CPQM’s quantum information processing laboratory collaborated with the CDISE supercomputing team “Zhores” to simulate Google’s quantum processor. By reproducing noise-free data based on the same statistics as Google’s recent experiments, the team was able to point out the subtle effects lurking in Google’s data. This effect is called an accessibility defect and was discovered by the Skoltech team in past work. The numbers confirm that Google’s data is on the edge of the so-called density-dependent avalanche, which means that future experiments will require more quantum resources to perform quantum approximation optimization. The results were published in the leading journal “Quantum” in the field. From the early days of numerical calculations, quantum systems have become extremely difficult to simulate, although the exact reason is still an active subject of research. Nevertheless, classical computers clearly have inherent difficulties in simulating quantum systems, which prompted several researchers to change their views. Scientists such as Richard Feynman and Yuri Manin speculated in the early 1980s that the unknown components that seemed to make quantum computers difficult to simulate with classical computers could themselves be used as computing resources. For example, quantum processors should be good at simulating quantum systems because they are governed by the same basic principles. This early idea eventually led Google and other tech giants to create prototype versions of the long-awaited quantum processors. These modern devices are prone to errors, they can only execute the simplest quantum programs, and each calculation must be repeated many times to average the error to finally form an approximation. One of the most studied applications of these contemporary quantum processors is the quantum approximation optimization algorithm, or QAOA (pronounced “kyoo-ay-oh-AY”). In a series of dramatic experiments, Google used its processor to probe the performance of QAOA with 23 qubits and three adjustable program steps. In short, QAOA is a method designed to approximate the optimization problem of a hybrid setup composed of a classical computer and a quantum coprocessor. Prototype quantum processors such as Google’s Sycamore are currently limited to performing noisy and limited operations. Using hybrid settings, the hope is to alleviate some of these system limitations and still restore quantum behavior to take advantage of, making methods such as QAOA particularly attractive. Skoltech scientists have recently made a series of discoveries related to QAOA, for example, please refer to the article here. What stands out is the impact that fundamentally limits the applicability of QAOA. They show that the density of the optimization problem—that is, the ratio between its constraints and variables—is the main obstacle to achieving approximate solutions.
News Source : Sci Tech Daily
