# Quantum annealing initialization of the quantum approximate optimization algorithm

@article{Sack2021QuantumAI, title={Quantum annealing initialization of the quantum approximate optimization algorithm}, author={Stefan H. Sack and Maksym Serbyn}, journal={Quantum}, year={2021}, volume={5}, pages={491} }

The quantum approximate optimization algorithm (QAOA) is a prospective near-term quantum algorithm due to its modest circuit depth and promising benchmarks. However, an external parameter optimization required in QAOA could become a performance bottleneck. This motivates studies of the optimization landscape and search for heuristic ways of parameter initialization. In this work we visualize the optimization landscape of the QAOA applied to the MaxCut problem on random graphs, demonstrating…

## 16 Citations

Parameter concentrations in quantum approximate optimization

- PhysicsPhysical Review A
- 2021

The quantum approximate optimization algorithm (QAOA) has become a cornerstone of contemporary quantum applications development. In QAOA, a quantum circuit is trained -- by repeatedly adjusting…

QTML 2021 Quantum Techniques in Machine Learning

- Computer Science
- 2021

This work shows matrix completion algorithms mitigate data transfer costs scaling quadratically with the data set size while being robust to shot noise, and formalizes the relationship between properties of quantum circuits and completability of quantum kernel matrices.

GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm

- PhysicsComputer Physics Communications
- 2022

Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware

- Computer Science, Physics
- 2022

This work investigates swap strategies to map dense problems into linear, grid and heavy-hex coupling maps and finds that the required gate fidelity for dense problems lies deep below the fault-tolerant threshold.

Dual Map Framework for Noise Characterization of Quantum Computers

- Computer Science, Physics
- 2021

This paper presents a method that faithfully reconstructs a marginal (local) approximation of the effective noise (MATEN) channel, that acts as a single layer at the end of the circuit, that justifies the MATEN, even in the presence of non-local errors that occur during a circuit.

Avoiding barren plateaus using classical shadows

- Computer Science
- 2022

This work defines a notion of weak barren plateaus (WBP) based on the entropies of local reduced density matrices and demonstrates that decreasing the gradient step size allows to avoid WBPs during the optimization process.

Counterdiabaticity and the quantum approximate optimization algorithm

- PhysicsQuantum
- 2022

The quantum approximate optimization algorithm (QAOA) is a near-term hybrid algorithm intended to solve combinatorial optimization problems, such as MaxCut. QAOA can be made to mimic an adiabatic…

Peptide conformational sampling using the Quantum Approximate Optimization Algorithm

- Computer Science
- 2022

This work numerically investigates the performance of a variational quantum algorithm, the Quantum Approximate Optimization Algorithm (QAOA), in sampling low-energy conformations of short peptides, and casts serious doubt on the ability of QAOA to address the protein folding problem in the near term, even in an ex-tremely simpliﬁed setting.

An evolving objective function for improved variational quantum optimisation

- Computer Science
- 2021

It is shown that Ascending-CVaR in all cases performs better than standard objective functions or the “constant” CVaR of Barkoutsos et al and that it can be used as a heuristic for avoiding sub-optimal minima.

Benchmarking Small-Scale Quantum Devices on Computing Graph Edit Distance

- Computer ScienceArXiv
- 2021

This paper presents a comparative study of two quantum approaches to computing GED: quantum annealing and variational quantum algorithms, which refer to the two types of quantum hardware currently available, namely quantumAnnealer and gate-based quantum computer, respectively.

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