Swedish researchers have achieved a major breakthrough in quantum computing that could dramatically accelerate artificial intelligence applications and transform how AI models are trained and deployed.
On June 24, 2025, a team led by doctoral student Yin Zeng at Chalmers University of Technology unveiled a pulse-driven qubit amplifier that addresses one of the most significant challenges in scaling up quantum computers: power consumption and heat generation.
The innovative amplifier activates only when reading information from qubits, consuming just one-tenth of the power required by today's best amplifiers without compromising performance. This dramatic reduction in power consumption helps prevent qubits from losing their quantum state—a phenomenon known as decoherence—which has been a major limiting factor in quantum computing.
"This is the most sensitive amplifier that can be built today using transistors," explains Zeng, the first author of the study published in IEEE Transactions on Microwave Theory and Techniques. "We've managed to reduce its power consumption to just one-tenth of that required by today's best amplifiers without compromising performance."
The team used genetic programming to enable smart control of the amplifier, allowing it to respond to incoming qubit pulses in just 35 nanoseconds. This speed is crucial since quantum information is transmitted in pulses, and the amplifier must activate rapidly enough to keep pace with qubit readout.
Professor Jan Grahn, who supervised the research, notes: "This study offers a solution in future upscaling of quantum computers where the heat generated by these qubit amplifiers poses a major limiting factor."
The implications for AI are profound. Recent experiments by researchers at the University of Vienna have demonstrated that even small-scale quantum computers can enhance machine learning performance using novel photonic quantum circuits. Their findings suggest that today's quantum technology isn't just experimental—it can already deliver practical advantages for specific AI applications.
Quantum computers leverage the principles of quantum mechanics, allowing qubits to exist in multiple states simultaneously. This enables them to process complex problems far beyond the capabilities of classical computers. With just 20 qubits, a quantum computer can represent over a million different states at once.
As quantum computers scale up with more qubits, their computational power increases exponentially, but so does the challenge of managing heat and preventing decoherence. The Chalmers breakthrough directly addresses this challenge, potentially enabling the development of larger, more stable quantum systems specifically optimized for AI workloads.
Experts predict that quantum-enhanced AI could revolutionize fields including drug discovery, materials science, financial modeling, and complex optimization problems that are currently intractable even for the most powerful supercomputers.