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Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata
Ramin Ayanzadeh1, Milton Halem2, Tim Finin2
1Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, 21250, United States. ayanzadeh@umbc.edu.
Scientific Reports
|May 16, 2020
Summary
We developed reinforcement quantum annealing (RQA), a new method where an intelligent agent optimizes problems for quantum annealers. RQA improves finding optimal solutions for complex problems like Boolean satisfiability using fewer samples.
Area of Science:
- Quantum Computing
- Artificial Intelligence
- Computational Complexity
Background:
- Quantum annealing is a metaheuristic optimization technique that leverages quantum mechanics to find the global optimum of a given objective function.
- Solving complex problems like Boolean satisfiability (SAT) remains a significant challenge in computer science, often requiring substantial computational resources.
- Current quantum annealing approaches may require extensive sampling to achieve optimal solutions.
Purpose of the Study:
- To introduce a novel Reinforcement Quantum Annealing (RQA) scheme that enhances problem-solving capabilities of quantum annealers.
- To develop a new method for casting Boolean satisfiability (SAT) problems into Ising Hamiltonians suitable for quantum annealing.
- To demonstrate the efficacy of RQA in improving the probability of finding global optima with fewer samples.
Main Methods:
- An intelligent agent interacts with a quantum annealer, adjusting problem Hamiltonians based on previous results.
- The RQA scheme involves an agent iteratively refining the penalty for unsatisfied constraints and reformulating the problem as an Ising Hamiltonian.
- A proof-of-concept approach for mapping SAT problems to Ising Hamiltonians was developed and tested.
Main Results:
- Experimental results on benchmark SAT problems using a D-Wave 2000Q quantum processor showed RQA's effectiveness.
- RQA consistently found better solutions compared to existing quantum annealing techniques.
- The RQA scheme required significantly fewer samples to achieve high-quality solutions.
Conclusions:
- Reinforcement Quantum Annealing (RQA) offers a promising advancement for optimizing complex problems on quantum hardware.
- The proposed SAT-to-Ising Hamiltonian casting method combined with RQA enhances solution quality and efficiency.
- RQA represents a significant step towards more effective utilization of quantum annealers for computational challenges.
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