Autonomous Floquet Engineering of Bosonic Codes via Reinforcement Learning
- URL: http://arxiv.org/abs/2510.22227v1
- Date: Sat, 25 Oct 2025 09:14:55 GMT
- Title: Autonomous Floquet Engineering of Bosonic Codes via Reinforcement Learning
- Authors: Zheping Wu, Lingzhen Guo, Haobin Shi, Wei-Wei Zhang,
- Abstract summary: Bosonic codes represent a promising route toward quantum error correction in continuous-variable computation systems.<n>We introduce a reinforcement-learning-assisted Floquet engineering approach for the autonomous preparation of bosonic codes that is general, efficient, and noise-resilient.
- Score: 4.78580425445851
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Bosonic codes represent a promising route toward quantum error correction in continuous-variable systems, with direct relevance to experimental platforms such as circuit QED and optomechanics. However, their preparation and stabilization remain highly challenging, requiring ultra-precise control of nonlinear interactions to create entangled superpositions, suppress decoherence, and mitigate dynamic errors. Here, we introduce a reinforcement-learning-assisted Floquet engineering approach for the autonomous preparation of bosonic codes that is general, efficient, and noise-resilient. By leveraging machine learning to optimize Floquet driving parameters, our method achieves over two orders of magnitude reduction in evolution time-requiring only about one percent of that in conventional adiabatic schemes-while maintaining high-fidelity state generation even under strong dissipative and dephasing noise. This approach not only demonstrates the power of artificial intelligence in quantum control but also establishes a scalable and experimentally feasible route toward fault-tolerant bosonic quantum computation. Beyond the specific application to bosonic code preparation, our results suggest a general paradigm for integrating machine learning and Floquet engineering to overcome decoherence challenges in next-generation quantum technologies.
Related papers
- Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
arXiv Detail & Related papers (2026-01-10T02:36:03Z) - Reinforcement Learning Control of Quantum Error Correction [108.70420561323692]
Quantum computer learns to self-improve directly from its errors and never stops computing.<n>This work enables a new paradigm: a quantum computer that learns to self-improve directly from its errors and never stops computing.
arXiv Detail & Related papers (2025-11-11T17:32:25Z) - Resource-Efficient Hadamard Test Circuits for Nonlinear Dynamics on a Trapped-Ion Quantum Computer [1.2063443893298391]
We propose a low-depth implementation of a class of Hadamard test circuits.<n>We develop a parameterized quantum ansatz specifically tailored for variational algorithms.<n>Our findings demonstrate a significant reduction in single- and two-qubit gate counts.
arXiv Detail & Related papers (2025-07-25T13:16:54Z) - Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors [64.50565018996328]
We introduce the concept of predictive surrogates, designed to emulate the mean-value behavior of a given quantum processor with provably computational efficiency.<n>We use these surrogates to emulate a quantum processor with up to 20 programmable superconducting qubits, enabling efficient pre-training of variational quantum eigensolvers.<n> Experimental results reveal that the predictive surrogates not only reduce measurement overhead by orders of magnitude, but can also surpass the performance of conventional, quantum-resource-intensive approaches.
arXiv Detail & Related papers (2025-07-23T12:51:03Z) - 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) - A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
arXiv Detail & Related papers (2025-06-12T11:00:21Z) - VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - 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) - Non-Markovian Quantum Control via Model Maximum Likelihood Estimation
and Reinforcement Learning [0.0]
We propose a novel approach that incorporates the non-Markovian nature of the environment into a low-dimensional effective reservoir.
We utilize machine learning techniques to learn the effective quantum dynamics more efficiently than traditional tomographic methods.
This approach may not only mitigates the issues of model bias but also provides a more accurate representation of quantum dynamics.
arXiv Detail & Related papers (2024-02-07T18:37:17Z) - Simulating the Mott transition on a noisy digital quantum computer via
Cartan-based fast-forwarding circuits [62.73367618671969]
Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model.
Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models.
This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
arXiv Detail & Related papers (2021-12-10T17:32:15Z) - 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) - Experimental implementation of precisely tailored light-matter
interaction via inverse engineering [5.131683740032632]
shortcuts to adiabaticity, originally proposed to speed up slow adiabatic process, have nowadays become versatile toolboxes.
Here, we implement fast and robust control for the state preparation and state engineering in a rare-earth ions system.
We demonstrate that our protocols surpass the conventional adiabatic schemes, by reducing the decoherence from the excited state decay and inhomogeneous broadening.
arXiv Detail & Related papers (2021-01-29T08:17:01Z) - Unboxing Quantum Black Box Models: Learning Non-Markovian Dynamics [2.4201087215689947]
We design learning architectures that explicitly encode physical constraints like the properties of completely-positive trace-preserving maps in a differential form.
Our approach provides the physical interpretability that machine learning and opaque superoperators lack.
This paradigm paves the way to noise-aware optimal quantum control and opens a path to exploiting the bath as a control and error mitigation resource.
arXiv Detail & Related papers (2020-09-08T18:00:01Z) - Demonstration of non-Markovian process characterisation and control on a
quantum processor [0.0]
Non-Markovian noise poses a serious challenge to the progression of quantum technology.
We develop a framework for characterising non-Markovian dynamics in quantum systems.
Our results show this characterisation technique leads to superior quantum control and extension of coherence time.
arXiv Detail & Related papers (2020-04-29T08:29:29Z)
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.