Running quantum software on a classical computer

Date:
August 3, 2021
Source:
Ecole Polytechnique Fe'de'rale de Lausanne
Summary:
Physicists have introduced an approach for simulating the
quantum approximate optimization algorithm using a traditional
computer. Instead of running the algorithm on advanced quantum
processors, the new approach uses a classical machine-learning
algorithm that closely mimics the behavior of near-term quantum
computers.



FULL STORY ==========================================================================
In a paper published in Nature Quantum Information, EPFL professor
Giuseppe Carleo and Matija Medvidovi?, a graduate student at Columbia University and at the Flatiron Institute in New York, have found a way
to execute a complex quantum computing algorithm on traditional computers instead of quantum ones.


==========================================================================
The specific "quantum software" they are considering is known as
Quantum Approximate Optimization Algorithm (QAOA) and is used to solve classical optimization problems in mathematics; it's essentially a
way of picking the best solution to a problem out of a set of possible solutions. "There is a lot of interest in understanding what problems
can be solved efficiently by a quantum computer, and QAOA is one of the
more prominent candidates," says Carleo.

Ultimately, QAOA is meant to help us on the way to the famed "quantum
speedup," the predicted boost in processing speed that we can achieve
with quantum computers instead of conventional ones. Understandably,
QAOA has a number of proponents, including Google, who have their
sights set on quantum technologies and computing in the near future:
in 2019 they created Sycamore, a 53-qubit quantum processor, and used
it to run a task it estimated it would take a state-of-the-art classical supercomputer around 10,000 years to complete.

Sycamore ran the same task in 200 seconds.

"But the barrier of "quantum speedup" is all but rigid and it is being continuously reshaped by new research, also thanks to the progress in
the development of more efficient classical algorithms," says Carleo.

In their study, Carleo and Medvidovi? address a key open question in the
field: can algorithms running on current and near-term quantum computers
offer a significant advantage over classical algorithms for tasks of
practical interest? "If we are to answer that question, we first need
to understand the limits of classical computing in simulating quantum
systems," says Carleo. This is especially important since the current generation of quantum processors operate in a regime where they make
errors when running quantum "software," and can therefore only run
algorithms of limited complexity.

Using conventional computers, the two researchers developed a method that
can approximately simulate the behavior of a special class of algorithms
known as variational quantum algorithms, which are ways of working out the lowest energy state, or "ground state" of a quantum system. QAOA is one important example of such family of quantum algorithms, that researchers believe are among the most promising candidates for "quantum advantage"
in near-term quantum computers.

The approach is based on the idea that modern machine-learning tools,
e.g. the ones used in learning complex games like Go, can also be used to
learn and emulate the inner workings of a quantum computer. The key tool
for these simulations are Neural Network Quantum States, an artificial
neural network that Carleo developed in 2016 with Matthias Troyer, and
that was now used for the first time to simulate QAOA. The results are considered the province of quantum computing, and set a new benchmark
for the future development of quantum hardware.

"Our work shows that the QAOA you can run on current and near-term
quantum computers can be simulated, with good accuracy, on
a classical computer too," says Carleo. "However, this does not
mean that alluseful quantum algorithms that can be run on near-term
quantum processors can be emulated classically. In fact, we hope that
our approach will serve as a guide to devise new quantum algorithms
that are both useful and hard to simulate for classical computers." ========================================================================== Story Source: Materials provided by
Ecole_Polytechnique_Fe'de'rale_de_Lausanne. Original written by Nik Papageorgiou. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Matija Medvidović, Giuseppe Carleo. Classical variational
simulation
of the Quantum Approximate Optimization Algorithm. npj Quantum
Information, 2021; 7 (1) DOI: 10.1038/s41534-021-00440-z ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210803121404.htm

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