Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture search
Akash Kundu, Stefano Mangini·May 14, 2025
Variational quantum algorithms hold the promise to address meaningful quantum problems already on noisy intermediate-scale quantum hardware. In spite of the promise, they face the challenge of designing quantum circuits that both solve the target pro...
Demonstration of logical quantum phase estimation for X-ray absorption spectra
Hirofumi Nishi, Taichi Kosugi, Satoshi Hirose +2 more·May 13, 2025
In this study, we employed Fourier-based quantum phase estimation (QPE) to calculate X-ray absorption spectroscopy (XAS) spectra. The primary focus of this study is the calculation of the XAS spectra of transition metal $L_{2,3}$-edges, which are dom...
Performance of rotation-symmetric bosonic codes in the presence of random telegraph noise
Adithi Udupa, Timo Hillmann, R. G. Ahmed +2 more·May 13, 2025
Decoherence in quantum devices, such as qubits and resonators, is often caused by bistable fluctuators modeled as random telegraph noise (RTN), leading to significant dephasing. We analyze the impact of individual and multiple fluctuators on a bosoni...
Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles
J. Park, Jiook Cha, Samuel Yen-Chi Chen +2 more·May 13, 2025
Practical Quantum Machine Learning (QML) is challenged by noise, limited scalability, and poor trainability in Variational Quantum Circuits (VQCs) on current hardware. We propose a multi-chip ensemble VQC framework that systematically overcomes these...
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...
Regularizing quantum loss landscapes by noise injection
Daniil S. Bagaev, Maxim A.Gavreev, A. Mastiukova +2 more·May 13, 2025
The difficulty of training variational quantum algorithms and quantum machine learning models is well established. In particular, quantum loss landscapes are often highly non-convex and dominated by poor local minima. While this renders their trainin...
Physics-Inspired Extrapolation for efficient error mitigation and hardware certification
Pablo Díez-Valle, Gaurav Saxena, Jack S. Baker +2 more·May 12, 2025
Quantum error mitigation (QEM) is essential for the noisy intermediate-scale quantum era, and will remain relevant for early fault-tolerant quantum computers, where logical error rates are still significant. However, most QEM methods incur an exponen...
Quantum Keyless Private Communication under intense background noise
Pedro Neto Mendes, Davide Rusca, Hugo Zbinden +1 more·May 12, 2025
Quantum key distribution relies on quantum mechanics to securely distribute cryptographic keys, offering security but necessitating complex infrastructure and significant resources for practical implementation. Quantum keyless private communication e...
Suppressing measurement noise in logical qubits through measurement scheduling
Xiao-Yue Xu, Chen Ding, Wan-Su Bao·May 12, 2025
Quantum error correction is essential for reliable quantum computation, where surface codes demonstrate high fault-tolerant thresholds and hardware efficiency. However, noise in single-shot measurements limits logical readout fidelity, forming a crit...
Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series
Wajdi Hammami, S. Cherkaoui, Shengrui Wang·May 12, 2025
Quantum computing may offer new approaches for advancing machine learning, including in complex tasks such as anomaly detection in network traffic. In this paper, we introduce a quantum generative adversarial network (QGAN) architecture for multivari...
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...
Protected Symmetrical Superconducting Qubit Based on Quantum Flux Parametron
Kian Rafati Sajedi, Mojtaba Hosseinpour Choubi, M. Fardmanesh·May 10, 2025
Conventional Quantum Flux Parametrons (QFPs) have historically been used for storing classical bits in Josephson junction-based computers. In this work, we propose a novel QFP-based topology dubbed"Degenerium"qubit, to process and compute quantum inf...
Inferring charge-noise source locations from correlations in spin qubits
Juan S. Rojas-Arias, Akito Noiri, Jun Yoneda +7 more·May 9, 2025
We investigate low-frequency noise in a spin-qubit device made in isotopically purified Si/Si-Ge. Observing sizable cross-correlations among energy fluctuations of different qubits, we conclude that these fluctuations are dominated by charge noise. A...
Towards Secondary Structure Prediction of Longer Mrna Sequences Using a Quantum-Centric Optimization Scheme
Vaibhaw Kumar, Dimitris Alevras, Mihir Metkar +9 more·May 9, 2025
Accurate prediction of mRNA secondary structure is critical for understanding gene expression, translation efficiency, and advancing mRNA-based therapeutics. However, the combinatorial complexity of possible foldings, especially in long sequences, po...
The Quantum Approximate Optimization Algorithm Can Require Exponential Time to Optimize Linear Functions
Francisco Chicano, Z. Dahi, Gabriel Luque·May 9, 2025
QAOA is a hybrid quantum-classical algorithm to solve optimization problems in gate-based quantum computers. It is based on a variational quantum circuit that can be interpreted as a discretization of the annealing process that quantum annealers foll...
Quasiprobabilistic imaginary-time evolution on quantum computers
A. Ray, Esha Swaroop, Ningping Cao +2 more·May 9, 2025
Imaginary-time evolution plays an important role in algorithms for computing ground-state and thermal equilibrium properties of quantum systems, but can be challenging to simulate on classical computers. Many quantum algorithms for imaginary-time evo...
A circuit-differentiation framework for Green's functions on quantum computers
Samuele Piccinelli, Francesco Tacchino, Ivano Tavernelli +1 more·May 8, 2025
We propose a general framework for computing Retarded Green's Functions (RGFs) on quantum computers by recasting their evaluation as a problem of circuit differentiation. Our proposal is based on real-time evolution and specifically designed circuit ...
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...
Quantum-enabled optical large-baseline interferometry: applications, protocols and feasibility
Zixin Huang, Oleg Titov, Mikolaj K. Schmidt +4 more·May 7, 2025
Optical Very Long Baseline Interferometry (VLBI) offers the potential for unprecedented angular resolution in both astronomical imaging and precision measurements. Classical approaches, however, face significant limitations due to photon loss, backgr...
Adiabatic state preparation and thermalization of simulated phase noise in a Rydberg spin Hamiltonian
Tomas Kozlej, Gerard Pelegri, Jonathan D. Pritchard +1 more·May 7, 2025
Laser phase noise is one of the main sources of decoherence in driven Rydberg systems with neutral atoms in tweezer arrays. While the effect of phase noise in the regimes of isolated qubits and few-qubit gate protocols has been studied extensively, t...