Credit: Pixabay/CC0 public domainAccording to a Nature Reviews Physics article, Los Alamos National Laboratory has developed hybrid quantum/classical algorithms that can extract the best performance from today's noisy and error-prone hardware. They are also known as variational quantum algorithm, and they use quantum boxes to manipulate quantum systems. However, classical computers take most of the work load. This allows them to do what they do best: solve optimization problem.Although quantum computers are capable of performing certain tasks better than classical computers, they cannot run long algorithms on the currently available quantum hardware. They produce too much noise when they interact with the environment, which corrupts information being processed," Marco Cerezo, a leading author of the paper and a physicist at Los Alamos who specializes in quantum computing, quantum machine-learning, and quantum information, said. "With variational quantum algorithm, we can get the best of both. You can use quantum computers to accomplish tasks that are difficult for classical computers, and then you can use classical computers to complement the quantum computing power.The current noisy, intermediate-scale quantum computers are between 50 and 100 qubits in size, lose their "quantumness", and do not provide error correction. This requires more qubits. Theoreticians have been working on algorithms that can run on a large, fault-tolerant, error-correcting quantum computer since the late 1990s.These algorithms are too complex or give nonsense results, so we can't use them yet. People realized that we needed an approach that can adapt to the hardware constraints. This was the optimization problem," stated Patrick Coles, a Los Alamos theoretical physicist who developed algorithms and is the senior author of this paper.Coles stated that "we found we could transform all the problems of curiosity into optimization problems, possibly with quantum advantage," meaning that the quantum computer can beat a traditional computer at the task. These problems include simulations of material science and quantum chemicals, factoring numbers and big-data analysis. They also cover virtually every possible application for quantum computers.Variational algorithms are used because the optimization process changes the algorithm as needed. This is a form of machine learning. It alters logic gates and parameters to reduce a cost function. This is a mathematical expression that indicates how well an algorithm has performed its task. When the cost function is at its lowest value, the problem can be solved.The variational quantum algorithm uses an iterative function to estimate the cost function. It then sends that result back the the classical computer. The input parameters are adjusted by the classical computer and sent to the quantum computer. It then runs the optimization again.This review article serves as a guideline and overview for researchers who are just beginning to explore this new field. The authors cover all aspects of algorithms, including how they work and the challenges and pitfalls. It also looks to the future and considers the best ways to achieve quantum advantage on computers in the coming years.Continue reading Running quantum software on a classic computerMore information: M. Cerezo et al, Variational quantum algorithms, Nature Reviews Physics (2021). M. Cerezo et al, Variational quantum algorithms,(2021). DOI: 10.1038/s42254-021-00348-9
