Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Frontiers in Optics

Experimental demonstration of a quantum model learning agent

Antonio A. Gentile, Brian Flynn, Sebastian Knauer, Nathan Wiebe, Stefano Paesani, Christopher Granade, John Rarity, Raffaele Santagati, Anthony Laing

Published Jan 1, 2020• 0 citations
Publisher

Abstract

Developing novel quantum devices poses the problem of their efficient characterization. We introduce and experimentally demonstrate a methodology to automatically formulate and rank Hamiltonian models, learning the most appropriate in reproducing the observed system’s dynamics.
DOI
10.1364/fio.2020.ftu8d.2

Related papers

Machine learning methods for quantum mechanics

Scott Lawerence, Yukari Yamauchi, Hyunwoo Oh +1 more

Exploratory implementations of possible algorithms for simulating small quantum mechanical systems on a classical computer.

•Jan 1, 2024•0 citations

Learning-agent-based approach to the characterization of open quantum systems

Lorenzo Fioroni, Ivan Rojkov, Florentin Reiter

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.

arXiv:2501.05350•Physical Review Applied•Jun 18, 2025•2 citations

Learning to maximize quantum neural network expressivity via effective rank

Juan Yao

Abstract Quantum neural networks (QNNs) are widely used as trainable models for solving variational problems, where their ability to represent complex functions directly determines performance. However, accurately quantifying this expressivity remains a major challenge, limiting the understanding of the expressive power of QNNs. Here, we introduce the effective rank κ as a quantitative measure of expressivity. We show that the expressivity of a QNN is determined by the interplay of three factors: the dataset, the measurement operators, and the circuit structure. Unlike conventional metrics based on parameter sampling or entanglement structure, κ captures the number of independent parameters that effectively contribute to the model output, providing an operational and data-dependent characterization of expressivity while avoiding costly parameter sampling. We demonstrate that κ can reach its theoretical maximum 4 n − 1 for an n -qubit system when the circuit architecture, input distribution, and measurement protocol are jointly optimized. Leveraging this insight, we employ κ as a design objective within a reinforcement learning framework with a self-attention transformer agent to automatically discover highly expressive circuit architectures. Our results show that the proposed approach efficiently identifies high-performing circuit configurations, outperforming heuristic designs and random search in terms of sample efficiency. By bridging theoretical characterization with automated design, our approach provides a practical, scalable, and computationally efficient route for constructing expressive quantum circuits, advancing the development of quantum machine learning models.

arXiv:2506.15375•Quantum Science and Technology•May 21, 2026•0 citations

Learning models of quantum systems from experiments

Antonio A. Gentile, Brian Flynn, Sebastian Knauer +6 more

arXiv:2002.06169•Nature Physics•Apr 29, 2021•62 citations

Quantum model learning agent: characterisation of quantum systems through machine learning

Brian Flynn, Antonio A. Gentile, Nathan Wiebe +2 more

Abstract Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ⩾ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.

arXiv:2112.08409•New Journal of Physics•Apr 21, 2022•11 citations

Experimental quantum Hamiltonian learning

Jianwei Wang, Stefano Paesani, Raffaele Santagati +8 more

arXiv:1703.05402•Nature Physics•Mar 13, 2017•248 citations
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.
Abstract Quantum neural networks (QNNs) are widely used as trainable models for solving variational problems, where their ability to represent complex functions directly determines performance. However, accurately quantifying this expressivity remains a major challenge, limiting the understanding of the expressive power of QNNs. Here, we introduce the effective rank κ as a quantitative measure of expressivity. We show that the expressivity of a QNN is determined by the interplay of three factors: the dataset, the measurement operators, and the circuit structure. Unlike conventional metrics based on parameter sampling or entanglement structure, κ captures the number of independent parameters that effectively contribute to the model output, providing an operational and data-dependent characterization of expressivity while avoiding costly parameter sampling. We demonstrate that κ can reach its theoretical maximum 4 n − 1 for an n -qubit system when the circuit architecture, input distribution, and measurement protocol are jointly optimized. Leveraging this insight, we employ κ as a design objective within a reinforcement learning framework with a self-attention transformer agent to automatically discover highly expressive circuit architectures. Our results show that the proposed approach efficiently identifies high-performing circuit configurations, outperforming heuristic designs and random search in terms of sample efficiency. By bridging theoretical characterization with automated design, our approach provides a practical, scalable, and computationally efficient route for constructing expressive quantum circuits, advancing the development of quantum machine learning models.
Abstract Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ⩾ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.