# Enhancing the efficiency of quantum annealing via reinforcement: A path-integral Monte Carlo simulation of the quantum reinforcement algorithm

@article{Ramezanpour2018EnhancingTE, title={Enhancing the efficiency of quantum annealing via reinforcement: A path-integral Monte Carlo simulation of the quantum reinforcement algorithm}, author={Abolfazl Ramezanpour}, journal={ArXiv}, year={2018}, volume={abs/1812.02569} }

The standard quantum annealing algorithm tries to approach the ground state of a classical system by slowly decreasing the hopping rates of a quantum random walk in the configuration space of the problem, where the on-site energies are provided by the classical energy function. In a quantum reinforcement algorithm, the annealing works instead by increasing gradually the strength of the on-site energies according to the probability of finding the walker on each site of the configuration space…

## 5 Citations

Quantum walk in a reinforced free-energy landscape: Quantum annealing with reinforcement

- Computer Science
- 2022

This study takes a local entropy in the configuration space for the reinforcement and applies the algorithm to a number of easy and hard optimization problems and finds the reinforced algorithm performs better than the standard quantum annealing algorithm in the quantum search problem where the optimal parameters behave very differently depending on the number of solutions.

Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata

- Computer ScienceScientific Reports
- 2020

Experimental results on two different benchmark SAT problems demonstrated that RQA finds notably better solutions with fewer samples, compared to the best-known techniques in the realm of quantum annealing.

Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach

- Computer ScienceArXiv
- 2020

A novel approach for reducing the NP-complete problem of Boolean satisfiability (SAT) to minimizing Ising Hamiltonians and how to apply the RQA for increasing the probability of finding the global optimum is proposed.

Leveraging Artificial Intelligence to Advance Problem-Solving with Quantum Annealers

- Computer Science
- 2020

SAT++ is introduced, as a novel quantum programming paradigm, that can compile classical algorithms and execute them on quantum annealers and a post-quantum error correction method is introduced that can find samples with significantly lower energy values, compared to the state-of-the-art techniques inquantum annealing.

Advanced unembedding techniques for quantum annealers

- Computer Science2020 International Conference on Rebooting Computing (ICRC)
- 2020

This work presents tailored unembedding techniques for four important NP-hard problems: the Maximum Clique, Maximum Cut, Minimum Vertex Cover, and Graph Partitioning problems, and demonstrates that the proposed algorithms outperform the currently available ones in that they yield solutions of better quality, while being computationally equally efficient.

## References

SHOWING 1-10 OF 53 REFERENCES

Quantum annealing by the path-integral Monte Carlo method: The two-dimensional random Ising model

- Physics
- 2002

Quantum annealing was recently found experimentally in a disordered spin- magnet to be more effective than its classical, thermal counterpart. We use the random two-dimensional Ising model as a test…

Optimization by a quantum reinforcement algorithm

- Physics, Computer ScienceArXiv
- 2017

The numerical simulations and the observation that reinforcement increases the minimal energy gap of the system in a quantum annealing algorithm show that such kind of quantum feedbacks might be helpful in solving a computationally hard optimization problem by a quantum reinforcement algorithm.

A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem

- Physics, Computer ScienceScience
- 2001

For the small examples that the authors could simulate, the quantum adiabatic algorithm worked well, providing evidence that quantum computers (if large ones can be built) may be able to outperform ordinary computers on hard sets of instances of NP-complete problems.

Path-integral representation for quantum spin models: Application to the quantum cavity method and Monte Carlo simulations

- Physics
- 2008

The cavity method is a well established technique for solving classical spin models on sparse random graphs (mean-field models with finite connectivity). Laumann et al. [arXiv:0706.4391] proposed…

The Quantum Adiabatic Algorithm applied to random optimization problems: the quantum spin glass perspective

- PhysicsArXiv
- 2012

Anderson localization makes adiabatic quantum optimization fail

- Physics, Computer ScienceProceedings of the National Academy of Sciences
- 2010

It turns out that due to a phenomenon similar to Anderson localization, exponentially small gaps appear close to the end of the adiabatic algorithm for large random instances of NP-complete problems, which implies that unfortunately, adiABatic quantum optimization fails: the system gets trapped in one of the numerous local minima.

Experimental signature of programmable quantum annealing.

- PhysicsNature communications
- 2013

This experiment uses groups of eight superconducting flux qubits with programmable spin-spin couplings, embedded on a commercially available chip with >100 functional qubits, and suggests that programmable quantum devices, scalable with currentsuperconducting technology, implement quantum annealing with a surprising robustness against noise and imperfections.

First-order transitions and the performance of quantum algorithms in random optimization problems

- PhysicsPhysical review letters
- 2010

The characterization of the nature of the phase transition is found to be a first-order quantum phase transition, which indicates that the quantum adiabatic algorithm requires a time growing exponentially with system size to find the ground state of this problem.

Quantum annealing with manufactured spins

- PhysicsNature
- 2011

This programmable artificial spin network bridges the gap between the theoretical study of ideal isolated spin networks and the experimental investigation of bulk magnetic samples, and may provide a practical physical means to implement a quantum algorithm, possibly allowing more-effective approaches to solving certain classes of hard combinatorial optimization problems.

Exponential Complexity of the Quantum Adiabatic Algorithm for certain Satisfiability Problems

- Physics, Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2011

We determine the complexity of several constraint satisfaction problems using the quantum adiabatic algorithm in its simplest implementation. We do so by studying the size dependence of the gap to…