What is Quantum AI?
Quantum artificial intelligence, or quantum AI, is the use of quantum technologies for running AI systems. AI models require immense computational power and infrastructure resources to run efficiently. Quantum AI aims to replace the underlying AI infrastructure with quantum computing resources so AI models can process data faster and cost-efficiently. The field envisions further AI advancement and enhanced capabilities due to the exponential processing speed that quantum computing is capable of. While possibilities are unlimited, it is important to note that quantum AI is still in the research phase, and AI workloads still require traditional computing resources to operate.
What is the difference between classical and quantum AI?
Classical (or current) AI uses classical computing resources based on binary bits, or information units that can be mathematically represented as 0 or 1. Physically, binary bits can be thought of as electronic pulses. Every computing device, from simple calculators to advanced computers, runs on this binary logic. These systems process data in a linear, step-by-step manner. Hence, more time and computing power are required for classical AI tasks like analyzing large datasets correlating audio, video, and unstructured text data.
In contrast, quantum AI uses quantum computing resources based on quantum bits or qubits. Physically, qubits are based on atoms and their electrons, and the principles of quantum mechanics govern their operations. Unlike classical bits, qubits can exist simultaneously in a state of 0, 1, or both due to a physics concept known as superposition. They can also be entangled, meaning the state of one qubit is directly related to another, even across distance.
Qubits allow quantum computers to process millions of operations simultaneously. Theoretically, quantum AI running on quantum computing resources could solve complex problems beyond the reach of classical AI.
Why is quantum AI research important?
Quantum AI research aims to help AI models achieve their full potential. AI models are systems trained to perform tasks that typically require human intelligence, such as recognizing images, translating languages, or predicting future trends. These models learn patterns from large datasets and use that learning to make decisions or generate outputs. There are two main categories of AI models:
- Predictive models analyze existing data to forecast future outcomes. For example, predicting stock prices or customer behavior.
- Generative models create new content based on their learning, such as generating realistic images, text, or music.
Both AI models are restricted in their ability to analyze data at scale because of the limitations of classical computers that power them. Quantum AI research aims to overcome these limitations to enhance the benefits AI can bring to society.
What are the potential benefits of quantum AI?
Combining quantum computing and artificial intelligence promises to bring the following benefits.
Reduce AI training costs
Training AI models—especially generative foundation models with billions of parameters—involves feeding large amounts of data through complex neural networks. These networks perform millions of mathematical operations to adjust internal weights and optimize for accuracy. This process is not only compute-heavy but also energy-intensive. Larger models often require distributed computing setups, which increase complexity and cost.
Quantum AI models would theoretically run millions of operations in parallel on a single quantum processor. This would remove the need for distributed computing setups for current AI needs. Multi-processor setups would train AI models at scale for tasks that are unimaginable today. This could significantly lower the cost and environmental impact of training cutting-edge AI models.
Improve AI prediction accuracy
Quantum computing’s ability to perform complex probabilistic simulations and optimization tasks at scale can improve AI output quality. Classical systems may simplify assumptions or ignore certain variables due to hardware constraints. Quantum AI, in contrast, could account for those variables, offering more nuanced and potentially more accurate predictions in high-stakes environments like stock trading, credit scoring, and supply chain forecasting.
For example, quantum AI could transform risk assessment and portfolio optimization in the financial sector. Analyzing market data at a granular level and simultaneously considering countless variables could provide insights that give investors a significant edge. It might even be able to predict market trends with a degree of accuracy, making today's best algorithms look like educated guesswork.
Advance scientific research
Quantum AI could dramatically accelerate medical, climate, materials science, and other research that requires processing vast amounts of data. For example, quantum AI could simulate complex chemical reactions with unprecedented accuracy, potentially leading to drug discovery and disease treatment breakthroughs. By modeling atomic-level interactions or analyzing planetary-scale datasets, scientists may make discoveries currently beyond our computational reach.
Enable new AI algorithms
Quantum AI isn’t just about speeding up existing AI processes—it also opens the door to entirely new ways of designing intelligent systems. Current AI models are built around the constraints of classical computation. As researchers develop algorithms specifically for quantum computers, AI systems could approach learning, reasoning, or pattern recognition in fundamentally different ways.
For example, quantum neural networks and quantum-enhanced reinforcement learning are early concepts that attempt to rethink how machines can "learn" in environments with many possible outcomes. These innovations could one day result in AI systems that perform well in domains where classical AI still struggles, such as long-term planning or real-time decision-making under incomplete information.
How does quantum AI work?
Quantum AI integrates principles from quantum mechanics with existing machine learning and AI concepts to explore new approaches. Traditional AI uses linear algebra and optimization techniques running on classical bits. However, quantum AI models use quantum algorithms that operate on qubits. These algorithms leverage quantum mechanics principles like superposition and entanglement in fundamentally different ways. However, these approaches are still in the research phase and are being tested primarily on simulators or small-scale quantum processors. We give some examples of research efforts below.
Quantum optimization algorithms
These algorithms are useful for solving combinatorial optimization problems where the goal is to find the best combination out of many possible configurations. Classical algorithms struggle to solve such problems due to the exponential increase in complexity. In AI, these algorithms are being explored as a tool for solving optimization sub-tasks, such as selecting the best model parameters or minimizing cost functions in reinforcement learning environments.
Quantum classifiers
Quantum classifiers are algorithms that leverage quantum computing principles to solve classification problems. They assign labels to data based on learned patterns. For example, the Variational Quantum Classifier (VQC) is being explored as a proof-of-concept in nonlinear decision making. So far, VQC has been demonstrated on small-scale datasets and quantum hardware with limited qubit counts, mainly to benchmark its performance against classical classifiers under controlled conditions.
Quantum neural networks
Quantum neural networks (QNNs) aim to mimic the structure of classical neural networks using qubit interactions. They are being used to explore whether quantum circuits can approximate complex functions and learn to recognize patterns in data. Some experimental designs attempt to use quantum circuits as layers in hybrid models, where parts of the network run on classical processors and others on quantum devices. Due to current hardware limitations, QNNs remain largely theoretical. They are tested on quantum simulators rather than full-scale quantum computers.
Quantum-enhanced reinforcement learning
Quantum-enhanced reinforcement learning investigates whether quantum computing can improve how AI agents learn optimal actions in an environment through trial and error. One study area involves using quantum states to represent the decision space, allowing simultaneous exploration of multiple decision paths. However, the field is highly experimental, and ideas are being tested primarily in toy environments or theoretical models.
How can AWS support your quantum computing and AI requirements?
AWS makes classical AI accessible to more people—from builders and data scientists to business analysts and students. AI on AWS includes pre-trained AI services for ready-made intelligence and AI infrastructure to maximize performance and lower costs.
- Ocelot, our first-generation quantum chip, uses a scalable architecture to accelerate the development of real-world quantum computing applications. It achieves the first realization of a scalable architecture for building scalable, commercially viable quantum computers for future quantum AI in practice.
- Amazon Braket is a fully managed service that helps you get started with quantum computing. You can use it to learn how to program quantum computers and design your own quantum algorithms from scratch. Amazon Braket also provides fully managed quantum circuit simulators that enable you to run your algorithms on AWS-managed infrastructure to validate and test your implementation.
- Amazon Quantum Solutions Lab engagements are collaborative research programs that allow you to work with leading experts in quantum AI. The programs help you research and identify your organization’s most promising quantum computing applications and get quantum-ready.
Get started with quantum computing and artificial intelligence on AWS by creating a free account today.