⚛️ Category

quant-ph

9083 papers

Quantum physics explores the strange and often counterintuitive rules that govern the universe at its smallest scales. This field investigates how particles like electrons and photons behave in ways that defy our everyday intuition, forming the backbone of modern technologies from lasers to future quantum computers. While the mathematics can be daunting, the core ideas promise to revolutionize how we understand reality and process information.

At Gist.Science, we make these complex discoveries accessible to everyone. We systematically process every new preprint published in the Quant-Ph category on arXiv, transforming dense academic papers into clear, plain-language explanations alongside detailed technical summaries. Whether you are a seasoned researcher or a curious reader, our goal is to bridge the gap between cutting-edge theory and human understanding.

Below are the latest papers in quantum physics, distilled to help you grasp the newest breakthroughs without getting lost in the jargon.

⚛️ quantum physics

Quantum Portfolio Optimization: An Extensive Benchmark

This paper presents an extensive benchmark comparing quantum optimization methods (quantum annealing and QAOA) against state-of-the-art classical algorithms on real-world portfolio optimization instances, concluding that classical mixed-integer programming and tailored heuristics significantly outperform quantum approaches in both solution quality and speed, thereby indicating very limited potential for quantum advantage in this specific domain.

Eric Stopfer, Friedrich Wagner2026-07-10
⚛️ quantum physics

Experimental preparation of WW states through frustration on a programmable quantum simulator

This paper presents a scalable protocol for generating multipartite WW states using topological ring frustration on a Rydberg atom array, achieving 11-qubit states with high fidelity and introducing an efficient Bayesian tomography method to certify these entangled states without exponential overhead.

Alberto Giuseppe Catalano, Ceren Dağ, Gianpaolo Torre, Salvatore Marco Giampaolo, Fabio Franchini2026-07-10
⚛️ quantum physics

Reservoir-Engineered Low-Threshold Quantum Energy Storage

This paper proposes a reservoir-engineered quantum battery that utilizes a two-photon-driven charger coupled via a lossy mediator to achieve a low-threshold, pump-efficient "broken" dissipative regime, enabling exponential energy storage that is predominantly coherent and extractable, outperforming traditional coherent benchmarks by requiring approximately 61% less critical pump power.

Borhan Ahmadi, André H. A. Malavazi, Paweł Mazurek, Paweł Horodecki, Shabir Barzanjeh2026-07-10
⚛️ quantum physics

Gaussian time-translation covariant operations: structure, implementation, and thermodynamics

This paper establishes a rigorous classification of Gaussian time-translation covariant operations, revealing that key results from discrete-variable systems break down in the continuous-variable optical setting due to fundamental discrepancies in implementation, asymmetry extensivity, and catalytic advantages.

Xueyuan Hu, Lea Lautenbacher, Giovanni Spaventa, Martin B. Plenio, Nelly H. Y. Ng, Jeongrak Son2026-07-10
🔭 astrophysics

Wigner function shapelets: Symplectic representation of astronomical images

This paper introduces Wigner function shapelets (WFSs), a novel symplectic framework that extends traditional shapelet analysis to four-dimensional phase space using Laguerre-Gaussian modes and Sp(4,R)\mathrm{Sp}(4,\mathbb{R}) group theory to provide a symmetry-preserving, quantum-information-based representation of astronomical images with inherent resolution limits and sensitivity to coherent morphological structures.

Shun Arai2026-07-10
🤖 machine learning

Image classification via a quantum-inspired strategy involving a mixture of experts

This paper proposes a hybrid classical-quantum image classification framework that utilizes a mixture of experts with quantum-inspired amplitude encoding, local unitary convolutions, and stabilizer code feature extraction to achieve significantly lower failure rates on MNIST and Fashion-MNIST benchmarks compared to individual experts, while maintaining moderate computational overhead on GPU workstations.

Kumari Jyoti, Rohith Babu, Apoorva D. Patel2026-07-10