Progress in Hybrid Algorithms Makes Small, Noisy Quantum Computer systems Viable

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Advanced Computer Algorithm Concept

Hybrid algorithms can accommodate restricted qubits, lack of error correction for real-world duties.

As reported in an article in Nature Evaluations Physics, as an alternative of ready for absolutely mature quantum computer systems to emerge, Los Alamos Nationwide Laboratory and different main establishments have developed hybrid classical/quantum algorithms to extract probably the most efficiency—and doubtlessly quantum benefit—from at the moment’s noisy, error-prone {hardware}. Generally known as variational quantum algorithms, they use the quantum containers to control quantum techniques whereas shifting a lot of the work load to classical computer systems to allow them to do what they presently do finest: remedy optimization issues.

“Quantum computer systems have the promise to outperform classical computer systems for sure duties, however on presently out there quantum {hardware} they’ll’t run lengthy algorithms. They’ve an excessive amount of noise as they work together with atmosphere, which corrupts the knowledge being processed,” stated Marco Cerezo, a physicist specializing in quantum computing, quantum machine learning, and quantum information at Los Alamos and a lead author of the paper. “With variational quantum algorithms, we get the best of both worlds. We can harness the power of quantum computers for tasks that classical computers can’t do easily, then use classical computers to compliment the computational power of quantum devices.”

Current noisy, intermediate scale quantum computers have between 50 and 100 qubits, lose their “quantumness” quickly, and lack error correction, which requires more qubits. Since the late 1990s, however, theoreticians have been developing algorithms designed to run on an idealized large, error-correcting, fault-tolerant quantum computer.

“We can’t implement these algorithms yet because they give nonsense results or they require too many qubits. So people realized we needed an approach that adapts to the constraints of the hardware we have—an optimization problem,” said Patrick Coles, a theoretical physicist developing algorithms at Los Alamos and the senior lead author of the paper.

“We found we could turn all the problems of interest into optimization problems, potentially with quantum advantage, meaning the quantum computer beats a classical computer at the task,” Coles said. Those problems include simulations for material science and quantum chemistry, factoring numbers, big-data analysis, and virtually every application that has been proposed for quantum computers.

The algorithms are called variational because the optimization process varies the algorithm on the fly, as a kind of machine learning. It changes parameters and logic gates to minimize a cost function, which is a mathematical expression that measures how well the algorithm has performed the task. The problem is solved when the cost function reaches its lowest possible value.

In an iterative function in the variational quantum algorithm, the quantum computer estimates the cost function, then passes that result back to the classical computer. The classical computer then adjusts the input parameters and sends them to the quantum computer, which runs the optimization again.

The review article is meant to be a comprehensive introduction and pedagogical reference for researchers starting on this nascent field. In it, the authors discuss all the applications for algorithms and how they work, as well as cover challenges, pitfalls, and how to address them. Finally, it looks into the future, considering the best opportunities for achieving quantum advantage on the computers that will be available in the next couple of years.

Reference: “Variational Quantum Algorithms” by M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio and Patrick J. Coles, 12 August 2021, Nature Reviews Physics.
DOI: 10.1038/s42254-021-00348-9

Funding: U.S Department of Energy (DOE) Office of Science, Advanced Scientific Computing Research program; DOE Quantum Science Center (QSC); Laboratory Directed Research and Development program, Los Alamos National Laboratory.

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