quant-ph papers | Gist.Science

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.

🔬 atomic physics

Operational criteria for quantum advantage in latency-constrained nonlocal games

This paper establishes a comprehensive framework for quantifying quantum advantage in latency-constrained distributed decision-making by incorporating realistic hardware constraints like finite operation times and entanglement rates, and proposes a time-multiplexed trapped-atom network architecture capable of meeting these stringent operational criteria for applications such as financial markets and power grids.

Changhao Li, Seigo Kikura, Akihisa Goban, Hayata Yamasaki, Shinichi Sunami2026-04-10

⚛️ quantum physics

Quantum Simulation of Collective Neutrino Oscillations using Dicke States

This paper proposes a new class of qubit-efficient quantum algorithms based on Dicke states and the $su(2)$ spin algebra to simulate collective neutrino oscillations in dense gases, demonstrating their superior performance on both classical and quantum hardware by fully exploiting the system's symmetries.

Katarina Bleau, Nikolina Ilic, Joachim Kopp, Ushak Rahaman, Xin Yue Yu2026-04-10

🔬 optics

Hybrid-2D Excitonic Metasurfaces for Complex Amplitude Modulation

This paper demonstrates a hybrid-2D excitonic metasurface platform using gate-tunable monolayer WS2 and inverse design to achieve independent, full-range control of both amplitude and phase in the visible spectrum, enabling reconfigurable wavefront shaping for applications like beam steering.

Tom Hoekstra, Mark L. Brongersma, Jorik van de Groep2026-04-10

⚛️ quantum physics

Critical Entanglement Dynamics at Dynamical Quantum Phase Transitions

This paper demonstrates that momentum-space entanglement entropy, when evaluated in the post-quench eigenbasis, serves as a robust, time-independent diagnostic for dynamical quantum phase transitions by exhibiting a maximal value of $\ln 2$ at critical times, whereas alternative bipartitions yield qualitatively different, time-dependent behaviors.

Kaiyuan Cao, Mingzhi Li, Xiang-Ping Jiang, Shu Chen, Jian Wang2026-04-10

⚛️ quantum physics

Quantum Simulation of Hyperbolic Equations and the Nonexistence of a Dirac Path Measure

This paper unifies two distinct perspectives to demonstrate that the absence of a well-defined probability measure for the Dirac equation in Minkowski space stems from a fundamental mathematical obstruction arising from both the distributional nature of its propagator and the indefinite signature of the metric, thereby precluding a classical stochastic path integral representation.

Sumita Datta2026-04-10

⚛️ quantum physics

Non-variational supervised quantum kernel methods: a review

This review provides a comprehensive analysis of non-variational supervised quantum kernel methods, examining their theoretical foundations, estimation techniques, and challenges while delineating the specific regimes and obstacles relevant to achieving genuine quantum advantage in machine learning.

John Tanner, Chon-Fai Kam, Jingbo Wang2026-04-10

⚛️ quantum physics

Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning

This paper presents a Double Deep-Q Network (DDQN) framework that automates the design of Variational Imaginary Time Evolution (VITE) circuits, successfully discovering non-intuitive structures with significantly reduced gate counts and depth compared to standard ansatz for solving Max-Cut and molecular hydrogen problems on NISQ devices.

Ryo Suzuki, Shohei Watabe2026-04-10

⚛️ high-energy theory

Leading low-temperature correction to the Heisenberg-Euler Lagrangian

This paper demonstrates that the leading low-temperature two-loop correction to the Heisenberg-Euler Lagrangian can be efficiently derived from its one-loop zero-temperature counterpart using real-time formalism, enabling the resummation of higher-loop contributions in the strong-field limit.

Felix Karbstein2026-04-10

⚛️ quantum physics

Orthogonalised Self-Guided Quantum Tomography: Insights from Single-Pixel Imaging

This paper introduces orthogonalised self-guided quantum tomography by establishing its mathematical equivalence to single-pixel imaging and demonstrating that this approach significantly improves convergence speed and fidelity without requiring additional experimental overhead.

Kiki Dekkers, Alice Ruget, Fazilah Nothlawala, Sabrina Henry, Stirling Scholes, Miles Padgett, Andrew Forbes, Isaac Nape (…)2026-04-10

⚛️ quantum physics

Divide et impera: hybrid multinomial classifiers from quantum binary models

This paper investigates hybrid strategies for combining quantum binary models into multinomial classifiers, demonstrating that a binary decision tree approach offers a cost-effective solution with logarithmic overhead while maintaining accuracy comparable to other methods like one-vs-one and one-vs-rest.

Simone Roncallo, Angela Rosy Morgillo, Seth Lloyd, Chiara Macchiavello, Lorenzo Maccone2026-04-10

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