I. Applications of Artificial Intelligence
Artificial intelligence (AI) has permeated every aspect of our lives, including smart homes, smart vehicles, smart healthcare, and smart finance. In the smart home sector, AI enables voice control, automatic recognition, and intelligent recommendations, making our lives more convenient and comfortable. In the smart vehicle sector, AI enables autonomous driving, traffic congestion prediction, and intelligent navigation, improving driving safety and efficiency. In smart healthcare, AI assists doctors in disease diagnosis and treatment planning, improving medical standards and efficiency. In smart finance, AI can perform risk assessment, investment decisions, and customer service, enhancing the intelligence level of the financial industry.
Beyond the aforementioned application areas, artificial intelligence (AI) has many promising future directions. For example, AI can be combined with biotechnology and nanotechnology to develop more intelligent and efficient robots, bringing greater convenience to human production and life. Simultaneously, AI can be applied to education, helping teachers teach better, helping students learn better, and personalizing educational programs. Furthermore, AI can play a crucial role in environmental protection, for instance, by using intelligent sensors and data analysis technologies to achieve real-time monitoring and early warning of air and water quality, helping to protect the environment and ecosystems.
II. Quantum computers may be the first to be applied to artificial intelligence.
As the most cutting-edge application of electronic computers today, artificial intelligence has always faced concerns about insufficient computing power. A few months after ChatGPT's launch, OpenAI President Altman publicly stated that he was not encouraging more users to register for OpenAI. In November 2023, OpenAI even announced a suspension of new registrations for ChatGPT Plus paid subscriptions to ensure existing users had a high-quality experience. Clearly, as the world's most powerful AI, ChatGPT has encountered bottlenecks in computing power and other areas. Against this backdrop, discussing the application of quantum computers in the field of artificial intelligence becomes a promising future solution.
First, most algorithms in the field of artificial intelligence fall under the category of parallel computing. For example, when AlphaGo plays Go, it needs to consider the opponent's responses after placing stones in different positions simultaneously, and find the most likely way to win the game. This requires optimizing the efficiency of parallel computing. Quantum computers excel at parallel computing because they can simultaneously compute and store both "0" and "1" states without consuming additional computing resources like electronic computers, such as serializing multiple computing units or parallelizing computational tasks. The more complex the computational task, the greater the advantage of quantum computing.
Secondly, the hardware requirements for running ChatGPT are also well-suited for use in today's massive quantum computers. Both need to be installed in a highly integrated computing center and managed and supported by a professional technical team.
What is a quantum computer? A quantum computer is a physical device that performs high-speed mathematical and logical operations, stores and processes quantum information according to the laws of quantum mechanics. Not only are they enormous, but their core component, the "quantum chip," typically needs to be placed in extremely low temperatures close to absolute zero (minus 273.15 degrees Celsius). It utilizes the quantum properties exhibited by some microscopic particles at these extremely low temperatures to perform information computation and processing, and the results only exist for a few milliseconds.
Since quantum computers are "large and difficult to maintain," why develop them at all? The reason lies in their enormous computational potential, to the point that they already demonstrate an "absolute crushing" speed advantage over electronic computers in some algorithms—a phenomenon known as "quantum supremacy." However, achieving "quantum supremacy" is only a starting point. Current quantum computers can only perform computational tasks specific to the quantum realm. To truly leverage this "quantum supremacy," they must first have a sufficient number of qubits to achieve general-purpose computing and programmability. Moreover, even after achieving general-purpose computing, quantum computers still need to maintain their advantage over electronic computers; this is known as "quantum advantage."
In 2022, researchers from Google, Microsoft, Caltech, and other institutions demonstrated in principle that "quantum advantage" does indeed exist in predicting observable variables, quantum principal component analysis, and quantum machine learning. Quantum machine learning is essentially the application of quantum computing in the field of artificial intelligence, reflecting the future trend of the convergence of these two cutting-edge technologies.
Theoretically proven, the practical application of quantum computing needs further expansion. Following the launch of its commercial quantum computer, Quantum System-1, in 2019, US quantum computing giant IBM launched Quantum System-2 in December 2023. The biggest breakthrough of the new system is its modular expansion capability, making it the company's first modular quantum computer. Quantum System-2 has over 1000 qubits. IBM also announced plans to build a 100,000-qubit quantum computer within 10 years. These increasing qubits are not just for competition; they play an indispensable role in achieving general-purpose computing and programmability. Therefore, the modularity of quantum computers signifies their greater practicality.
Research on quantum machine learning algorithms has become a new research hotspot. However, quantum computers will not completely replace electronic computers in the future. It is more likely that quantum computers and electronic computers will leverage their respective strengths in different application scenarios to achieve synergistic development, which will greatly improve computing power while also taking into account cost and feasibility.
