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| Workers calibrate and install the China's independently developed third-generation superconducting quantum computer. Photo:Courtesy: Anhui Quantum Computing Engineering Research Center |
This breakthrough led to an 8.4% improvement in training performance while reducing the number of parameters by 76%. The Al model also showed better results in specific tasks-when trained on mental health conversation data, it made 15% fewer mistakes, and in a math problem-solving test, its accuracy jumped from 68% to 82%.
The fine-tuning process traditionally requires high computing power, but quantum computing offers unique Chinese researchers have achieved a global first by using a real quantum computer to fine-tune an Al model with one billion parameters. The experiment was conducted on Origin Wukong, China's third-generation superconducting quantum computer with 72 qubits.
This breakthrough led to an 8.4% improvement in training performance while reducing the number of parameters by 76%. The Al model also showed better results in specific tasks-when trained on mental health conversation data, it made 15% fewer mistakes, and in a math problem-solving test, its accuracy jumped from 68% to 82%.
The fine-tuning process traditionally requires high computing power, but quantum computing offers unique advantages. By leveraging superposition and entanglement, quantum computers can explore vast combinations of parameters simultaneously, making Al training faster and more efficient.
This development could be a game-changer for Al training, reducing computational costs and improving model efficiency.
Experts have reacted with cautious optimism to China's breakthrough in using a quantum computer to fine-tune a billion-parameter AI model.
Some experts remain skeptical, pointing out that while the results are promising, the research is still in the demonstration phase and lacks peer-reviewed validation.
The fine-tuning process traditionally requires high computing power, but quantum computing offers unique Chinese researchers have achieved a global first by using a real quantum computer to fine-tune an Al model with one billion parameters. The experiment was conducted on Origin Wukong, China's third-generation superconducting quantum computer with 72 qubits.
This breakthrough led to an 8.4% improvement in training performance while reducing the number of parameters by 76%. The Al model also showed better results in specific tasks-when trained on mental health conversation data, it made 15% fewer mistakes, and in a math problem-solving test, its accuracy jumped from 68% to 82%.
The fine-tuning process traditionally requires high computing power, but quantum computing offers unique advantages. By leveraging superposition and entanglement, quantum computers can explore vast combinations of parameters simultaneously, making Al training faster and more efficient.
This development could be a game-changer for Al training, reducing computational costs and improving model efficiency.
Experts have reacted with cautious optimism to China's breakthrough in using a quantum computer to fine-tune a billion-parameter AI model.
Some experts remain skeptical, pointing out that while the results are promising, the research is still in the demonstration phase and lacks peer-reviewed validation.
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