A learning agent-based approach to the characterization of open quantum systems
- URL: http://arxiv.org/abs/2501.05350v1
- Date: Thu, 09 Jan 2025 16:25:17 GMT
- Title: A learning agent-based approach to the characterization of open quantum systems
- Authors: Lorenzo Fioroni, Ivan Rojkov, Florentin Reiter,
- Abstract summary: We introduce the open Quantum Model Learning Agent (oQMLA) framework to account for Markovian noise through the Liouvillian formalism.
By simultaneously learning the Hamiltonian and jump operators, oQMLA independently captures both the coherent and incoherent dynamics of a system.
We validate our implementation in simulated scenarios of increasing complexity, demonstrating its robustness to hardware-induced measurement errors.
- Score: 0.0
- License:
- Abstract: Characterizing quantum processes is crucial for the execution of quantum algorithms on available quantum devices. A powerful framework for this purpose is the Quantum Model Learning Agent (QMLA) which characterizes a given system by learning its Hamiltonian via adaptive generations of informative experiments and their validation against simulated models. Identifying the incoherent noise of a quantum device in addition to its coherent interactions is, however, as essential. Precise knowledge of such imperfections of a quantum device allows to devise strategies to mitigate detrimental effects, for example via quantum error correction. We introduce the open Quantum Model Learning Agent (oQMLA) framework to account for Markovian noise through the Liouvillian formalism. By simultaneously learning the Hamiltonian and jump operators, oQMLA independently captures both the coherent and incoherent dynamics of a system. The added complexity of open systems necessitates advanced algorithmic strategies. Among these, we implement regularization to steer the algorithm towards plausible models and an unbiased metric to evaluate the quality of the results. We validate our implementation in simulated scenarios of increasing complexity, demonstrating its robustness to hardware-induced measurement errors and its ability to characterize systems using only local operations. Additionally, we develop a scheme to interface oQMLA with a publicly available superconducting quantum computer, showcasing its practical utility. These advancements represent a significant step toward improving the performance of quantum hardware and contribute to the broader goal of advancing quantum technologies and their applications.
Related papers
- Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection [3.9554540293311864]
This paper introduces a novel Bayesian approach using Quantum Bayesian Networks (QBNs) to classify imbalanced datasets.
We effectively address the challenge of integrating quantum enhancements with classical machine learning architectures.
Our study demonstrates significant advances in detecting and classifying anomalies, contributing to more effective and precise environmental monitoring and management.
arXiv Detail & Related papers (2024-12-24T15:44:26Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Exploring Quantum-Enhanced Machine Learning for Computer Vision: Applications and Insights on Noisy Intermediate-Scale Quantum Devices [0.0]
This study explores the intersection of quantum computing and Machine Learning (ML)
It evaluates the effectiveness of hybrid quantum-classical algorithms, such as the data re-uploading scheme and the patch Generative Adversarial Networks (GAN) model, on small-scale quantum devices.
arXiv Detail & Related papers (2024-04-01T20:55:03Z) - Quantum benefit of the quantum equation of motion for the strongly
coupled many-body problem [0.0]
The quantum equation of motion (qEOM) is a hybrid quantum-classical algorithm for computing excitation properties of a fermionic many-body system.
We demonstrate explicitly that the qEOM exhibits a quantum benefit due to the independence of the number of required quantum measurements.
arXiv Detail & Related papers (2023-09-18T22:10:26Z) - Explainable Quantum Machine Learning [0.7046417074932257]
Methods of artificial intelligence (AI) and especially machine learning (ML) have been growing ever more complex.
In parallel, quantum machine learning (QML) is emerging with the ongoing improvement of quantum computing hardware.
arXiv Detail & Related papers (2023-01-22T15:17:12Z) - Potential and limitations of quantum extreme learning machines [55.41644538483948]
We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements.
Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
arXiv Detail & Related papers (2022-10-03T09:32:28Z) - 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) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Efficient Quantum Simulation of Open Quantum System Dynamics on Noisy
Quantum Computers [0.0]
We show that quantum dissipative dynamics can be simulated efficiently across coherent-to-incoherent regimes.
This work provides a new direction for quantum advantage in the NISQ era.
arXiv Detail & Related papers (2021-06-24T10:37:37Z) - Entangling Quantum Generative Adversarial Networks [53.25397072813582]
We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN)
We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
arXiv Detail & Related papers (2021-04-30T20:38:41Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z)
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.