Table 3839. Comparisons among artificial intelligence (AI), machine learning (ML) and quantum machine learning (QML).
| |
Artificial intelligence |
Machine learning |
Quantum machine learning |
| Definition |
AI is a broad field of computer science that focuses on creating systems or machines that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and making decisions. |
ML is a subset of AI that specifically deals with the development of algorithms and models that enable machines to learn from data and improve their performance on a task over time. |
QML is a specialized field of ML that leverages the principles of quantum mechanics to develop and optimize machine learning algorithms. |
| Data and Learning |
AI systems can be rule-based, symbolic, or data-driven. They may not always rely on data for decision-making. |
ML is data-driven and relies on the availability of labeled data for training algorithms. It involves learning patterns and making predictions or decisions based on data. |
QML, like classical ML, relies on data and quantum algorithms to learn patterns and make predictions, but it leverages quantum computing power for certain tasks. |
| Computing Infrastructure |
AI systems can run on traditional computing hardware and do not necessarily require specialized hardware. |
ML models can be trained and deployed on standard CPUs and GPUs but may benefit from specialized hardware for performance improvement (e.g., TPUs). |
QML requires quantum computing infrastructure, such as quantum processors and quantum gates, to perform quantum operations. |
| Algorithms |
AI encompasses a wide range of algorithms, including expert systems, knowledge graphs, and more. |
ML includes algorithms like decision trees, support vector machines, neural networks, and clustering algorithms. |
QML utilizes quantum algorithms like quantum variational circuits and quantum support vector machines. |
| Applications |
AI applications are diverse, including natural language processing, robotics, computer vision, and game playing. |
ML is used in recommendation systems, image and speech recognition, fraud detection, and many other applications. |
QML is primarily in the research and development stage but shows promise in areas like drug discovery, optimization, and cryptography. |
| Data Dependency |
Both ML and QML rely on data for training and decision-making, although the data may differ in nature for quantum datasets. |
| Problem-Solving |
All three fields are focused on solving complex problems, making decisions, and optimizing outcomes. |
| Mathematical Foundations |
ML and QML share some mathematical foundations, as quantum algorithms can be used to improve certain ML tasks. |
| Optimization |
ML and QML often involve optimization techniques to fine-tune models or algorithms. |
