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An introduction to variational quantum algorithms for combinatorial optimization problems

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arxiv 2212.11734 v2 pith:L5UW2UGP submitted 2022-12-22 math.OC quant-ph

classification math.OCquant-ph
keywords quantumalgorithmsoptimizationqaoavariationalalgorithmcombinatorialcomputers
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Noisy intermediate-scale quantum computers (NISQ computers) are now readily available, motivating many researchers to experiment with Variational Quantum Algorithms (VQAs). Among them, the Quantum Approximate Optimization Algorithm (QAOA) is one of the most popular one studied by the combinatorial optimization community. In this tutorial, we provide a mathematical description of the class of Variational Quantum Algorithms, assuming no previous knowledge of quantum physics from the readers. We introduce precisely the key aspects of these hybrid algorithms on the quantum side (parametrized quantum circuit) and the classical side (guiding function, optimizer). We devote a particular attention to QAOA, detailing the quantum circuits involved in that algorithm, as well as the properties satisfied by its possible guiding functions. Finally, we discuss the recent literature on QAOA, highlighting several research trends.

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  1. AEQUAM: Accelerating Quantum Algorithm Validation through FPGA-Based Emulation

    quant-ph 2025-06 conditional novelty 4.0 of 10

    AEQUAM compiles OpenQASM 2.0 circuits into customizable FPGA emulators and demonstrates six-qubit emulation on a low-cost Cyclone 10LP FPGA with 20-bit fixed-point arithmetic.

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