Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing
Kuan-Cheng Chen, Chen-Yu Liu, Y. Shang +2 more·May 13, 2025
We introduce a distributed quantum-classical framework that synergizes photonic quantum neural networks (QNNs) with matrix product state (MPS) mapping to achieve parameter-efficient training of classical neural networks. By leveraging universal linea...
Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation
Samuel Yen-Chi Chen, Chen-Yu Liu, Kuan-Cheng Chen +3 more·May 13, 2025
The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational quantum circuits (VQCs), also kno...
QSeer: A Quantum-Inspired Graph Neural Network for Parameter Initialization in Quantum Approximate Optimization Algorithm Circuits
Lei Jiang, Chi Zhang, Fangjing Chen·May 11, 2025
To mitigate the barren plateau problem, effective parameter initialization is crucial for optimizing the Quantum Approximate Optimization Algorithm (QAOA) in the near-term Noisy Intermediate-Scale Quantum (NISQ) era. Prior physicsdriven approaches le...
Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks
Erik L. Connerty, Ethan N. Evans, Gerasimos Angelatos +1 more·May 11, 2025
Recent advances in artificial intelligence have highlighted the remarkable capabilities of neural network (NN)-powered systems on classical computers. However, these systems face significant computational challenges that limit scalability and efficie...
Quantum RNNs and LSTMs Through Entangling and Disentangling Power of Unitary Transformations
Ammar Daskin·May 10, 2025
In this paper, we present a framework for modeling quantum recurrent neural networks (RNNs) and their enhanced version, long short-term memory (LSTM) networks using the core ideas presented by Linden et al. (2009), where the entangling and disentangl...
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints
Peter Röseler, Oliver Schaudt, Helmut Berg +2 more·May 9, 2025
While classical convolutional neural networks (CNNs) have revolutionized image classification, the emergence of quantum computing presents new opportunities for enhancing neural network architectures. Quantum CNNs (QCNNs) leverage quantum mechanical ...
A short review on qudit quantum machine learning
Tiago de Souza Farias, Lucas Friedrich, Jonas Maziero·May 8, 2025
As quantum devices scale toward practical machine learning applications, the binary qubit paradigm faces expressivity and resource efficiency limitations. Multi-level quantum systems, or qudits, offer a promising alternative by harnessing a larger Hi...
ARDNS-FN-Quantum: A Quantum-Enhanced Reinforcement Learning Framework with Cognitive-Inspired Adaptive Exploration for Dynamic Environments
Umberto Gonccalves de Sousa·May 7, 2025
Reinforcement learning (RL) has transformed sequential decision making, yet traditional algorithms like Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) often struggle with efficient exploration, stability, and adaptability in dynamic en...
Quantum Feature Space of a Qubit Coupled to an Arbitrary Bath
Chris Wise, Akram Youssry, Alberto Peruzzo +2 more·May 6, 2025
Qubit control protocols have traditionally leveraged a characterisation of the qubit-bath coupling via its power spectral density. Previous work proposed the inference of noise operators that characterise the influence of a classical bath using a gre...
Noisy HQNNs: A Comprehensive Analysis of Noise Robustness in Hybrid Quantum Neural Networks
Tasnim Ahmed, Alberto Marchisio, Muhammad Kashif +1 more·May 6, 2025
Hybrid Quantum Neural Networks (HQNNs) offer promising potential of quantum computing while retaining the flexibility of classical deep learning. However, the limitations of Noisy Intermediate-Scale Quantum (NISQ) devices introduce significant challe...
Qracle: A Graph-Neural-Network-Based Parameter Initializer for Variational Quantum Eigensolvers
Chi Zhang, Lei Jiang, Fangjing Chen·May 2, 2025
Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms with broad applications in quantum physics and quantum chemistry. However, as system size increases, VQE optimization is increasingly hi...
Systematically improved potential energy surfaces via sinNN models and sparse grid sampling
Antoine Aerts·Apr 30, 2025
Accurate, global Potential Energy Surfaces (PES) expressed in sum-of-products (SOP) form are a prerequisite for efficient high-dimensional quantum dynamics simulations using the MCTDH method. This work introduces a methodology for constructing such s...
Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit
Debarshi Kundu, Avimita Chatterjee, Archisman Ghosh +1 more·Apr 30, 2025
We propose Quantum Snapshot with Dynamic Circuit (QSDC), a hardware-agnostic, learning-driven framework for capturing quantum snapshots: non-destructive estimates of quantum states at arbitrary points within a quantum circuit, which can then be class...
Hybrid quantum recurrent neural network for remaining useful life prediction
Olga Tsurkan, Aleksandra Konstantinova, Aleksandr Sedykh +5 more·Apr 29, 2025
Predictive maintenance in aerospace heavily relies on accurate estimation of the remaining useful life of jet engines. In this paper, we introduce a Hybrid Quantum Recurrent Neural Network framework, combining Quantum Long Short-Term Memory layers wi...
Photonic quantum convolutional neural networks with adaptive state injection
Léo Monbroussou, Beatrice Polacchi, Verena Yacoub +14 more·Apr 29, 2025
Abstract. Recent photonic quantum machine learning proposals combined linear optics with adaptivity to enhance expressivity and improve algorithm performance and scalability. The particle-number-preserving property of linear optical platforms was rec...
QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction
Subham Das, Ashtakala Meghanath, B. Behera +3 more·Apr 28, 2025
Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating credit card f...
Fooling the Decoder: An Adversarial Attack on Quantum Error Correction
J. Lenssen, A. Paler·Apr 28, 2025
Neural network decoders are becoming essential for achieving fault-tolerant quantum computations. However, their internal mechanisms are poorly understood, hindering our ability to ensure their reliability and security against adversarial attacks. Le...
Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots
Satwik Kundu, Swaroop Ghosh·Apr 27, 2025
Circuit compilation, a crucial process for adapting quantum algorithms to hardware constraints, often operates as a “black box,” with limited visibility into the optimization techniques used by proprietary systems or advanced open-source frameworks. ...
Predicting sampling advantage of stochastic Ising Machines for Quantum Simulations
Rutger J. L. F. Berns, Davi R. Rodrigues, Giovanni Finocchio +1 more·Apr 25, 2025
Stochastic Ising machines, sIMs, are highly promising accelerators for optimization and sampling of computational problems that can be formulated as an Ising model. Here we investigate the computational advantage of sIM for simulations of quantum mag...
Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis
Mathias Weiden, Justin Kalloor, J. Kubiatowicz +1 more·Apr 22, 2025
Machine Learning with deep neural networks has transformed computational approaches to scientific and engineering problems. Central to many of these advancements are precisely tuned neural architectures that are tailored to the domains in which they ...