resources such as superposition and entanglement, allowing for more complex data representations. “Expressivity” here is the capacity of a quantum model, particularly its parameterized quantum circuits, to represent complex data patterns and relationships.
The classical-quantum combination unlocks a new paradigm, enabling AI models to process information in fundamentally new ways. It can be particularly powerful in scenarios where classical models struggle with nonlocal correlations in data, or “hidden” connections between distant parts of data. The hypothesis here is that by adding a quantum machine learning (QML) layer, the model can achieve improved accuracy and efficiency in detecting these hidden relationships, especially in smaller datasets.
As our first-of-a-kind research continues to pave the way for commercial use cases across the AI landscape, this work points to how models augmented with a quantum layer can outperform traditional ones in tasks like sentiment analysis. It suggests that in the near future quantum computing can change the economics of AI, unlocking new levels of efficiency, accuracy, energy consumption, and scalability.
Our Novel Quantum Classical AI Model
Here are the specific components of our novel hybrid quantum-classical model, including the key quantum-enhancement steps, and how it all works together:
Classical Backbone: It starts by turning sentences into numerical vectors that capture the sentence meaning—like creating a "semantic fingerprint" for each sentence.
Quantum-Enhanced Encoder: These vectors are too large to feed into a quantum circuit, so we then compressed them using quantum-enhanced techniques that simulate how a quantum system might process them. This is similar to a classical autoencoder, but using quantum mechanics for more efficient feature retention and less computational complexity. Now, we have a lower-dimensional vector representing the sentence.
Quantum Head: The compressed vectors are then loaded into a quantum circuit to analyze the data and make decisions. We used parameterized quantum circuits, or quantum circuits designed for flexible qubit connections, enabling powerful transformations of input data so that it can process complex patterns and learn from them. During this step, the data is encoded and converted into quantum states using angle encoding, where values control qubit rotations. Re-uploading the data multiple times enhances expressivity, allowing the models to handle more complex data representations and improving their ability to recognize patterns and make accurate predictions. This process boosts the quantum circuit’s ability to learn complex patterns from the data, acting kind of like deeper layers in a neural network.
Experimental Demonstration: Evaluating the Hybrid Model in Action
The model was evaluated on the Stanford Sentiment Treebank (SST-2), a widely used benchmark for sentiment analysis. It tests how accurately models can classify text as expressing either positive or negative sentiment. In this case, the model was tested in a low-data regime, meaning it had access to only a small amount of labeled training data—making the task more challenging and a better test of the model’s efficiency and generalization.
The hybrid model was evaluated against classical baselines, including support vector classifiers (SVC), logistic regression, and perceptrons, to assess its performance. These traditional machine-learning models were used as benchmarks to determine whether our quantum-enhanced approach offered any advantage in classification accuracy.
Key Findings from Our Research
Quantum can improve classification accuracy: The hybrid quantum-classical model consistently outperformed classical baselines like logistic regression, support vector classifiers (SVC), and basic neural networks in the SST-2 sentiment classification task. Notably, the multi-encoder quantum setup (multiple, smaller encoders) achieved the highest test accuracy of 92.7%, surpassing the best classical MLP configurations. See Fig 2 and Fig 3.
Scaling shows a “sweet spot”: Accuracy increased with the number of qubits, but circuit depth needed to be optimized as there was a “sweet spot” —too shallow circuits lacked expressivity, while overly deep circuits became difficult to train.
Quantum-enhanced encoders are effective even when simulated: Simulated parameterized quantum circuits performed competitively or better (2 encoders) than classical multi-layer perceptrons (MLPs), one of the most basic types of neural networks that can serve as a baseline for measuring quantum value here. See Fig 4.
Quantum noise does not eliminate gains: We conducted noisy simulations (modeled after our physical quantum systems) to assess the model’s feasibility for near-term quantum hardware. These simulations incorporated realistic quantum gate error rates, evaluating their effects on accuracy and the desired outcomes, and performance still trended upward.
Key Benefits of IonQ Trapped Ion Technology for Quantum AI
Quantum-ready circuits: The parameterized quantum circuit design is not only built for gate-based quantum computers, but more specifically to support arbitrary qubit connectivity, which our systems excel at with all-to-all and any-to-any connectivity (unlike superconducting systems, for example, which have limited connectivity).
Optimized for today’s NISQ era: Our novel architecture is built for noisy intermediate-scale quantum (NISQ) hardware, and performs well under realistic error rates.
Strategic alignment with cloud partners and supercomputing: Our quantum technology is already available via all major public clouds and is being tested for large-scale studies on the world’s fastest Frontier supercomputer with our partner Oak Ridge National Lab (ORNL).
Real-World Applications of Our Quantum AI Approach
Our hybrid quantum-classical approach paves the way for more than just research and exploration; it is built to solve real-world problems where classical AI hits limits. Here are just a few areas where it could potentially create powerful capabilities across key industries.
Natural Language Processing (NLP): Our architecture enhances transformer-based language models and excels at capturing nonlocal correlations in data — patterns that are hard for classical models to learn. This could result in more efficient and expressive models for chatbots, search engines, and automated translation.
Financial Modeling: Our model can analyze high-dimensional datasets more efficiently using fewer parameters, potentially making it ideal for tasks like fraud detection, portfolio optimization, and risk assessment.
Healthcare and Drug Discovery: The model could potentially simulate molecular and biological systems more accurately than classical approximations, as it uses quantum layers to reduce data dimensionality and capture complex relationships in bio-data. This could result in more precise diagnostic tools, faster and more targeted drug discovery, or earlier identification of diseases.
We first conducted this research in a simulated environment, which is often the first step for us to prove out the model at a small scale and get directional signals into the value and upside. Based on these results, it is clear that there may be real value here, and as such, the next step is to take these models out of simulation and into full deployment on our quantum hardware like IonQ Forte and Forte Enterprise. We are also preparing for large-scale tests on the ORNL Frontier supercomputer, all of which will serve to dramatically scale up the qubit count and circuit complexity, enabling deeper insights.
Conclusion
Quantum computing has evolved from theory to practice and is now emerging as a potentially vital tool for advancing AI, even with today’s evolving computers. Our novel hybrid model marks a significant step toward harnessing quantum computing to refine deep learning for LLMs across the AI landscape and providing rapidly increasing quantum computational power to support the endless hunger for AI growth.
As we navigate the NISQ era, tackling noise and scalability challenges, rapid progress at IonQ suggests that quantum-classical AI synergy, even with some noise, could soon drive breakthroughs in performance and efficiency on IonQ’s hardware.
Just as the ChatGPT moment took the world by surprise, we are approaching a quantum inflection point in AI. Early signs are promising, and as research advances, we could soon witness transformative leaps in how machines learn, analyze, and make decisions using quantum computing resources.
The paper was authored by IonQ scientists Sang Hyub Kim, Jonathan Mei, Claudio Girotto, Masako Yamada, Martin Roetteler. For more details, read the full technical paper here: https://arxiv.org/abs/2504.08732