Quantum annealing learning search
implementations
(pp0181-0208)
Andrea Bonomi,
Thomas De Min,
Enrico
Zardini,
Enrico Blanzieri,
Valter Cavecchia,
and
Davide
Pastorello
doi:
https://doi.org/10.26421/QIC22.3-4-1
Abstracts:
This paper presents the details and testing of two
implementations (in C++ and Python) of the hybrid quantum-classical
algorithm Quantum Annealing Learning Search (QALS)
on a D-Wave quantum
annealer.
QALS
was proposed in 2019 as a novel technique to solve general
QUBO
problems that cannot be directly represented into the hardware
architecture of a D-Wave machine. Repeated calls to the quantum machine
within a classical iterative structure and a related convergence proof
originate a learning mechanism to find an encoding of a given problem
into the quantum architecture. The present work considers the Number
Partitioning Problem (NPP)
and the
Travelling Salesman Problem (TSP)
for the testing of
QALS.
The results turn out to be quite unexpected, with
QALS
not being able to perform as well as the other considered methods,
especially in
NPP,
where classical methods outperform quantum annealing in general.
Nevertheless, looking at the TSP tests,
QALS
has fulfilled its primary goal, i.e., processing
QUBO
problems not directly
mappable
to the
QPU topology.
Key Words:
Quantum
Annealing, Quantum-Classical Hybrid Algorithm, Binary Optimization,
Quantum Software, Empirical Evaluation |