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

Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)

Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond

Stefano Paesani, Jianwei Wang, Raffaele Santagati, Sebastian Knauer, Andrea A. Gentile, Nathan Wiebe, Maurangelo Petruzzella, Anthony Laing, John Rarity, Jeremy L. O’Brien, Mark G. Thompson

Published Jun 1, 2017• 4 citations
Publisher

Abstract

Summary form only given. The efficient characterization and validation of the underlying model of a quantum physical system is a central challenge in the development of quantum devices and for our understanding of foundational quantum physics. However, the impossibility to efficiently predict the behaviour of complex quantum models on classical machines makes this challenge to be intractable to classical approaches. Quantum Hamiltonian Learning (QHL) [1, 2] combines the capabilities of quantum information processing and classical machine learning to allow the efficient characterisation of the model of quantum systems. In QHL the behaviour of a quantum Hamiltonian model is efficiently predicted by a quantum simulator, and the predictions are contrasted with the data obtained from the quantum system to infer the system Hamiltonian via Bayesian methods.

DOI
10.1109/cleoe-eqec.2017.8087392

Related papers

Learning nitrogen-vacancy electron spin dynamics on a silicon quantum photonic simulator

Jianwei Wang, Stefano Paesani, Raffaele Santagati +8 more

We present the experimental demonstration of quantum Hamiltonian learning. Using an integrated silicon-photonics quantum simulator with the classical machine learning technique, we successfully learn the Hamiltonian dynamics of a diamond nitrogen-vacancy center’s electron ground-state spin.

Conference on Lasers and Electro-Optics•Jan 1, 2017•0 citations

Experimental Hamiltonian Learning of an 11-Qubit Solid-State Quantum Spin Register*

Pan‐Yu Hou, L. He, F. Wang +7 more

Learning the Hamiltonian of a quantum system is indispensable for prediction of the system dynamics and realization of high fidelity quantum gates. However, it is a significant challenge to efficiently characterize the Hamiltonian which has a Hilbert space dimension exponentially growing with the system size. Here, we develop and implement an adaptive method to learn the effective Hamiltonian of an 11-qubit quantum system consisting of one electron spin and ten nuclear spins associated with a single nitrogen-vacancy center in a diamond. We validate the estimated Hamiltonian by designing universal quantum gates based on the learnt Hamiltonian and implementing these gates in the experiment. Our experimental result demonstrates a well-characterized 11-qubit quantum spin register with the ability to test quantum algorithms, and shows our Hamiltonian learning method as a useful tool for characterizing the Hamiltonian of the nodes in a quantum network with solid-state spin qubits.

arXiv:1905.12248•Chinese Physics Letters•Oct 1, 2019•15 citations

Towards practical characterization of quantum systems with quantum Hamiltonian learning

Raffaele Santagati, Jianwei Wang, Stefano Paesani +8 more

Here we show the first experimental implementation of quantum Hamiltonian Learning, where a silicon-on-insulator quantum photonic simulator is used to learn the dynamics of an electron-spin in an NV center in diamond.

Frontiers in Optics•Jan 1, 2017•0 citations

Schrödinger's Catwalk

Brian Flynn

Quantum technologies exploit quantum mechanical processes to achieve outcomes beyond the reach of classical machinery.One of their most promising applications is quantum simulation, whereby particles, atoms and molecules can be examined thoroughly for the first time, having been beyond the scope of even the most powerful supercomputers.Models have been useful tools in understanding physical systems: these are mathematical structures encoding physical interactions, which allow us to predict how the system will behave under various conditions.Models of quantum systems are particularly difficult to design and test, owing to the huge computational resources required to represent them accurately.In this thesis, we introduce and develop an algorithm to characterise quantum systems efficiently, by inferring a model consistent with their observed dynamics.The Quantum Model Learning Agent (QMLA) is an extensible framework which permits the study of any quantum system of interest, by combining quantum simulation with state of the art machine learning.QMLA iteratively proposes candidate models and trains them against the target system, finally declaring a single model as the best representation for the system of interest.We describe QMLA and its implementation through open source software, before testing it under a series of physical scenarios.First, we consider idealised theoretical systems in simulation, verifying the core principles of QMLA.Next, we incorporate strategies for generating candidate models by exploiting the information QMLA has gathered to date; by incorporating a genetic algorithm within QMLA, we explore vast spaces of valid candidate models, with QMLA reliably identifying the precise target model.Finally, we apply QMLA to realistic quantum systems, including operating on experimental data measured from an electron spin in a nitrogen vacancy centre.QMLA is shown to be effective in all cases studied in this thesis; however, of greater interest is the platform it provides for examining quantum systems.QMLA can aid engineers in configuring experimental setups, facilitate calibration of near term quantum devices, and ultimately enable complete characterisation of natural quantum structures.This thesis marks the beginning of a new line of research, into automating the understanding of quantum mechanical systems.

•Jun 1, 2021•0 citations

Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction

Zheng An, J. Wu, Zidong Lin +3 more

Recent advancements in quantum hardware and classical computing simulations have significantly enhanced the accessibility of quantum system data, leading to an increased demand for precise descriptions and predictions of these systems. Accurate prediction of quantum Hamiltonian dynamics and identification of Hamiltonian parameters are crucial for advancements in quantum simulations, error correction, and control protocols. This Letter introduces a machine learning model with dual capabilities: it can deduce time-dependent Hamiltonian parameters from observed changes in local observables within quantum many-body systems, and it can predict the evolution of these observables based on Hamiltonian parameters. Our model's validity was confirmed through theoretical simulations across various scenarios and further validated by two experiments. Initially, the model was applied to a nuclear magnetic resonance quantum computer, where it accurately predicted the dynamics of local observables. The model was then tested on a superconducting quantum computer with initially unknown Hamiltonian parameters, successfully inferring them. Our approach aims to enhance various quantum computing tasks, including parameter estimation, noise characterization, feedback processes, and quantum control optimization.

arXiv:2405.13582•Physical Review Letters•Mar 27, 2025•5 citations

Quantum and classical processing with photonic quantum machine learning

J. C. López Carreño, S. Świerczewski, A. Opala +3 more

Artificial intelligence and machine learning have been widely adopted both in the industry and in everyday life, but at the cost of high compute demands. Recent studies show that implementing machine learning in physical systems in the deep quantum regime could not only lead to faster information processing, but also to perform tasks that are out of reach for classical systems. Here, we report a quantum reservoir processing device capable of performing both quantum and classical machine learning tasks. The implementation is realized with a programmable silicon chip excited with single photons, a highly scalable and adaptable photonics technology. We successfully implement a variety of quantum tasks, including quantum state tomography and measurement of entanglement via negativity. Moreover, we implement a method of mitigation of experimental imperfections which results in a significant improvement in accuracy in comparison to the same system operating in the classical regime. Our results demonstrate a method to overcome a crucial bottleneck of quantum technologies by providing a practical way of probing quantum states.

quant-pharXiv:2605.10471•May 12, 2026•0 citations
Summary form only given. The efficient characterization and validation of the underlying model of a quantum physical system is a central challenge in the development of quantum devices and for our understanding of foundational quantum physics. However, the impossibility to efficiently predict the behaviour of complex quantum models on classical machines makes this challenge to be intractable to classical approaches. Quantum Hamiltonian Learning (QHL) [1, 2] combines the capabilities of quantum information processing and classical machine learning to allow the efficient characterisation of the model of quantum systems. In QHL the behaviour of a quantum Hamiltonian model is efficiently predicted by a quantum simulator, and the predictions are contrasted with the data obtained from the quantum system to infer the system Hamiltonian via Bayesian methods.
Learning the Hamiltonian of a quantum system is indispensable for prediction of the system dynamics and realization of high fidelity quantum gates. However, it is a significant challenge to efficiently characterize the Hamiltonian which has a Hilbert space dimension exponentially growing with the system size. Here, we develop and implement an adaptive method to learn the effective Hamiltonian of an 11-qubit quantum system consisting of one electron spin and ten nuclear spins associated with a single nitrogen-vacancy center in a diamond. We validate the estimated Hamiltonian by designing universal quantum gates based on the learnt Hamiltonian and implementing these gates in the experiment. Our experimental result demonstrates a well-characterized 11-qubit quantum spin register with the ability to test quantum algorithms, and shows our Hamiltonian learning method as a useful tool for characterizing the Hamiltonian of the nodes in a quantum network with solid-state spin qubits.
Quantum technologies exploit quantum mechanical processes to achieve outcomes beyond the reach of classical machinery.One of their most promising applications is quantum simulation, whereby particles, atoms and molecules can be examined thoroughly for the first time, having been beyond the scope of even the most powerful supercomputers.Models have been useful tools in understanding physical systems: these are mathematical structures encoding physical interactions, which allow us to predict how the system will behave under various conditions.Models of quantum systems are particularly difficult to design and test, owing to the huge computational resources required to represent them accurately.In this thesis, we introduce and develop an algorithm to characterise quantum systems efficiently, by inferring a model consistent with their observed dynamics.The Quantum Model Learning Agent (QMLA) is an extensible framework which permits the study of any quantum system of interest, by combining quantum simulation with state of the art machine learning.QMLA iteratively proposes candidate models and trains them against the target system, finally declaring a single model as the best representation for the system of interest.We describe QMLA and its implementation through open source software, before testing it under a series of physical scenarios.First, we consider idealised theoretical systems in simulation, verifying the core principles of QMLA.Next, we incorporate strategies for generating candidate models by exploiting the information QMLA has gathered to date; by incorporating a genetic algorithm within QMLA, we explore vast spaces of valid candidate models, with QMLA reliably identifying the precise target model.Finally, we apply QMLA to realistic quantum systems, including operating on experimental data measured from an electron spin in a nitrogen vacancy centre.QMLA is shown to be effective in all cases studied in this thesis; however, of greater interest is the platform it provides for examining quantum systems.QMLA can aid engineers in configuring experimental setups, facilitate calibration of near term quantum devices, and ultimately enable complete characterisation of natural quantum structures.This thesis marks the beginning of a new line of research, into automating the understanding of quantum mechanical systems.
Recent advancements in quantum hardware and classical computing simulations have significantly enhanced the accessibility of quantum system data, leading to an increased demand for precise descriptions and predictions of these systems. Accurate prediction of quantum Hamiltonian dynamics and identification of Hamiltonian parameters are crucial for advancements in quantum simulations, error correction, and control protocols. This Letter introduces a machine learning model with dual capabilities: it can deduce time-dependent Hamiltonian parameters from observed changes in local observables within quantum many-body systems, and it can predict the evolution of these observables based on Hamiltonian parameters. Our model's validity was confirmed through theoretical simulations across various scenarios and further validated by two experiments. Initially, the model was applied to a nuclear magnetic resonance quantum computer, where it accurately predicted the dynamics of local observables. The model was then tested on a superconducting quantum computer with initially unknown Hamiltonian parameters, successfully inferring them. Our approach aims to enhance various quantum computing tasks, including parameter estimation, noise characterization, feedback processes, and quantum control optimization.
Artificial intelligence and machine learning have been widely adopted both in the industry and in everyday life, but at the cost of high compute demands. Recent studies show that implementing machine learning in physical systems in the deep quantum regime could not only lead to faster information processing, but also to perform tasks that are out of reach for classical systems. Here, we report a quantum reservoir processing device capable of performing both quantum and classical machine learning tasks. The implementation is realized with a programmable silicon chip excited with single photons, a highly scalable and adaptable photonics technology. We successfully implement a variety of quantum tasks, including quantum state tomography and measurement of entanglement via negativity. Moreover, we implement a method of mitigation of experimental imperfections which results in a significant improvement in accuracy in comparison to the same system operating in the classical regime. Our results demonstrate a method to overcome a crucial bottleneck of quantum technologies by providing a practical way of probing quantum states.