Protein Folding Quantum Circuit for Bio Material Modelling Compression

Lisnichenko, Marina; Protasov, Stanislav

doi:10.1134/s1063739722080182

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quantum computing has the potential to revolutionize various fields, including optimization, machine learning, chemistry and cryptography, solving matrix and systems of linear equations [1][2][3][4][5][6][7][8][9][10][11]. One of the most promising quantum algorithms for optimization problems is quantum approximate optimization algorithm (QAOA), which was proposed by Farhi et al in 2014 [12].…”

Section: Introductionmentioning

confidence: 99%

Entropic property of randomized QAOA circuits

Chernyavskiy,

Bantysh,

Bogdanov

2023

Laser Phys. Lett.

View full text Add to dashboard Cite

Quantum approximate optimization algorithm (QAOA) aims to solve discrete optimization problems by sampling bitstrings using a parameterized quantum circuit. The circuit parameters (angles) are optimized in the way that minimizes the cost Hamiltonian expectation value. Recently, general statistical properties of QAOA output probability distributions have begun to be studied. In contrast to the conventional approach, we analyse QAOA circuits with random angles. We provide analytical equations for probabilities and the numerical evidence that for unweighted Max-Cut problems on connected graphs such sampling always gives higher entropy of energy distribution than uniform random sampling of bitstrings. We also analyse the probability to obtain the global optima, which appears to be higher on average than for random sampling.

show abstract