⚛️ Category

quant-ph

11028 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.

⚛️ high-energy experiments

Comparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data

This study systematically compares classical and quantum machine learning architectures for regression on simulated high-energy physics collision data, finding that while classical models like CNNs and LSTMs currently offer marginally better performance, quantum counterparts achieve competitive accuracy with significantly fewer trainable parameters, highlighting a distinct parameter-efficiency advantage for near-term quantum devices.

Tariq Mahmood, Zain ul Abidin, Itzel Luviano Soto, Alfredo Raya2026-08-31
⚛️ quantum physics

A quantum generative model for in silico clinical trials using scarce training datasets

This paper proposes and validates a quantum generative model pipeline that effectively synthesizes high-fidelity *in silico* patients from scarce clinical datasets, demonstrating superior generalization and expressivity compared to classical baselines using real-world Myelodysplastic Syndrome data on an IBM quantum computer.

Olatz Sanz Larrarte, Reza Dastbasteh, Roberto Sanchez-Navarro, Maria Diez-Campelo, Felipe Prosper, Ana Alfonso-Pierola (…)2026-08-31
⚛️ quantum physics

Variationally Optimized Imaginary-time Polynomial Filters for Ground State Projection

This paper introduces a variational imaginary-time evolution framework using optimized polynomial filters derived from an operator-level action principle, which significantly enhances the accuracy, stability, and success probability of ground-state preparation on near-term quantum devices compared to standard Taylor-Trotter-Suzuki approaches.

Bahman Seifi, Ibsal Assi, J. P. F. LeBlanc2026-08-31
⚛️ quantum physics

From quantum reservoirs to quantum extreme learning machines through a nearest-neighbor spin chain with tunable quantum memory

This paper demonstrates that Quantum Reservoir Computing and Quantum Extreme Learning Machines are two limits of a unified architecture connected by input-encoding length, revealing that optimal temporal processing occurs at the edge of chaos regardless of connectivity, while the necessity of recurrent memory depends on the required historical depth of the task.

Carlos Ramon-Escandell, Arnau Riera, Marcin Płodzień2026-08-31
🔬 mesoscale physics

Fabrication-free assessment of microwave losses in germanium-based dielectrics and superconductors

This paper introduces a fabrication-free flip-chip sensing technique to rapidly evaluate microwave losses in germanium-based dielectrics and superconductors, revealing that strain-engineered Ge/SiGe heterostructures are promising for quantum circuits while unmodified PtSiGe films suffer from high losses that can be significantly mitigated by coating with niobium.

Haoran Lu, Kushagra Aggarwal, Xiangqin Wang, Pauline Drexler, Daniel Tong, Maciej W. Olszewski, Anand Ithepalli, Lingda (…)2026-08-31