Quantum-classical hybrid algorithms for noisy quantum computers

Adam Callison, Nicholas Chancellor

The coming era of quantum computing promises to revolutionize computation, but, given the efficiency of many classical routines as well as quantum hardware limitations, calls to quantum devices are and will continue to be used as subroutines in larger, hybrid quantum-classical algorithms. This Perspective explores the essential differences between pure and hybrid quantum algorithms, discusses some examples, and frames the future of hybrid quantum-classical algorithms in its natural context of classical heterotic computing.

Jay Gambetta

Davoodi Yeganeh, H

The Jaynes-Cummings model is the canonical model for atom-light interactions, describing ‎a single confined bosonic mode interacting with a two-level system (qubit). This is ‎sufficient to describe a wide range of phenomena in quantum optics and quantum ‎computing. We simulate the dynamics of this model using the hybrid quantum-classical ‎algorithm (HQCA) consisting of quantum and classical computers. The parametric quantum ‎state preparations and quantum measurements are performed on the quantum computer ‎and parameters optimization employ on the classic computer. For implement of hybrid ‎quantum-classical algorithms, the Noisy Intermediate Scale Quantum (NISQ) computer is ‎used. In Noisy Intermediate Scale Quantum computers, we don’t need to error correction. ‎For this purpose, we transform Hamiltonian to qubit form and using an algorithm to obtain ‎the dynamic of the Jaynes-Cummings model. We obtain occupation probability and ‎transition probability in the Jaynes-Cummings model using the hybrid quantum-classical ‎algorithm. The output of the algorithm is compatible with the exact calculation‎‎‎‎.‎

Belal Ehsan Baaquie, L. C. Kwek

Alvira Nursahira, Reni Safitri, Muhammad Alvin +3 more

This paper presents a qualitative analysis of hybrid quantum-classical computing approaches aimed at solving complex optimization problems using near-term quantum processors. Hybrid algorithms leverage the strengths of quantum and classical computing to tackle computationally intensive tasks often constrained by current quantum hardware limitations. Through an extensive literature review and synthesis of recent empirical studies, we benchmark various hybrid algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), focusing on their performance, scalability, and practical applicability on noisy intermediate-scale quantum (NISQ) devices. The study highlights the advantages, challenges, and future prospects of integrating hybrid quantum-classical computation in optimization domains, providing a comprehensive framework and qualitative insights into benchmarking methodologies and critical performance metrics.

Samuele Ferracin, nate stemen, Joel J. Wallman