Noise tolerance via reinforcement: Learning a reinforced quantum dynamics

• URL: http://arxiv.org/abs/2506.12418v2
• Date: Wed, 09 Jul 2025 13:01:12 GMT
• Title: Noise tolerance via reinforcement: Learning a reinforced quantum dynamics
• Authors: Abolfazl Ramezanpour,
• Abstract summary: Reinforcement has emerged as a powerful strategy to enhance the efficiency of learning and optimization algorithms.<n>We show that a reinforced quantum dynamics can exhibit significant robustness against interactions with a noisy environment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of quantum simulations heavily depends on the efficiency of noise mitigation techniques and error correction algorithms. Reinforcement has emerged as a powerful strategy to enhance the efficiency of learning and optimization algorithms. In this study, we demonstrate that a reinforced quantum dynamics can exhibit significant robustness against interactions with a noisy environment. We study a quantum annealing process where, through reinforcement, the system is encouraged to maintain its current state or follow a noise-free evolution. A learning algorithm is employed to derive a concise approximation of this reinforced dynamics, reducing the total evolution time and, consequently, the system's exposure to noisy interactions. This also avoids the complexities associated with implementing quantum feedback in such reinforcement algorithms. The efficacy of our method is demonstrated through numerical simulations of reinforced quantum annealing with one- and two-qubit systems under Pauli noise.

Related papers

• Error mitigation of shot-to-shot fluctuations in analog quantum simulators [46.54051337735883]
  We introduce an error mitigation technique that addresses shot-to-shot fluctuations in the parameters for the Hamiltonian governing the system dynamics.<n>We rigorously prove that amplifying this shot-to-shot noise and extrapolating to the zero-noise limit recovers noiseless results for realistic noise distributions.<n> Numerically, we predict a significant enhancement in the effective many-body coherence time for Rydberg atom arrays under realistic conditions.
  arXiv  Detail & Related papers  (2025-06-19T18:00:00Z)
• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• Impact of Amplitude and Phase Damping Noise on Quantum Reinforcement Learning: Challenges and Opportunities [0.0]
  We investigate the effects of amplitude and phase damping noise on a quantum reinforcement learning algorithm.<n>Our findings contribute to a deeper understanding of the role of noise in quantum learning algorithms.
  arXiv  Detail & Related papers  (2025-03-31T13:27:30Z)
• Adaptive Genetic Algorithms for Pulse-Level Quantum Error Mitigation [0.0]
  Noise remains a fundamental challenge in quantum computing, significantly affecting pulse fidelity and overall circuit performance.<n>This paper introduces an adaptive algorithm for pulse-level quantum error mitigation, designed to enhance fidelity by dynamically responding to noise conditions without modifying circuit gates.
  arXiv  Detail & Related papers  (2025-01-23T15:28:22Z)
• Quantum noise modeling through Reinforcement Learning [38.47830254923108]
  We introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations.<n>Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models.<n>The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits.
  arXiv  Detail & Related papers  (2024-08-02T18:05:21Z)
• Layering and subpool exploration for adaptive Variational Quantum Eigensolvers: Reducing circuit depth, runtime, and susceptibility to noise [0.0]
  Adaptive variational quantum eigensolvers (ADAPT-VQEs) are promising candidates for simulations of strongly correlated systems. Recent efforts have been directed towards compactifying, or layering, their ansatz circuits. We show that layering leads to an improved noise resilience with respect to amplitude-damping and dephasing noise.
  arXiv  Detail & Related papers  (2023-08-22T18:00:02Z)
• Robustness of quantum reinforcement learning under hardware errors [0.0]
  Variational quantum machine learning algorithms have become the focus of recent research on how to utilize near-term quantum devices for machine learning tasks. They are considered suitable for this as the circuits that are run can be tailored to the device, and a big part of the computation is delegated to the classical. However, the effect of training quantum machine learning models under the influence of hardware-induced noise has not yet been extensively studied.
  arXiv  Detail & Related papers  (2022-12-19T13:14:22Z)
• Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks [68.8204255655161]
  We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
  arXiv  Detail & Related papers  (2022-06-29T14:34:00Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Process tomography of Robust Dynamical Decoupling in Superconducting Qubits [91.3755431537592]
  The Rigetti quantum computing platform was used to test different dynamical decoupling sequences. The performance of the sequences was characterized by Quantum Process Tomography and analyzed using the quantum channels formalism.
  arXiv  Detail & Related papers  (2020-06-18T14:48:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.