Pith. sign in

REVIEW 2 cited by

Theory and Implementation of the Quantum Approximate Optimization Algorithm: A Comprehensible Introduction and Case Study Using Qiskit and IBM Quantum Computers

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2301.09535 v1 pith:VBIDXRDB submitted 2023-01-23 quant-ph

classification quant-ph
keywords quantumimplementationtheoryalgorithmapproximatecasecomprehensiblecomputers
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The present tutorial aims to provide a comprehensible and easily accessible introduction into the theory and implementation of the famous Quantum Approximate Optimization Algorithm (QAOA). We lay our focus on practical aspects and step-by-step guide through the realization of a proof of concept quantum application based on a real-world use case. In every step we first explain the underlying theory and subsequently provide the implementation using IBM's Qiskit. In this way we provide a thorough understanding of the mathematical modelling and the (quantum) algorithms as well as the equally important knowledge how to properly write the code implementing those theoretical concepts. As another central aspect of this tutorial we provide extensive experiments on the 27 qubits state-of-the-art quantum computer ibmq_ehningen. From the discussion of these experiments we gain an overview on the current status of quantum computers and deduce which problem sizes can meaningfully be executed on today's hardware.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Analytical Expressions for the Quantum Approximate Optimization Algorithm and its Variants

    quant-ph 2024-11 conditional novelty 7.0 of 10

    Exact analytical expressions are derived for QAOA cost expectation values, unifying product-mixer variants and giving the first exact multi-layer results for Grover-type mixers, which are shown to be sensitive to cycl...

  2. Quantum Algorithm for Protein Side-Chain Optimisation: Comparing Quantum to Classical Methods

    quant-ph 2025-07 conditional novelty 5.0 of 10

    The authors show that QAOA with a local XY mixer finds ground-state rotamer configurations for small peptides with a milder fitted exponential scaling than their simulated annealing baseline, suggesting a crossover ar...

Pith tools