Corpus ID: 235829296

Mixer-Phaser Ans\"atze for Quantum Optimization with Hard Constraints

@inproceedings{LaRose2021MixerPhaserAF,
  title={Mixer-Phaser Ans\"atze for Quantum Optimization with Hard Constraints},
  author={Ryan LaRose and Eleanor Gilbert Rieffel and Davide Venturelli},
  year={2021}
}
We introduce multiple parametrized circuit ansätze and present the results of a numerical study comparing their performance with a standard Quantum Alternating Operator Ansatz approach. The ansätze are inspired by mixing and phase separation in the QAOA, and also motivated by compilation considerations with the aim of running on near-term superconducting quantum processors. The methods are tested on random instances of a weighted quadratic binary constrained optimization problem that is fully… Expand
1 Citations

Figures from this paper

Filtering variational quantum algorithms for combinatorial optimization
Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, weExpand

References

SHOWING 1-10 OF 50 REFERENCES
XY mixers: Analytical and numerical results for the quantum alternating operator ansatz
The quantum alternating operator ansatz (QAOA) is a promising gate-model metaheuristic for combinatorial optimization. Applying the algorithm to problems with constraints presents an implementationExpand
Barren plateaus in quantum neural network training landscapes
TLDR
It is shown that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits. Expand
Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries
TLDR
This paper introduces an applicationspecific approach for mitigating the errors in QAOA evolution by leveraging the symmetries present in the classical objective function to be optimized, and improves the fidelity of theQAOA state. Expand
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
TLDR
The Quantum Alternating Operator Ansatz is introduced, design criteria for mixing operators are laid out, detail mappings for eight problems are provided, and brief descriptions of mappings are provided for diverse problems. Expand
Quantum Algorithms for Fixed Qubit Architectures
Gate model quantum computers with too many qubits to be simulated by available classical computers are about to arrive. We present a strategy for programming these devices without error correction orExpand
Optimal Protocols in Quantum Annealing and Quantum Approximate Optimization Algorithm Problems.
TLDR
Analytically applying the framework of optimal control theory is applied to show that generically, given a fixed amount of time, the optimal procedure has the pulsed structure of QAOA at the beginning and end but can have a smooth annealing structure in between. Expand
Optimizing QAOA: Success Probability and Runtime Dependence on Circuit Depth
The quantum approximate optimization algorithm~(QAOA) first proposed by Farhi et al. promises near-term applications based on its simplicity, universality, and provable optimality. A depth-p QAOAExpand
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
TLDR
The experimental optimization of Hamiltonian problems with up to six qubits and more than one hundred Pauli terms is demonstrated, determining the ground-state energy for molecules of increasing size, up to BeH2. Expand
Analytical Framework for Quantum Alternating Operator Ans\"atze
We develop a framework for analyzing layered quantum algorithms such as quantum alternating operator ansätze. In the context of combinatorial optimization, our framework relates quantum cost gradientExpand
Improving Variational Quantum Optimization using CVaR
TLDR
This paper empirically shows that the Conditional Value-at-Risk as an aggregation function leads to faster convergence to better solutions for all combinatorial optimization problems tested in this study. Expand
...
1
2
3
4
5
...