A team of researchers has demonstrated that some quantum computing problems once believed to be practical only for quantum machines can now be solved using conventional computers, tells Science Daily. The study introduces a tensor network algorithm that dramatically compresses the massive wave functions generated by entangled quantum systems, allowing complex simulations to run on hardware as modest as a standard laptop. The work challenges long-held assumptions about the boundary between classical and quantum computing while expanding the range of problems accessible to traditional computational methods.
The research focuses on one of quantum computing’s greatest challenges: representing the behavior of hundreds of interacting qubits. As qubits become entangled, the amount of information required to describe the system grows exponentially, making direct simulation impossible for most classical computers. Instead of storing every possible quantum state, the researchers used tensor networks to identify and preserve only the information essential to the system’s behavior. This compressed representation significantly reduced computational demands without sacrificing accuracy.
To validate the approach, the team compared its results with both theoretical predictions and simulations performed on a quantum computer. The agreement between the methods demonstrated that the tensor network algorithm could reliably reproduce the evolution of highly entangled quantum systems while using only a fraction of the computational resources previously thought necessary. In some cases, simulations that were expected to require specialized supercomputers could instead be completed on an ordinary laptop.
The researchers emphasize that the breakthrough does not eliminate the need for quantum computers. Many quantum problems remain too complex for classical techniques, particularly as systems grow larger and entanglement becomes more intricate. Instead, the new method redefines the practical limits of classical simulation, providing scientists with a powerful tool for investigating quantum materials, testing quantum algorithms, and verifying the performance of emerging quantum hardware.
The study suggests that advances in algorithms can be just as important as advances in hardware. By extending the capabilities of conventional computing, the new tensor network approach could accelerate research in quantum physics while helping researchers better understand where quantum computers will deliver genuine advantages over classical machines.
