Papers
Stochastic Neural Networks for Quantum Devices
Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche·Feb 24, 2026
This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing. Motivated by a classical perceptron, stochastic neurons are introduced and combined into a quantum neural network...
Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning
Luis H. Delgado-Granados, David A. Mazziotti·Feb 23, 2026
We introduce a data-driven framework for approximating the convex set of $N$-representable two-electron reduced density matrices (2-RDMs). Traditional approaches characterize this set through linear matrix inequalities that define its supporting hype...
Quantum Machine Learning for Complex Systems
Vinit Singh, Amandeep Singh Bhatia, Mandeep Kaur Saggi +2 more·Feb 23, 2026
Quantum machine learning (QML) is rapidly transitioning from theoretical promise to practical relevance across data-intensive scientific domains. In this Review, we provide a structured overview of recent advances that bridge foundational quantum lea...
Improving Generalization and Trainability of Quantum Eigensolvers via Graph Neural Encoding
Jungyun Lee, Daniel K. Park·Feb 23, 2026
Determining the ground state of a many-body Hamiltonian is a central problem across physics, chemistry, and combinatorial optimization, yet it is often classically intractable due to the exponential growth of Hilbert space with system size. Even on f...
Learning partial transpose signatures in qubit ququart states from a few measurements
Christian Candeago, Paolo Da Rold, Michele Grossi +2 more·Feb 22, 2026
Higher-dimensional quantum systems are attracting interest for improving quantum protocol performance by increasing memory space. Characterizing quantum resources of such systems is fundamental but experimentally costly. We tackle the first non-trivi...
A rigorous hybridization of variational quantum eigensolver and classical neural network
Minwoo Kim, Kyoung Keun Park, Kyungmin Lee +2 more·Feb 19, 2026
Neural post-processing has been proposed as a lightweight route to enhance variational quantum eigensolvers by learning how to reweight measurement outcomes. In this work, we identify three general desiderata for such data-driven neural post-processi...
Extending quantum theory with AI-assisted deterministic game theory
Florian Pauschitz, Ben Moseley, Ghislain Fourny·Feb 19, 2026
We present an AI-assisted framework for predicting individual runs of complex quantum experiments, including contextuality and causality (adaptive measurements), within our long-term programme of discovering a local hidden-variable theory that extend...
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds
G. Laskaris, D. Morozov, D. Tarpanov +6 more·Feb 18, 2026
Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. F...
Neural Network Discovery of Paired Wigner Crystals in Artificial Graphene
Conor Smith, Yubo Yang, Zhou-Quan Wan +3 more·Feb 18, 2026
Moiré systems have emerged as an exciting tunable platform for engineering and probing quantum matter. A large number of exotic states have been observed, stimulating intense efforts in experiment, theory, and simulation. Utilizing a neural-network-b...
Beyond the Classical Ceiling: Multi-Layer Fully-Connected Variational Quantum Circuits
Howard Su, Chen-Yu Liu, Samuel Yen-Chi Chen +2 more·Feb 18, 2026
Standard Variational Quantum Circuits (VQCs) struggle to scale to high-dimensional data due to the ``curse of dimensionality,'' which manifests as exponential simulation costs ($\mathcal{O}(2^d)$) and untrainable Barren Plateaus. Existing solutions o...
DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting
Prabhjot Singh, Adel N. Toosi, Rajkumar Buyya·Feb 18, 2026
Circuit cutting decomposes a large quantum circuit into a collection of smaller subcircuits. The outputs of these subcircuits are then classically reconstructed to recover the original expectation values. While prior work characterises cutting overhe...
Reinforcement learning for path integrals in quantum statistical physics
Timour Ichmoukhamedov, Dries Sels·Feb 18, 2026
Machine learning is rapidly finding its way into the field of computational quantum physics. One of the most popular and widely studied approaches in this direction is to use neural networks to model quantum states (NQS) in the Hamiltonian formulatio...
Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era
Armin Ahmadkhaniha, Jake Doliskani·Feb 17, 2026
Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit...
Dynamic Synaptic Modulation of LMG Qubits populations in a Bio-Inspired Quantum Brain
J. J. Torres, E. Romera·Feb 17, 2026
We present a biologically inspired quantum neural network that encodes neuronal populations as fully connected qubits governed by the Lipkin-Meshkov-Glick (LMG) quantum Hamiltonian and stabilized by a synaptic-efficacy feedback implementing activity-...
Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer
Hayato Kunugi, Mohsen Rahmani, Yosuke Iyama +8 more·Feb 17, 2026
Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency of drug-like compounds. To r...
Quantum-Assisted Trainable-Embedding Physics-Informed Neural Networks for Parabolic PDEs
Ban Q. Tran, Nahid Binandeh Dehaghani, Rafal Wisniewski +2 more·Feb 16, 2026
Physics-informed neural networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs) by embedding governing physical laws directly into the training objective. Recent advances in quantum machine learning hav...
Forked Physics Informed Neural Networks for Coupled Systems of Differential equations
Zhao-Wei Wang, Zhao-Ming Wang·Feb 16, 2026
Solving coupled systems of differential equations (DEs) is a central problem across scientific computing. While Physics Informed Neural Networks (PINNs) offer a promising, mesh-free approach, their standard architectures struggle with the multi-objec...
TensorCircuit-NG: A Universal, Composable, and Scalable Platform for Quantum Computing and Quantum Simulation
Shi-Xin Zhang, Yu-Qin Chen, Weitang Li +22 more·Feb 15, 2026
We present TensorCircuit-NG, a next-generation quantum software platform designed to bridge the gap between quantum physics, artificial intelligence, and high-performance computing. Moving beyond the scope of traditional circuit simulators, TensorCir...
Neural-network quantum states for the nuclear many-body problem
Alessandro Lovato, Giuseppe Carleo, Bryce Fore +4 more·Feb 14, 2026
A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum ...
Robustness Verification of Binary Neural Networks: An Ising and Quantum-Inspired Framework
Rahul Singh, Seyran Saeedi, Zheng Zhang·Feb 14, 2026
Binary neural networks (BNNs) are increasingly deployed in edge computing applications due to their low hardware complexity and high energy efficiency. However, verifying the robustness of BNNs against input perturbations, including adversarial attac...