QIOA: Quantum Inspired Optimisation Algorithm
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Examensarbete för masterexamen
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Modellbyggare
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Sammanfattning
The current era of Quantum computing has been aptly named Noisy Intermediate-
Scale Quantum era, or NISQ era [1], owing to the highly promising, yet still imperfect
state of available quantum hardware. The applicability of these NISQ devices available
today is an area of research attracting increasing amounts of attention, as the
field of quantum computing grows in popularity. Quantum-classical variational algorithms
such as the Quantum Approximate Optimisation Algorithm [2], Variational
Quantum Eigensolver [3], and Variational Autoencoder [4] are promising attempts
in this direction, aiming to extract maximum value from NISQ devices. These algorithms
aim to minimise the number of operations on the quantum processor by
incorporating classical support-routine overheads.
The Quantum Approximate Optimisation Algorithm, also known as QAOA [2], is
one such hybrid algorithm designed to solve optimisation problems. In this thesis, we
study the QAOA using the framework of tensor networks and matrix product states
(MPS). We make approximations on the quantum aspects of QAOA by limiting
the amount of entanglement in QAOA circuits. Then, we analyse the effect of
these approximations on its performance, by applying this approximated method on
multiple Max-Cut and Exact-Cover problems of different sizes.
In the MPS formulation, it is the exponential increase of entanglement that makes
exact simulations of highly entangled quantum circuits, such as QAOA circuits, classically
infeasible. Our results suggest that it could still be possible to extract the
solutions of the optimisation problems QAOA tries to solve, even from an approximated
MPS-based QAOA-implementation with restricted entanglement. This added
restriction in entanglement growth makes our implementation classically tractable.
Hence, with this thesis, we propose a classical version of QAOA, which we are calling
Quantum Inspired Optimisation Algorithm, QIOA, that is inspired by the original
QAOA. We have validated the performance of QIOA on problems of small sizes, and
further studies are required to better understand its limitations.
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Ämne/nyckelord
Tensor Networks, QAOA, Quantum-inspired algorithms, Optimisation problems