Paper: Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata

May 24th, 2020

Reinforcement Quantum Annealing:
A Hybrid Quantum Learning Automata


Ramin Ayanzadeh, Milton Halem, and Tim Finin, Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata, Nature Scientific Reports, v10, n1, May 2020

We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an intelligent agent searches in the space of Hamiltonians and interacts with a quantum annealer that plays the stochastic environment role of learning automata. At each iteration of RQA, after analyzing results (samples) from the previous iteration, the agent adjusts the penalty of unsatisfied constraints and re-casts the given problem to a new Ising Hamiltonian. As a proof-of-concept, we propose a novel approach for casting the problem of Boolean satisfiability (SAT) to Ising Hamiltonians and show how to apply the RQA for increasing the probability of finding the global optimum. Our experimental results on two different benchmark SAT problems (namely factoring pseudo-prime numbers and random SAT with phase transitions), using a D-Wave 2000Q quantum processor, demonstrated that RQA finds notably better solutions with fewer samples, compared to the best-known techniques in the realm of quantum annealing.

See also:


Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach

January 3rd, 2020

Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach

Ramin Ayanzadeh, Milton Halem, and Tim Finin, Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach, arXiv:2001.00234 [quant-ph], January 1, 2020.

We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we propose a novel approach for reducing the NP-complete problem of Boolean satisfiability (SAT) to minimizing Ising Hamiltonians and show how to apply the RQA for increasing the probability of finding the global optimum. Our experimental results on two different benchmark SAT problems (namely factoring pseudo-prime numbers and random SAT with phase transitions), using a D-Wave 2000Q quantum processor, demonstrated that RQA finds notably better solutions with fewer samples, compared to state-of-the-art techniques in the realm of quantum annealing.