• Corpus ID: 252519581

A Depth-Progressive Initialization Strategy for Quantum Approximate Optimization Algorithm

@inproceedings{Lee2022ADI,
  title={A Depth-Progressive Initialization Strategy for Quantum Approximate Optimization Algorithm},
  author={Xinwei Lee and Ning Xie and Yoshiyuki Saito and DongSheng Cai and Nobuyoshi Asai},
  year={2022}
}
The quantum approximate optimization algorithm (QAOA) is known for its capability and uni-versality in solving combinatorial optimization problems on near-term quantum devices. The results yielded by QAOA depend strongly on its initial variational parameters γ and β . Hence, parameters selection for QAOA becomes an active area of research as bad initialization might deteriorate the quality of the results, especially at great circuit depths. We first discuss the patterns of optimal parameters in… 

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References

SHOWING 1-10 OF 34 REFERENCES

Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices

An in-depth study of the performance of QAOA on MaxCut problems is provided by developing an efficient parameter-optimization procedure and revealing its ability to exploit non-adiabatic operations, illustrating that optimization will be important only for problem sizes beyond numerical simulations, but accessible on near-term devices.

Quantum annealing initialization of the quantum approximate optimization algorithm

The initialization of QAOA parameters based on the Trotterized quantum annealing (TQA) protocol is introduced, finding that the TQA initialization allows to circumvent the issue of false minima for a broad range of time steps, yielding the same performance as the best result out of an exponentially scaling number of random initializations.

Accelerating Quantum Approximate Optimization Algorithm using Machine Learning

A machine learning based approach to accelerate quantum approximate optimization algorithm (QAOA) implementation which is a promising quantum-classical hybrid algorithm to prove the so-called quantum supremacy by developing a machine learning model to predict the gate parameters close to the optimal values.

On Circuit Depth Scaling For Quantum Approximate Optimization

A predictive model, based on a logistic saturation conjecture for critical depth scaling with respect to density, is proposed, which shows a linear trend for the critical depth with respect problem size is recovered for the range of 5 to 15 qubits.

For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances

Findings suggest ways to run the QAOA that reduce or eliminate the use of the outer loop optimization and may allow us to find good solutions with fewer calls to the quantum computer.

Performance of the Quantum Approximate Optimization Algorithm on the Maximum Cut Problem

It is found that QAOA can amortize the training cost by optimizing on batches of problems instances, and can exceed the performance of the classical polynomial time Goemans-Williamson algorithm with modest circuit depth, and that performance with fixed circuit depth is insensitive to problem size.

Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm

This work studies unsupervised Machine Learning approaches for setting parameters without optimization of the Quantum Approximate Optimization Algorithm, and performs clustering with the angle values but also instances encodings, and compares different approaches.

The Quantum Alternating Operator Ansatz on Maximum k-Vertex Cover

This paper studies Maximum k-Vertex Cover under this ansatz due to its modest complexity, while still being more complex than the well studied problems of Max-Cut and Max E3-LIN2.

Exploiting Symmetry Reduces the Cost of Training QAOA

A novel approach for accelerating the evaluation of QAOA energy by leveraging the symmetry of the problem by showing a connection between classical symmetries of the objective function and the symmetry of the terms of the cost Hamiltonian with respect to the QAoa energy.

Quantum Supremacy through the Quantum Approximate Optimization Algorithm

It is argued that beyond its possible computational value the QAOA can exhibit a form of Quantum Supremacy in that, based on reasonable complexity theoretic assumptions, the output distribution of even the lowest depth version cannot be efficiently simulated on any classical device.