6391 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.
This paper demonstrates that combining Sample-based Quantum Diagonalization (SQD) with Density Matrix Embedding Theory (DMET) enables accurate, chemically precise ground-state energy calculations for complex, low-symmetry ligand-like molecules on IBM's Eagle R3 quantum hardware by effectively managing subsystem-dependent entanglement variations.
This paper presents a conceptually and technically simpler proof of an unconditional classical oracle separation between and , while also establishing the first such separation between and by leveraging the code intersection problem combined with codes possessing strong list-recovery properties.
The paper proposes a hybrid quantum-classical algorithm called Quantum-accelerated Conjugate Gradient (QACG), which utilizes a fault-tolerant quantum device solely to generate a spectrally informed initial guess for a classical solver, thereby achieving runtime advantages for large-scale linear systems with significantly reduced quantum resource requirements compared to fully quantum approaches.
This paper proposes a scalable, data-driven quantum state tomography method using multilayer perceptron networks that effectively mitigates unknown experimental noise without requiring explicit assumptions about the noise model or measurement setup.
This paper establishes a unified framework connecting non-Hermitian Zeeman quantum geometry, local Dirac-node topology, and measurable transport signatures by demonstrating that the Zeeman quantum geometric tensor decomposes into normal and anomalous sectors, where the latter reveals a novel curvature-flux representation of local topology and distinct linear response scalings.
This paper introduces a rigorous, non-asymptotic quantum state tomography protocol for distributed quantum computers that utilizes local operations and classical communication to derive explicit trace-norm error bounds without assuming remote entanglement as a primitive resource.
This paper presents a comprehensive framework for modeling complex systems of oscillating and decaying neutrinos using open quantum system methods, specifically demonstrating that the Lindblad master equation, Liouvillian superoperator, and Kraus operators offer a fully general and numerically superior approach compared to traditional differential equation solutions.
This paper presents a resist-free Josephson junction fabrication method using etched silicon trenches that eliminates chemical contamination, achieves median energy relaxation times up to 184 microseconds, and offers a CMOS-compatible, scalable alternative to traditional polymer-masked processes for superconducting qubits.
The paper claims to resolve the long-standing Einstein-Podolsky-Rosen paradox by pinpointing its specific origin within the original chain of reasoning, despite the paradox having been historically cemented by Bell experiments.
This paper introduces a scalable and verifiable family of planted-solution benchmarks for SAT solvers and Ising optimization, derived from integer factorization constraints, which exhibit exponential runtime growth relative to the bit-length of the factors.