Quantum Algorithms for solving Hard Constrained Optimisation Problems
- URL: http://arxiv.org/abs/2202.13125v1
- Date: Sat, 26 Feb 2022 12:23:17 GMT
- Title: Quantum Algorithms for solving Hard Constrained Optimisation Problems
- Authors: Parfait Atchade-Adelomou
- Abstract summary: The thesis deals with Quantum Algorithms for solving Hard Constrained Optimization Problems.
It shows how quantum computers can solve everyday problems such as finding the best schedule for social workers.
We have proposed EVA: a quantum Exponential Value Approximation algorithm that speeds up the VQE.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The thesis deals with Quantum Algorithms for solving Hard Constrained
Optimization Problems. It shows how quantum computers can solve difficult
everyday problems such as finding the best schedule for social workers or the
path of a robot picking and batching in a warehouse. The path to the solution
has led to the definition of a new artificial intelligence paradigm with
quantum computing, quantum Case-Based Reasoning (qCBR) and to a proof of
concept to integrate the capacity of quantum computing within mobile robotics
using a Raspberry Pi 4 as a processor (qRobot), capable of operating with
leading technology players such as IBMQ, Amazon Braket (D-Wave) and Pennylane.
To improve the execution time of variational algorithms in this NISQ era and
the next, we have proposed EVA: a quantum Exponential Value Approximation
algorithm that speeds up the VQE, and that is, to date, the flagship of the
quantum computation. To improve the execution time of variational algorithms in
this NISQ era and the next, we have proposed EVA: a quantum Exponential Value
Approximation algorithm that speeds up the VQE, and that is, to date, the
flagship of the quantum computation.
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