II Machine Learning Methods for Complex and Quantum Systems

Contribution List

68. ML Training Session for Early Career Researchers

Timothy Heightman (ICFO)

10/06/2026, 09:30

69. ML Training Session for Early Career Researchers

Timothy Heightman (ICFO)

10/06/2026, 11:30

49. Welcome

10/06/2026, 14:30

50. Interpretable deep learning and quantum machine learning for medicine

Prof. Miguel Ángel González Ballester (Universitat Pompeu Fabra & ICREA)

10/06/2026, 14:45

Deep learning has had a profound effect in all walks of life, not least in medical research. The impressive performance of these methods is partly due to the development of very efficient approaches to solve highly overparameterized neural networks. Conversely, this leads to the problem of trust and interpretability of these complex models, which are key aspects in the medical domain. In this...

48. Quantum Scrambling as a resource for quantum generative models

Marcin Płodzień (Qilimanjaro Quantum Tech)

10/06/2026, 15:30

Invited Talk

51. Networks and AI to explore materials space

Prof. Guido Caldarelli (CNR-ISC)

10/06/2026, 16:30

53. Learning to simulate quantum circuits from samples

Prof. Ryan LaRose (Michigan State University)

10/06/2026, 17:00

28. Predicting Quantum Many-Body Dynamics with Classical and Quantum Attention Models

Matteo Rosati (Università Roma Tre)

10/06/2026, 17:30

Contributed Talk

Predicting the real-time dynamics of quantum many-body systems is a central challenge in quantum physics, where the exponential growth of Hilbert space makes direct simulation rapidly intractable. In this contribution, we explore attention-based models as a framework for learning and extrapolating quantum dynamics.

Attention mechanisms are the core building blocks underlying modern large...

54. Quantum Computing with Words

Prof. Giovanni Acampora (University of Naples Federico II)

11/06/2026, 09:30

55. Learning dynamics and Fast Sampling for Energy-Based Models

Prof. Aurélien Decelle (Universidad Politécnica de MadridU)

11/06/2026, 10:00

56. Learnability of noise in quantum devices

Dr Cristina Cirstoiu (Quantinuum)

11/06/2026, 10:30

10. Predicting Phonon Thermal Transport in 2D Materials with Machine-Learning Interatomic Potentials

Cem Sevik (Department of Physics, University of Antwerp, Belgium)

11/06/2026, 11:30

Invited Talk

Group-VI transition metal dichalcogenides, such as MoS$_2$ and MoSe$_2$, are prototypical two-dimensional materials with distinctive phononic and electronic properties, making them highly attractive for nanoelectronic, optoelectronic, and thermoelectric applications. However, their reported lattice thermal conductivities remain highly inconsistent, with experimental measurements and...

57. Interpretable machine learning for quantum simulators

Anna Dawid (Leiden University)

11/06/2026, 12:00

Invited Talk

58. Neural quantum states through the lens of modern machine learning

Dr Agnes Valenti (Flatiron Institute)

11/06/2026, 12:30

59. Probabilistic learning of closed-form mathematical models from data

Prof. Roger Guimerà (ICREA & Universitat Rovira i Virgili)

11/06/2026, 14:30

64. What geometry are graph neural networks learning?

Prof. Marián Boguñá (University of Barcelona & ICREA)

11/06/2026, 15:00

70. Neural Quantum Kernels for classification

Camila Roxana Cristiano Romero (Basque Center for Applied Mathematics)

11/06/2026, 15:30

23. Nuclear-physics-guided Gaussian Processes

Hristijan Kochankovski (Departament de Fisica Quantica i Astrofisica and Institut de Ciencies del Cosmos, Universitat de Barcelona, Marti i Franques 1, 08028, Barcelona, Spain)

11/06/2026, 16:00

Contributed Talk

Gaussian process (GP) regression is a powerful nonparametric Bayesian method that provides both predictions and principled uncertainty estimates in closed form. While most applications rely on generic priors, the flexibility of the GP framework allows physics knowledge to be systematically encoded into the mean function and the kernel. We exploit this capability in the context of nuclear...

61. Neural Differential Equations in Quantum Process Tomography

Timothy Heightman (ICFO)

12/06/2026, 09:30

62. Modeling complex systems with energy-based models

Prof. Beatriz Seoane (Universidad Complutense de Madrid)

12/06/2026, 10:00

63. Variational learning strongly interacting systems

Alessandro Lovato (Argonne National Laboratory & IFIC-CSIC)

12/06/2026, 10:30

60. Learning the quantum many-body problem: From quantum materials to quantum simulators

Dr Ivan Morera (ETH Zurich)

12/06/2026, 11:30

65. A decisional step for Variational Monte Carlo: Optimizing Neural Quantum States with Decision Geometry

Mehdi Drissi (TU Darmstadt, Theory Center)

12/06/2026, 12:00

66. Applications of Foundation Neural Quantum States in Many-Body Quantum Systems

Alessandro Santini (CPhT)

12/06/2026, 12:30

67. Efficient estimation of the Partition Function of Restricted Boltzmann Machines via Annealed Importance Sampling

Ferran Mazzanti (Universitat Politècnica de Catalunya)

12/06/2026, 14:30

18. Simulating nuclear physics using gadget Ising hamiltonians

Emanuele Costa (University of Barcelona)

12/06/2026, 15:00

Invited Talk

We present a perturbative gadget construction that maps quasiparticle nuclear shell model (NSM) Hamiltonians onto effective Ising dynamics. Starting from a one-hot encoded quasiparticle basis, we derive an effective low-energy Hamiltonian via Brillouin-Wigner perturbation theory, recovering the target nuclear interactions as emergent couplings of a perturbed Ising model. We discuss how this...

35. A machine learning tool to analyze spectroscopic changes in high-dimensional data

Giancarlo Franzese (Universitat de Barcelona)

12/06/2026, 15:30

Contributed Talk

When analyzing spectroscopic data of complex systems, a key challenge is identifying the features that most effectively distinguish their differences, thereby facilitating the study of physical properties and their evolution. A common strategy involves combining multiple measurements to acquire more detailed information; however, this approach does not always enhance analysis quality,...

22. AI-assisted characterization and tuning of quantum dot-based quantum simulators

Jarosław Pawłowski (Wroclaw University of Science and Technology)

12/06/2026, 16:30

Contributed Talk

Quantum simulators based on semiconductor quantum dots (QDs) [1] are promising candidates for practical quantum technologies in the present NISQ (Noisy Intermediate-Scale Quantum) era. However, a fundamental challenge is the characterization and control of already fabricated devices. This task becomes increasingly complex in arrays of many interacting QDs, where the number of tunable...

40. Dissipation–Performance Interplay in Quantum Reservoir Computing

Christopher Gies (Carl von Ossietzky Universität Oldenburg)

12/06/2026, 17:00

Invited Talk

Quantum Reservoir Computing (QRC) has emerged as a powerful paradigm for dynamical quantum machine learning that is particularly suited for the noisy intermediate-scale quantum (NISQ) era where gate fidelities remain a limiting factor. As recent experimental and theoretical advances have demonstrated that QRC can leverage the intrinsic dynamics of quantum systems to process temporal...

4. Entanglement-assisted Hamiltonian dynamics learning

Ayaka Usui (Universitat Autònoma de Barcelona)

12/06/2026, 17:30

Contributed Talk

Approximating the dynamics given by a complex many-body Hamiltonian with a simpler effective model lies at the interface of quantum Hamiltonian learning and quantum simulation. In this context, quantum generative adversarial networks (QGANs) have been shown to outperform standard Trotter-based approximations. However, their performance is often hindered by training plateaus and local minima...

32. A Convolutional Neural Network approach to Neural-Shadow Quantum State Tomography

José Dardón (Universitat Autònoma de Barcelona)

Poster

Neural-Shadow Quantum State Tomography (NSQST) [Wei24] combines classical-shadow measurements [HKP20] with Neural Quantum States (NQS) [CT17] in order to reconstruct unknown quantum states from classical-shadow data. The learned state is represented...

1. A variational approach to quantum field theory

Poster

In strongly coupled field theories, perturbation theory cannot be employed to study the low-energy spectrum. Thus, non-perturbative techniques are required. One possibility is the Lagrangian approach, where energies are extracted from the Euclidean-time dependence of correlation functions. This method suffers from excited-state contamination at shorter times and rapidly growing statistical...

43. Adding equivariance to Variational Quantum Eigensolvers

Poster

Variational Quantum Eigensolvers (VQEs) have risen as a leading paradigm for solving the many-body problem, but their success has been hindered by emerging results on trainability and noise issues. By exploiting symmetries, these issues may be mitigated, and generic frameworks have been proposed to incorporate them by means of equivariant layers [1, 2]. These strategies have been formalized in...

19. AI Enabled Formative Assessment in Higher Education through a Machine Learning Assisted PRISMA-ScR Scoping Review

Poster

AI in Education has evolved from earlier computer based systems to data driven, web based, and, more recently, generative AI environments, expanding the possibilities for formative assessment while also raising questions about transparency, ethics, and educational quality [1,2,3]. Following a PRISMA-ScR scoping review approach [4], this ongoing study examines the emerging landscape of AI...

3. Basis dependence of Neural Quantum States for the Transverse Field Ising Model

Poster

Neural Quantum States (NQS) are powerful tools used to represent complex quantum
many-body states in an increasingly wide range of applications. However, despite their
popularity, at present only a rudimentary understanding of their limitations exists. In
this work, we investigate the dependence of NQS on the choice of the computational ba-
sis, focusing on restricted Boltzmann machines....

34. Bayesian model averaging for nuclear symmetry energy from effective proton-neutron chemical potential difference of neutron-rich nuclei

Poster

The data-driven Bayesian model averaging is a rigorous statistical approach to combining multiple models for a unified prediction. Compared with the individual model, it provides more reliable information, especially for problems involving apparent model dependence. In this work, we employed a Bayesian model averaging analysis based on Gaussian process emulators to extract the symmetry energy...

39. Classical acceleration of Variational Quantum Eigensolvers from symmetry constrains and backprogagation

Poster

Classical simulation of quantum circuits is a computationally expensive task. However, physics inspired algorithms are often based in properties of the system, such as their symmetries. For the case of the Variational Quantum Eigensolvers, when the expressivity of the Ansatz is reduced to satisfy symmetry constrains, this reduces the space that the circuit can explore.

This constrains, in...

44. Comparing Neural Network Architectures for Modeling Spectral Diffusion in NV Nanopillars

Poster

Spectral diffusion is a key limitation for the integration of solid-state quantum emitters into nanophotonic platforms. In diamond nanopillars, nitrogen-vacancy (NV) centers exhibit enhanced spectral instability due to surface-induced electric-field fluctuations. It is an open question whether stochastic or correlated effects dominate the noise and to what degree.
In this work, we employ a...

6. Comparing Symmetrized Determinant Neural Quantum States For the Hubbard Model

Poster

Accurate simulations of the Hubbard model are crucial to understanding strongly correlated phenomena, where small energy differences between competing orders demand high numerical precision. In this talk, I will present how Neural Quantum States are used to probe the strongly coupled and underdoped regime of the square-lattice Hubbard model. We systematically compare the Hidden Fermion...

2. Complementary Optimization Method for Progressive Adaptive State Search with Biorthogonal Neural Quantum States in Non-Hermitian Systems

Poster

Neural quantum states (NQS) offer a powerful variational framework for many-body systems; however, extending them to non-Hermitian (NH) Hamiltonians raises fundamental questions regarding optimization, symmetry, and architectural bias. In this study, we introduce a complementary optimization framework for progressive adaptive state search (COMPASS) based on biorthogonal adaptive recurrent...

37. DGD: A New Optimizer For Neural Quantum States

Javier Rozalén Sarmiento (Universitat de Barcelona)

Poster

Neural Quantum States started as a machine-learning method to solve for ground states of quantum systems [1], and is now a fully-fledged framework which can deliver competent results when compared to the standard many-body methods. At its core, it is Variational Monte Carlo, with the only difference that the ansätze are neural networks. Therefore, the central calculation is energy...

41. Dynamic Neural Quantum States

Poster

Neural Quantum States have emerged as a powerful variational ansatz for modeling complex quantum systems. However, extending this framework to time evolution presents notable challenges. In this work, we simulate the time evolution of quantum states subjected to potential shifts and momentum kicks using a real-valued network architecture governed by the Dirac-Frenkel variational principle. The...

15. Extracting Photon-Number Information from Superconducting Nanowire Single-Photon Detector Traces via Mean-Derivative Projection

Poster

Superconducting Nanowire Single Photon Detectors (SNSPDs) represent a highly promising and versatile detection technology for applications in quantum optics, photonic quantum computing, quantum key distribution, and beyond. Commercial implementations achieve high detection efficiencies alongside low timing jitter and short reset times. However, a key limitation of standard SNSPD readout...

14. Generative Models using Quantum Reservoirs

Poster

Quantum Reservoir Computing has emerged as a promising approach to exploit the complex dynamics of quantum systems for machine learning, offering an alternative to variational quantum algorithms that suffer from issues like Barren plateaus and expensive optimization. Quantum reservoirs are simple to optimize since they depend only on the training of a linear readout layer. The performance of...

12. Harnessing Quantum Backaction for Time-Series Processing

Giacomo Franceschetto (ICFO)

Poster

Quantum measurements affect the state of the observed systems via backaction. While projective measurements extract maximal classical information, they drastically alter the system. In contrast, indirect measurements balance information extraction with the degree of disturbance. Considering the prevalent use of projective measurements in quantum computing and communication protocols, the...

52. LaRose, "Learning to simulate quantum circuits from samples"

33. Machine Learning Prediction of Diabetic Retinopathy Progression in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images

Poster

The early identification of patients with type 1 diabetes mellitus at risk of developing diabetic retinopathy (DR) is challenging. In this study, we evaluate a non-invasive machine learning (ML) strategy that integrates radiomic features extracted from multimodal retinal imaging with selected clinical variables to predict DR progression at 5 years. Radiomic features were computed from color...

26. Neural Network-Driven Discovery of Classical Communication Protocols, Simulating Qubit Prepare-and-Measure Scenarios

Poster

The Quantum Prepare-and-Measure scenario is a fundamental framework for studying the boundaries of Classical/Quantum communication, and studying the grounds for quantum advantage from a communication complexity perspective. One way of quantifying the quantum advantage would be by measuring classical communication, that should be added to a Local-Hidden-Variable model, so that it can reproduce...

45. Neural Quantum States for Soliton Dynamics in the 1D Gross-Pitaevskii Equation

Poster

A variational approach based on Neural Quantum States (NQS) [1, 2] is proposed to simulate the real-time dynamics of bright solitons in the one-dimensional Gross-Pitaevskii Equation (GPE). This framework enables the effective simulation of the system's time evolution by updating the neural network parameters according to the variational equations of motion [3]. This approach offers a flexible...

42. Normalizing Flows for Classical Simulation of Quantum Circuits in Phase Space

Poster

We present a novel framework to learn a representation of quantum circuits based on the phase space formulation of many-body systems. Given that N-qubit states can be represented as quasi-probability distributions on a 2N-dimensional manifold, we show how unitary gates can be parameterized as normalizing flows between these distributions, opening a new avenue for AI-assisted quantum circuit...

36. One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

Poster

Variational Monte Carlo is a powerful approach to tackle the exponential complexity of quantum many body physics. Taking the variational principle in quantum physics as a starting point, VMC transforms the problem of finding the ground state from an eigenvalue problem to an optimization problem. Then, it relies on two key elements to solve this optimization problem efficiently: an ansatz, i.e...

31. Optimizing ADAPT-VQE: Reduced operator Pools and Gradient-Free Approaches

Poster

Variational Quantum Eigensolvers are one of the main alternatives for quantum ground state preparation for simulating many-body systems on near-term quantum hardware. Fixed structure ansätz such as the Unitary Coupled Cluster, are simple to implement, however, they often leads to deep circuits and optimization bottlenecks. To overcome these limitations, adaptive strategies such as ADAPT-VQE...

11. Quantum Reservoir Computing with tuneable bath memory

Poster

Processing temporal information in quantum many-body systems usually follows one of two paradigms. Quantum reservoir computing retains an unmeasured part of the evolving state, allowing past inputs to influence later outputs. Quantum extreme learning machines instead fully measure and reset the system at each step, relying only on instantaneous nonlinear features. We study the tradeoff between...

24. Self-learning projective quantum Monte Carlo simulations of Rydberg atom arrays with neural network wavefunctions

Vichayuth Imchitr (Perimeter Institute for Theoretical Physics)

Poster

The projective quantum Monte Carlo (PQMC) algorithm provides a way to study the ground state properties of quantum many-body systems. Here, we employ a self-learning PQMC framework using an artificial neural network ansatz as a guiding wavefunction to study Rydberg square lattice systems. The accuracy of the PQMC simulations is improved by employing an accurate neural network ansatz, as...

27. Self-learning projective quantum Monte Carlo simulations of Rydberg atom arrays with neural network wavefunctions

Vichayuth Imchitr (Perimeter Institute for Theoretical Physics)

Poster

The projective quantum Monte Carlo (PQMC) algorithm provides a way to study the ground state properties of quantum many-body systems. Here, we employ a self-learning PQMC framework using an artificial neural network ansatz as a guiding wavefunction to study Rydberg square lattice systems. The accuracy of the PQMC simulations is improved by employing an accurate neural network ansatz, as...

13. Simulating Open Continuous-Variable Many-Body Dynamics with Neural Quantum States

Poster

The simulation of open quantum systems presents a significant computational challenge due to the exponential scaling of the Hilbert space when solving the Lindblad Master Equation. To circumvent this dimensionality curse, we apply a framework that maps the system dynamics onto a phase-space representation, which we then solve using a variational Ansatz parameterized by an invertible neural...

25. Study of the criticality on RBMs using AIS

Arnau Prat Pou (Universitat Politècnica de Catalunya)

Poster

We have used Annealed Importance Sampling (AIS) to study the critical phenomena in Ising-like models and Restricted Boltzmann Machines (RBMs). We have proven that, using a proper initialization based on a mean-field approximation of the target distribution, we can obtain more efficient and accurate sampling of different thermodynamical quantities, and successfully detect phase transitions...

7. TetrisCNN for detecting phase transitions and order parameters

Kacper Cybiński (University of Warsaw, IDEAS Research Institute)

Poster

Detecting phases of matter generally relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transitions and is traditionally guided by physical intuition. While neural networks have recently offered an alternative route by locating phase transitions without a priori physical knowledge, these approaches often remain black boxes. Moreover, they...

5. Unlocking photodetection for quantum sensing with Bayesian likelihood-free methods and deep learning

Mateusz Molenda (Institute of Physics, Polish Academy of Sciences)

Poster

To operate quantum sensors at their quantum limit in real time, it is crucial to identify efficient data inference tools for rapid parameter estimation. In photodetection, the key challenge is the fast interpretation of click-patterns that exhibit non-classical statistics—the very features responsible for the quantum enhancement of precision. We achieve this goal by comparing Bayesian...