⚛️ Category

quant-ph

9628 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.

💻 computer science

quantum-safe: Bridging the Post-Quantum Production Gap with a Hybrid-by-Default Python Cryptography Library

This paper introduces *quantum-safe*, a hybrid-by-default Python cryptography library that bridges the post-quantum production gap by achieving full coverage across eight critical readiness dimensions, significantly reducing implementation complexity and overhead while providing the first statistically rigorous performance and timing side-channel analysis of a Python-based PQC ecosystem.

Animesh Shaw2026-05-19
⚛️ quantum physics

Near-Optimal Quantum Time Evolution Circuits via Provably Convergent Compression

This paper introduces a provably convergent variational compression method with a specific initialization recipe that guarantees near-optimal gate complexity for simulating local, translationally invariant Hamiltonians, successfully demonstrated on a 48-site Kagome lattice Heisenberg antiferromagnet to enable quantum simulations beyond classical capabilities.

Erenay Karacan, Isabel Nha Minh Le, Matteo D'Anna, Juan Carasquilla, Christian B. Mendl, Ivan Rojkov2026-05-19
⚛️ high-energy theory

Covariant extrinsic curvature expansion of the nonlocal effective action for a massless scalar field on a manifold with boundary

This paper employs a heat-kernel approach to derive a covariant expansion of the nonlocal effective action for a massless scalar field on a flat manifold with a curved boundary, extending previous results to general surfaces and applying the framework to calculate particle-creation rates for oscillating deformed geometries in 2+1 and 3+1 dimensions.

A. Boasso, C. D. Fosco, B. C. Guntsche, F. D. Mazzitelli2026-05-19
⚛️ quantum physics

Toward Near-Real-Time Marine Oil Spill Detection in SAR Imagery using Quantum-Assisted SVM

This paper presents a quantum-assisted Support Vector Machine (QSVM) bagging ensemble that leverages quantum annealing to optimize support vectors for near-real-time marine oil spill detection in SAR imagery, achieving performance comparable to classical baselines with an IoU of 0.60 and demonstrating feasibility for efficient, transferable environmental monitoring.

Joseph Strauss, Jyotsna Sharma2026-05-19
⚛️ quantum physics

Maximum Likelihood Decoding of Quantum Error Correction Codes

This topical review provides a unified perspective on the computationally intractable but optimal Maximum Likelihood Decoding (MLD) of quantum error correction codes by surveying recent advances through the complementary lenses of statistical mechanics, tensor networks, and artificial intelligence, while discussing their connections, applications, and future challenges.

Hanyan Cao, Ge Yan, Yuxuan Du, Feng Pan2026-05-19
⚛️ quantum physics

Module Lattice Security (Part III): Structured CVP Distance on the Log-Unit Lattice

This paper establishes that the L2L^2 distance from random short ring elements to the log-unit lattice of \Q(ζ2k)\Q(\zeta_{2^k}) converges to a specific constant times n\sqrt{n}, proving that structured targets lie within the Voronoi cell of the origin and enabling a reduction of the CDPR approximation factor for ML-KEM from exponential to sub-polynomial.

Ming-Xing Luo2026-05-19
⚛️ quantum physics

Large-Scale Quantum Kernels for Hyperspectral Data Classification

This paper presents the first large-scale study demonstrating that fidelity-based quantum kernel support vector machines, accelerated by tensor network contraction and GPU techniques, achieve competitive or superior classification accuracy on high-dimensional hyperspectral data compared to state-of-the-art classical baselines without requiring extensive prior feature selection.

A. Delilbasic, A. Miroszewski, A. Wijata, J. Nalepa, J. Mielczarek, M. Riedel, G. Cavallaro2026-05-19