Quantum Computing Algorithms and Architecture

Últimos artículos

• AG-CoT: Verified Algorithmic Traces for LLM Program Synthesis on Clifford Circuits

  Lu Wei, Yufeng Wang, Chenfeng Cao, Lu Pang, Haibin Ling · 30 de septiembre de 2026

  Scientific code generation can produce executable programs that fail to compute the intended scientific object. We study this problem in language-model synthesis of Clifford circuits, which prepare the stabilizer states used in quantum error correction and admit exact classical verification. In our …

• SQUARE: Structured Quantum Representation Adapters as Compact Quadratic Feature Maps for Frozen Language Models

  Emily Jimin Roh, Hyojun Ahn, Hoyeong Lee, Soohyun Park, Sung Whan Yoon, Vaneet Aggarwal, Joongheon Kim · 30 de septiembre de 2026

  Frozen language models (LMs) are increasingly used as fixed feature extractors for downstream reranking, scoring, and preference modeling, raising a practical question: how should a compact module represent interactions among features in a fixed low-dimensional bottleneck? Common linear and low-rank…

• Quantum Monte Carlo Tree Search with Fixed Confidence

  Mingjie Hu, Jian-Qiang Hu, Enlu Zhou · 29 de septiembre de 2026

  Recent advances in quantum computing are opening new opportunities for computationally intensive decision problems. This paper studies how quantum computing can improve Monte Carlo tree search (MCTS) in the fixed-confidence setting, where the goal is to identify a near-optimal move in a given game t…

• QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAG

  Hyojun Ahn, Emily Jimin Roh, Soohyun Park, Walid Saad, Hyung-Chul Lee, Joongheon Kim · 29 de septiembre de 2026

  Fidelity-based quantum retrieval ranks candidates by the fidelity between query and archive states. Applying a shared input-independent unitary after fixed state encoding leaves that fidelity unchanged, so training the circuit cannot alter the ranking. Quantum parameter-efficient input-dependent ret…

• Cross-Backend QIEO: Universal Runtime Portability across OpenMP5, CUDA, HIP, and Multi-Language Interfaces

  Aman Mittal, Ferdin Sagai Don Bosco, Kasturi Venkata Srikanth, Abhishek Singh, Aditya Singh, Abhishek Chopra · 28 de septiembre de 2026

  Quantum-inspired algorithms emulate quantum mechanical principles, such as, superposition, interference, and probabilistic amplitude evolution, on classical hardware by representing candidate solutions as qubit vectors and evolving them through rotation-gate operators. This approach offers higher op…

• Quantum Diffusion Models for Medical Image Analysis

  Francesco Aldo Venturelli, Stefano Martina, Marco Parigi, Filippo Caruso, Alba Cervera-Lierta, Miguel A. Gonz\'alez Ballester · 28 de septiembre de 2026

  Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such as superposition, entanglement and interference. In this context, we present a scalable hybrid Quantum Diffusion Model, and evaluate its use for medica…

• Learning and interpreting policies for simultaneous entanglement requests in quantum networks

  Leon Rode, Sumeet Khatri, Supartha Podder · 25 de septiembre de 2026

  Future quantum networks will make use of entanglement to perform numerous tasks, such as sending quantum information over long distances, distributed quantum computing, and quantum sensing. In general, these tasks will need to be performed simultaneously in various regions of a network, while minimi…

• MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline

  Andre Youssefi (Leibniz Supercomputing Centre), Erc\"ument Kaya (Leibniz Supercomputing Centre, Technical University of Munich), Minh Chung (Leibniz Supercomputing Centre), Jorge Echavarria (Munich Quantum Valley), Laura B. Schulz (Argonne National Laboratory), Martin Schulz (Leibniz Supercomputing Centre, Technical University of Munich) · 25 de septiembre de 2026

  High Performance Computing (HPC) and Quantum Computing (QC) systems are increasingly converging towards unified High Performance Computing-Quantum Computing (HPCQC) infrastructures, driven by a growing need to bridge classical and quantum workflows, which affects all levels of the system stack, from…

• QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Models

  Mostafa Mehdipour Ghazi · 25 de septiembre de 2026

  Adapting large pretrained vision models under limited data and frozen-backbone constraints remains a central challenge in transfer learning. While lightweight adapters and parameter-efficient fine-tuning methods are widely adopted, most rely on generic multilayer perceptrons or low-rank linear updat…

• Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment

  Dilli Hang Rai · 25 de septiembre de 2026

  Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep Quantum Neural Network that couples an adaptive, parameter-free SimAM weightin…

• Quantum score matching with applications to learning thermal states

  Yulong Dong, Jiaqi Leng · 24 de septiembre de 2026

  Score matching has driven major advances in classical generative learning by enabling models to learn from data without evaluating intractable normalization constants, or partition functions. Yet, extending this principle to quantum learning requires rethinking its foundations, as quantum states are…

• Quantum Reinforcement Learning for Cost and Delay Tradeoffs in Quantum Cloud Orchestration

  An N. H. Phan, Dang Van Huynh, Muhammad Usman, Hoa T. Nguyen · 24 de septiembre de 2026

  Quantum cloud computing, delivered through the quantum-as-a-service (QaaS) model, provides access to quantum computing resources. However, applying uniform time-based pricing across fundamentally heterogeneous quantum resources significantly complicates task orchestration, particularly when addressi…

• GRPO-QPS: Target-Preserving Reinforcement Learning for Quantum Posterior Sampling

  Yufeng Wang, Parivesh Priye, Lu Wei, Haibin Ling · 23 de septiembre de 2026

  Bayesian quantum tomography requires efficient inference while preserving a posterior fixed by the prior and Born likelihood. Learned transport provides fast amortized samples, but reward tuning can reshape the generated distribution rather than improve exploration of this fixed target. We introduce…

• Bridge of $\Psi$'s: Quantum Circuit Optimization with Schr\"odinger Bridges

  Lino S. Hofstetter, Lia Yeh, Prakash Murali · 23 de septiembre de 2026

  Quantum circuit optimization replaces a circuit with an equivalent one of fewer gates and lower depth, reducing execution cost and error rate. We ask whether a generative model can learn this transformation directly from examples, rather than selecting from a fixed rewrite library or rigid algebraic…

• End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks

  Melek Krichen, Nikhitha Nunavath, Riccardo Bassoli, Soumaya Cherkaoui · 23 de septiembre de 2026

  This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom). Classical data are compressed into low-dimensional semantic representations, encoded and processed by a variational quantum transmitter, transmitted …

• From IceCube to IT-Sphere: A Hybrid Quantum-Classical GNN for Banking IT Root Cause Analysis

  Antonio Greco, Riccardo Paoletti, Roberto Cappuccio, Mario Onorato · 23 de septiembre de 2026

  We present Hybrid Quantum Root Cause Analysis (HQ-RCA), an industrially grounded workflow for root cause analysis in banking IT operations, built on a hybrid Quantum Graph Neural Network (QGNN): the classical backbone of DynEdge (the IceCube neutrino-reconstruction GNN, which we call standalone DynE…

• When Quantum Meets AI: Quantum Methods for Machine Learning and Machine Learning Methods for Quantum Systems

  Tak Hur · 23 de septiembre de 2026

  This thesis studies the intersection of quantum computing and artificial intelligence in two directions: quantum methods for machine learning and machine learning methods for quantum systems. For quantum machine learning, Neural Quantum Embedding learns data representations that increase the trace d…

• Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach

  Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal, Georgios Kellaris, Joaquin del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria · 23 de septiembre de 2026

  Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purely classical approaches. Hybrid quantum-classical models operationalize this potential by embedding a parameterized quant…

• Weakly Supervised Quantum Error Mitigation

  Seyed Mohamad Ali Tousi, G. N. DeSouza · 23 de septiembre de 2026

  Supervised approaches to quantum error mitigation learn a map from noisy circuit outputs to ideal ones, and therefore require the ideal outputs. Producing those ideal outputs demands noiseless classical simulation, whose cost grows exponentially with system size, so supervision is unavailable in exa…

• Watching Quantum Models Think: Hilbert-Space Interpretability in Quantum Transformer Blocks

  Diego Iacopetta, Andrea Gasparini · 22 de septiembre de 2026

  Deep learning models are powerful but opaque. As quantum machine learning matures, the field faces a defining choice: build quantum models that are equally opaque, or exploit the mathematical structure of quantum mechanics to make them inherently interpretable. We show that the latter is possible. B…

• A Hybrid Quantum Neural Network to Analyse Big Experimental Powder X-ray Diffraction Data

  H. Dong, S. D. M. Jacques, M. Q. Hlatshwayo, E. Papoutsellis, K. Georgopoulos, A. M. Beale, A. Vamvakeros · 22 de septiembre de 2026

  Quantitative analysis of experimental powder X-ray diffraction data remains challenging when evaluating complex multiphase materials and noisy measurements. We introduce a hybrid quantum neural network framework designed to extract quantitative parameters, such as phase weight fractions and scale fa…

• Q-DEQ: Discrete Solving and Quantization for Deep Equilibrium Models in Time Series Forecasting under Edge Deployment Coding Constraints

  Ruotong Yang, Hongdong Zhu, Qi Gao, Yin Ma, Hai Wei, Kai Wen · 22 de septiembre de 2026

  Edge deployment motivates forecasting models with compact parameter storage and low-bit representations. Deep equilibrium models (DEQs) obtain implicit depth by repeatedly applying a shared layer, reducing the parameter cost of explicit layer stacking. Their usual Anderson solver, however, searches …

• Task-Aware Hybrid QUBO Optimization for Structured Neural Network Pruning

  Osama Orabi, Artur Zagitov, Hadi Salloum, Viktor A. Lobachev, Yaroslav Kholodov · 22 de septiembre de 2026

  Neural network pruning can be formulated as a combinatorial optimization problem, yet many existing approaches rely on independent filter-importance scores or simplified objective functions. In this work, we propose a Hybrid Quadratic Unconstrained Binary Optimization (QUBO) framework for structured…

• Circuit Hypernetworks for Quantum-Augmented Diffusion Language Models

  Xiaoqiang Wang, Mengyang Xiong, Jun Dai, Bang Liu · 22 de septiembre de 2026

  Language models can be adapted by changing the computations applied to individual tokens. Quantum circuits offer one such approach, but evaluating wider circuits inside a large model can be computationally demanding. Here we introduce HyperQ, which adds token-conditioned quantum residual branches to…

• Weighted Quantum Signal Processing: Low-Depth Polynomial Approximation with Applications to Kolmogorov-Arnold Networks

  Rohit Sarma Sarkar, Rupayan Bhattacharjee, Elias F. Combarro, Michele Grossi, Lirand\"e Pira, Carmen G. Almud\'ever, Sergi Abadal, Eduard Alarcon · 21 de septiembre de 2026

  Quantum Signal Processing is a powerful quantum framework for generating and approximating univariate polynomials. However, QSP is often limited by circuit-depth bottlenecks and parity constraints on the class of realizable polynomials. In this work, we introduce Weighted Quantum Signal Processing, …