QUANTUM SIMULATIONS SCALE UP ON SUPERCOMPUTERS

Scientists have developed a parallel algorithm that simulates the behavior of complex quantum systems on supercomputers, tackling a challenge that has long stumped researchers.

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A NEW WAY TO MODEL QUANTUM SYSTEMS

Known as or-represented quantum algebra, or ORQA, this method efficiently models how groups of quantum particles interact, offering insights into materials science, chemistry, and physics.

Unlike traditional approaches bogged down by computational limits, ORQA splits tasks across thousands of processors, making it possible to study large-scale quantum dynamics. The algorithm focuses on many-body quantum systems, where particles like electrons or qubits influence each other in intricate ways. These interactions create exponential complexity, overwhelming even the fastest classical computers. ORQA sidesteps this by representing quantum states as combinations of simpler operations, called Pauli strings, which describe fundamental quantum properties. This allows supercomputers to handle systems with dozens or even hundreds of qubits, far beyond what was previously feasible.

The method was tested on a 127-qubit model, simulating a benchmark problem called the kicked Ising model, which explores chaotic quantum behavior. Running on a supercomputer, ORQA tracked how magnetization evolves over time, using up to a trillion Pauli strings to capture the system's state. The results showed near-linear scaling, meaning the algorithm's performance improves steadily as more processors are added, a key factor for tackling bigger problems.

WHY MANY-BODY PROBLEMS MATTER