Reinforcement Learning Boosts Quantum Annealing Resilience to Environmental Noise.

Quantum systems, despite their potential for computational power, are inherently susceptible to environmental disturbances, a challenge that limits their practical application. Maintaining the delicate quantum states necessary for computation requires sophisticated strategies to mitigate the effects of noise. Recent research explores a novel approach utilising reinforcement learning, a computational technique where an agent learns to make decisions within an environment to maximise a reward, to develop dynamics that are resilient to these disturbances. Abolfazl Ramezanpour, from Shiraz University and Leiden University, and colleagues present findings in their article, ‘Noise tolerance via reinforcement: Learning a reinforced quantum dynamics’, detailing a method where a quantum annealing process is guided by reinforcement, encouraging the system to resist deviations caused by noise and thereby reducing the overall time spent vulnerable to errors. Their work demonstrates the efficacy of this approach using numerical simulations of one- and two-qubit systems exposed to Pauli noise, a common type of quantum error.

Quantum annealing, a metaheuristic for finding the global minimum of a given objective function, benefits considerably from strategies that lessen the impact of environmental noise. Current research focuses on utilising reinforcement learning to improve robustness against Pauli noise, a prevalent source of error in quantum systems. Pauli noise refers to errors arising from unintended rotations of qubits around the Pauli axes (X, Y, and Z), disrupting the quantum state. This approach bypasses the difficulties associated with real-time feedback control, which requires continuous monitoring and correction, and offers a new method for noise mitigation.

Reinforcing specific system dynamics, such as encouraging the preservation of the current quantum state or a noise-free evolution, delivers substantial performance gains. The core of this work involves employing a reinforcement learning algorithm to approximate these reinforced dynamics, crucially reducing the total computation time. Minimising this duration limits the system’s exposure to disruptive noise, thereby increasing the probability of identifying the optimal solution to the problem being solved.

Numerical simulations confirm the effectiveness of this method, concentrating on systems comprising one and two quantum bits, or qubits. The results demonstrate that the reinforced annealing process consistently surpasses standard algorithms when operating under noisy conditions, exhibiting a clear advantage in maintaining quantum coherence – the ability of a qubit to exist in a superposition of states – and achieving accurate results. These simulations quantify the improvement in solution quality and the reduction in error rates attained through the application of reinforcement learning.

Consequently, the system experiences reduced exposure to noisy interactions, directly contributing to improved computational fidelity. This method successfully addresses a significant challenge in quantum computing: maintaining coherence in the face of decoherence, the loss of quantum information due to interaction with the environment.