Progress in algorithms makes small, noisy quantum computers viable


Date:
August 13, 2021
Source:
DOE/Los Alamos National Laboratory
Summary:
Instead of waiting for fully mature quantum computers to emerge,
researchers have developed hybrid classical/quantum algorithms to
extract the most performance -- and potentially quantum advantage --
from today's noisy, error-prone hardware.



FULL STORY ==========================================================================
As reported in a new article in Nature Reviews Physics, instead of
waiting for fully mature quantum computers to emerge, Los Alamos
National Laboratory and other leading institutions have developed
hybrid classical/quantum algorithms to extract the most performance --
and potentially quantum advantage -- from today's noisy, error-prone
hardware. Known as variational quantum algorithms, they use the quantum
boxes to manipulate quantum systems while shifting much of the work
load to classical computers to let them do what they currently do best:
solve optimization problems.


========================================================================== "Quantum computers have the promise to outperform classical computers
for certain tasks, but on currently available quantum hardware they
can't run long algorithms. They have too much noise as they interact
with environment, which corrupts the information being processed,"
said 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 complement 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 researches 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.

========================================================================== Story Source: Materials provided by
DOE/Los_Alamos_National_Laboratory. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru
Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai,
Xiao Yuan, Lukasz Cincio, Patrick J. Coles. Variational
quantum algorithms. Nature Reviews Physics, 2021; DOI:
10.1038/s42254-021-00348-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210813100316.htm

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