Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
Pulse-based optimization of quantum many-body states with Rydberg atoms in optical tweezer arrays
Kazuma Nagao, S. Julia-Farr'e, J. Vovrosh +2 more·Jul 25, 2025
We explore a pulse-based variational quantum eigensolver (VQE) algorithm for Rydberg atoms in optical tweezer arrays and evaluate its performance on prototypical quantum spin models. We numerically demonstrate that the ground states of the one-dimens...
Programmable exploration of magnetic states in Lieb-kagome interpolated lattices
A. Lopez-Bezanilla, Pavel A. Dub, Avadh Saxena·Jul 24, 2025
We investigate a hybrid modeling framework in which a quantum annealer is used to simulate magnetic interactions in molecular qubit lattices inspired by experimentally realizable systems. Using phthalocyanine assemblies as a structurally constrained ...
Co-Optimization of Codon Usage and mRNA Secondary Structure Using Quantum Computing
Dimitris Alevras, Mihir Metkar, Triet Friedhoff +5 more·Jul 24, 2025
Co-optimizing mRNA sequences for both codon optimality and secondary structure is crucial for producing stable and efficacious mRNA therapeutics. Codon optimization, which adjusts nucleotide sequences to enhance translational efficiency, inherently i...
Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
Gilberto Cunha, Alexandra Ramoa, A. Sequeira +2 more·Jul 24, 2025
Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks. Recent advan...
Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
Sara Giordano, Kornikar Sen, Miguel A. Martin-Delgado·Jul 22, 2025
A reinforcement learning (RL) framework is introduced for the efficient synthesis of quantum circuits that generate specified target quantum states from a fixed initial state, addressing a central challenge in both the Noisy Intermediate-Scale Quantu...
Meta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines
R. V. Bhat, Rahul Bhowmick, Avinash Singh +1 more·Jul 22, 2025
The preparation of quantum Gibbs states is a fundamental challenge in quantum computing, essential for applications ranging from modeling open quantum systems to quantum machine learning. Building on the Meta-Variational Quantum Eigensolver framework...
Global-scale quantum networking using hybrid-channel quantum repeaters with relays based on a chain of balloons
Pei-Xi Liu, Yu-Ping Lin, Zong-Quan Zhou +2 more·Jul 21, 2025
Global-scale entanglement distribution has been a formidable challenge due to the unavoidable losses in communication channels. Here, we propose a novel backbone channel for quantum network based on balloon-based aerial relays. We demonstrate for the...
Ground and excited-state energies with analytic errors and short time evolution on a quantum computer
Timothy Stroschein, D. Castaldo, Markus Reiher·Jul 20, 2025
Accurately solving the Schr\"odinger equation remains a central challenge in computational physics, chemistry, and materials science. Here, we propose an alternative eigenvalue problem based on a system's autocorrelation function, avoiding direct ref...
QAS-QTNs: Curriculum Reinforcement Learning-Driven Quantum Architecture Search for Quantum Tensor Networks
Siddhant Dutta, Nouhaila Innan, S. Yahia +1 more·Jul 16, 2025
Quantum Architecture Search (QAS) is an emerging field aimed at automating the design of quantum circuits for optimal performance. This paper introduces a novel QAS framework employing hybrid quantum reinforcement learning with quantum curriculum lea...
A scalable quantum-neural hybrid variational algorithm for ground state estimation
Minwoo Kim, Kyoung Keun Park, Uihwan Jeong +2 more·Jul 15, 2025
We propose the unitary variational quantum-neural hybrid eigensolver (U-VQNHE), which improves upon the original VQNHE by enforcing unitary neural transformations. The non-unitary nature of VQNHE causes normalization issues and divergence of the loss...
On the Importance of Fundamental Properties in Quantum-Classical Machine Learning Models
Silvie Illésová, Tomasz Rybotycki, Piotr Gawron +1 more·Jul 14, 2025
We present a systematic study of how quantum circuit design, specifically the depth of the variational ansatz and the choice of quantum feature mapping, affects the performance of hybrid quantum-classical neural networks on a causal classification ta...
Quantum Coulomb Blockade in Orbital Resolved Phosphorus Triple-Donor Molecule
Soumya Chakraborty, Pooja Sudha, Hemant Arora +2 more·Jul 13, 2025
Multi-donor architecture in silicon offers a promising direction towards scalable solid-state qubits and quantum technologies operating at practical conditions. However, the overlap of multiple donor wave-functions develops a complex internal electro...
Hybrid Quantum-Classical Generative Adversarial Networks with Transfer Learning
Asma Al-Othni, Saif Al-Kuwari, Mohammad Mahdi Nasiri Fatmehsari +2 more·Jul 13, 2025
Generative Adversarial Networks (GANs) have demonstrated immense potential in synthesizing diverse and high-fidelity images. However, critical questions remain unanswered regarding how quantum principles might best enhance their representational and ...
Intrinsic Multi-Mode Interference for Passive Suppression of Purcell Decay in Superconducting Circuits
Mustafa Bakr, Mohammed Alghadeer, Simon Pettersson Fors +6 more·Jul 13, 2025
Decoherence due to radiative decay remains an important consideration in scaling superconducting quantum processors. We introduce a passive, interference-based methodology for suppressing radiative decay using only the intrinsic multi-mode structured...
Photonic processor benchmarking for variational quantum process tomography
Vladlen Galetsky, Paul Kohl, Janis Nötzel·Jul 11, 2025
We present a quantum-analogous experimental demonstration of variational quantum process tomography using an optical processor. This approach leverages classical one-hot encoding and unitary decomposition to perform the variational quantum algorithm ...
A Neural-Guided Variational Quantum Algorithm for Efficient Sign Structure Learning in Hybrid Architectures
Mengzhen Ren, Yu-Cheng Chen, Yangsen Ye +3 more·Jul 10, 2025
Variational quantum algorithms hold great promise for unlocking the power of near-term quantum processors, yet high measurement costs, barren plateaus, and challenging optimization landscapes frequently hinder them. Here, we introduce sVQNHE, a neura...
Quantum and Hybrid Machine‐Learning Models for Materials‐Science Tasks
Leyang Wang, Yilun Gong, Zongrui Pei·Jul 10, 2025
Quantum computing has become increasingly practical in solving real‐world problems due to advances in hardware and algorithms. In this paper, we aim to design, apply, and evaluate quantum machine learning and hybrid quantum‐classical models in a few ...
Parametrized Quantum Circuit Learning for Quantum Chemical Applications
G. Jones, Viki Kumar Prasad, U. Fekl +1 more·Jul 10, 2025
In the field of quantum machine learning (QML), parametrized quantum circuits (PQCs) -- constructed using a combination of fixed and tunable quantum gates -- provide a promising hybrid framework for tackling complex machine learning problems. Despite...
Two Variations of Quantum Phase Estimation for Reducing Circuit Error Rates: Application to the Harrow--Hassidim--Lloyd Algorithm
Yonghae Lee, Minjin Choi, Youngho Min +2 more·Jul 9, 2025
We introduce two variations of the quantum phase estimation algorithm: quantum shifted phase estimation and quantum punctured phase estimation. The shifted method employs a bit-string left shift to discard the most significant bit and focus on lower-...
Utility-Scale Quantum Computation of Ground-State Energy in a 100+ Site Planar Kagome Antiferromagnet via Hamiltonian Engineering
Muhammad Ahsan·Jul 8, 2025
We present experimental quantum computation of the ground-state energy in a 103-site flat Kagome lattice under the antiferromagnetic Heisenberg model (KAFH), with IBM's Heron r1 and Heron r2 quantum processors. For spin-1/2 KAFH, our per-site ground-...