Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Loss-tolerant detection of squeezed states in the 2 um region
K. M. Kwan, T. G. McRae, J. Qin +9 more·Sep 17, 2025
Squeezed states of light enable quantum-enhanced measurements but are limited by optical loss, particularly at 2 um where photodiode efficiency is low. We report the first loss-tolerant, audio-band squeezed light detection at 1984 nm by using a phase...
Rare Event Simulation of Quantum Error-Correcting Circuits
Carolyn Mayer, Anand Ganti, Uzoma Onunkwo +3 more·Sep 17, 2025
We describe a practical approach for accessing the logical failure rates of quantum error-correcting (QEC) circuits under low physical (component) failure rate regimes. Standard Monte Carlo is often the de facto approach for studying the failure rate...
Machine Learning for Quantum Noise Reduction
Karan Kendre·Sep 17, 2025
Quantum noise fundamentally limits the utility of near-term quantum devices, making error mitigation essential for practical quantum computation. While traditional quantum error correction codes require substantial qubit overhead and complex syndrome...
End-to-End Complexity Analysis for Quantum Simulation of the Extended Jaynes-Cummings Models
Nam Nguyen, Michael Yu, Alan Robertson +3 more·Sep 16, 2025
The extended Jaynes-Cummings model (eJCM) is a foundational framework for describing multi-mode light-matter interactions, with direct applications in quantum technologies such as photon addition and quasi-noiseless amplification. However, the model'...
QDFlow: A Python package for physics simulations of quantum dot devices
Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler +2 more·Sep 16, 2025
Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, representative datasets designed to capture the full spectrum of data quali...
Resource-efficient entanglement detection in high-dimensional states via two-qubit witnesses
Josef Kadlec, Artur Barasiński, Karel Lemr·Sep 16, 2025
This paper presents an efficient method for detecting entanglement in high-dimensional two-qudit states by mapping the Hilbert space onto the space of two qubits. This transformation enables the use of well-established two-qubit entanglement witnesse...
Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin +2 more·Sep 16, 2025
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional models often fail to capture the complex error dynamics of real hardw...
The Quantum Control Hierarchy: When Physics-Informed Design Meets Machine Learning
Atta ur Rahman, M. Y. Abd-Rabbou, Cong-feng Qiao·Sep 16, 2025
We address a wide spectrum of quantum control strategies, including various open-loop protocols and advanced adaptive methods. These methodologies apply to few-qubit scenarios and naturally scale to larger N-qubit systems. We benchmark them across fu...
Non-Markovian amplitude damping in a central spin model with random couplings
Mehboob Rashid, Rayees A Mala, Saima Bashir +1 more·Sep 16, 2025
Non-Markovian dynamics is central to quantum information processing, as memory effects strongly influence coherence preservation, metrology, and communication. In this work, we investigate the role of stochastic system--bath couplings in shaping non-...
EmuPlat: A Framework-Agnostic Platform for Quantum Hardware Emulation with Validated Transpiler-to-Pulse Pipeline
Jun Ye, Jun Yong Khoo·Sep 16, 2025
We present EmuPlat, a framework-agnostic quantum hardware emulation platform that addresses the interoperability gap between high-level quantum programming frameworks and hardware-specific pulse control systems. Unlike existing solutions that operate...
Enlarging the GKP stabilizer group for enhanced noise protection
Jonathan Pelletier, Baptiste Royer·Sep 15, 2025
Encoding a qubit in a larger Hilbert space of an oscillator is an efficient way to protect its quantum information against decoherence. Promising examples of such bosonic encodings are the Gottesman-Kitaev-Preskill (GKP) codes. In this work, we inves...
Efficient Entanglement Purification Circuit Design for Dual-Species Atom Arrays
Bikun Li, Daniel Dilley, Alvin Gonzales +6 more·Sep 15, 2025
Entanglement purification protocols (EPPs) are essential for generating high-fidelity entangled states in noisy quantum systems, enabling robust quantum networking and computation. Building on the circuit of the foundational recurrence protocol, we g...
Numerical Optimization Methods in the environment with Quantum Noise
Tomáš Bezděk·Sep 15, 2025
The accurate calculation of electronic potential energy surfaces for ground and excited states is crucial for understanding photochemical processes, particularly near conical intersections. While classical methods are limited by scaling and quantum a...
Optomechanical Accelerometer Search for Ultralight Dark Matter
M. Dey Chowdhury, J. P. Manley, C. A. Condos +2 more·Sep 15, 2025
Cavity optomechanical systems have recently been proposed as detectors for ultralight dark matter, leveraging their ability to cool and probe mechanical oscillators at the quantum limit. Here we present a resonant search for ultralight dark matter us...
Adiabatically driven dissipative many-body quantum spin systems
Paulo J. Paulino, Stefan Teufel, Federico Carollo +1 more·Sep 15, 2025
We explore the evolution of a strongly interacting dissipative quantum Ising spin chain that is driven by a slowly varying time-dependent transverse field. This system possesses an extensive number of instantaneous (adiabatic) stationary states which...
Quantum reservoir computing for predicting and characterizing chaotic maps
Qingyu Li, Chiranjib Mukhopadhyay, Ludovico Minati +1 more·Sep 15, 2025
Quantum reservoir computing has emerged as a promising paradigm for harnessing quantum systems to process temporal data efficiently by bypassing the costly training of gradient-based learning methods. Here, we demonstrate the capability of this appro...
Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing
Leo Sünkel, Jonas Stein, Jonas Nüßlein +2 more·Sep 15, 2025
Scaling quantum computers, i.e., quantum processing units (QPUs) to enable the execution of large quantum circuits is a major challenge, especially for applications that should provide a quantum advantage over classical algorithms. One approach to sc...
Characterizing Scaling Trends of Post-Compilation Circuit Resources for NISQ-era QML Models
Rupayan Bhattacharjee, Pau Escofet, Santiago Rodrigo +3 more·Sep 15, 2025
This work investigates the scaling characteristics of post-compilation circuit resources for Quantum Machine Learning (QML) models on connectivity-constrained NISQ processors. We analyze Quantum Kernel Methods and Quantum Neural Networks across proce...
Quantum Noise Tomography with Physics-Informed Neural Networks
Antonin Sulc·Sep 15, 2025
Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are often data-intensive and struggle with scalability. In this work, we intr...
Towards a Global Scale Quantum Information Network: A Study Applied to Satellite-Enabled Distributed Quantum Computing
Laurent de Forges de Parny, Luca Paccard, Mathieu Bertrand +8 more·Sep 15, 2025
Recent developments have reported on the feasibility of interconnecting small quantum registers in a quantum information network of a few meter-scale for distributed quantum computing purposes. This multiple small-scale quantum processors communicati...