Artificial Intelligence (“AI”) continues to command attention as today’s prominent technological asset, revolutionizing key markets and sectors. Simultaneously, discussions of another advanced technology known as quantum computing have gained traction. Because both technologies expand the universe of problems that can be tackled by computers, one might wonder, if we have AI, do we also need quantum computing technologies? In this article, we will discuss quantum computing and how it complements AI, including its ability to enhance AI models, and conversely, also explore AI’s ability to strengthen the power of quantum computing.
AI vs. Quantum Computing
AI and quantum computing are two distinct, but complementary, technologies. Generally, AI involves the development of machines that are designed to simulate human intelligence. Quantum computing focuses on strengthening the power of computing, harnessing the principles of quantum mechanics to solve complex problems. Unlike classical computers which rely on binary bits to process data, quantum computers encode using quantum bits (“qubits”). Qubits allow quantum computers to perform complex calculations without the need to explicitly perform every step required in a classical computer, thus allowing them to arrive at answers much faster (in some cases, even exponentially faster) than classical computers.
How Quantum Computing Can Accelerate AI Capabilities
Most of today’s AI models are trained and deployed on classical computers. The limitations of classical computers in storing and processing massive datasets have, at times, hindered the continued scaling of large language models, which rely on vast amounts of data to improve their performance and to fine-tune their capabilities. Contrastingly, quantum computers may excel by processing and analyzing large, complex datasets more efficiently than classical computers, potentially enabling AI models to achieve more powerful training, reasoning, and understanding. With an increased ability to process complex information in parallel, future AI models deployed on quantum computers may expedite decision making, performing certain tasks and solving certain complex issues much faster than classical computers.
Quantum Machine Learning (“QML”) is a developing field that explores how quantum computing can enhance or accelerate machine learning by integrating quantum algorithms to enhance model training, data processing, or optimization. QML creates a powerful synergy, leveraging quantum algorithms to solve optimization problems more efficiently than classical computers. Quantum algorithms such as the Quantum Approximate Optimization Algorithm, enhance the capabilities of machine learning models, thereby enabling advancements in tasks such as classification, regression, predictive analytics, and clustering. Though QML is in its early, experimental phases, it is gaining traction across certain industries such as in the life sciences for drug discovery, logistics for route optimizations, financial services for enhancing fraud detection and risk management, aerospace to optimize aircraft design and operations, and energy to forecast consumption patterns and improve grid management, among others.
How AI Can Enhance Quantum Computing
From the inverse perspective, AI has shown promise in unlocking quantum computing’s potential. Quantum computers are infamously delicate machines that can be challenging to calibrate, are susceptible to noise and interference that can ruin entire calculations, and demand extensive computational infrastructure across multiple layers of software and hardware. The volume of sequential operations that can be executed by quantum computers is often limited by qubit decoherence, a process in which a quantum system loses its coherence due to interaction with its surrounding environment. Recent scientific studies have shown that AI can help stabilize and optimize quantum systems. Specifically, deep learning models running on classical computers have succeeded in detecting system parameter drifts and characterizing states in quantum computers, consequently improving the overall performance of quantum computers. Furthermore, AI has shown potential to provide support in developing fault-tolerant quantum computers. AI is also being applied in quantum error correction, which remains a critical issue and must be dealt with in order to build fault-tolerant, scalable quantum computers. Machine learning algorithms are used to detect errors and mitigate high error rates that are caused by qubit instability. Quantum error correction is integral to making quantum computing more practical and scalable.
Hybrid Systems
In addition to the ability of one technology to improve the other, AI and quantum computing can work together as complementary, hybrid systems. For example, AI and quantum computing can complement each other in drug discovery: AI can analyze datasets to identify which molecules to prioritize, while quantum computers may be used to deliver more precise simulations of such molecules. Viewed broadly, AI and quantum computing can be combined in hybrid systems where classical AI handles more general tasks while quantum computing could be selectively applied to the most complex or resource intensive problems.