6146 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 study demonstrates that while connection-less sequential entanglement swapping suffers from significant performance penalties due to memory decoherence in current quantum hardware, these limitations are not fundamental and can be overcome as memory coherence times improve relative to entanglement heralding latencies.
This paper introduces FTPrimitiveBench, a systematic benchmarking suite that evaluates how logical quantum computing primitives interact with diverse, hardware-motivated noise models beyond the standard uniform depolarizing assumption, thereby enabling reproducible studies for hardware-aware fault-tolerant architecture co-design.
This paper establishes that any -qubit unitary can be implemented both approximately and exactly with a query or depth complexity of using Grover search-based reductions, while also proving a matching lower bound for these specific implementation classes.
This article presents a state-independent quotient procedure on the Cayley graph of the Clifford group to construct reduced graphs that precisely map the orbits of both stabilizer and non-stabilizer states under Clifford gate actions, thereby generalizing earlier reachability results and yielding deeper insights into state evolution.
This paper characterizes the most general quasi-free Markovian dynamical semigroups for finite-dimensional quantum-classical hybrid systems by providing a quantum generalization of the Lévy-Khintchine formula that unifies Gaussian and jump contributions, thereby enabling the continuous-time extraction of information from quantum systems through classical observations while elucidating the necessary role of dissipation in such interactions.
This paper introduces a novel tensor network-based approach to efficiently simulate the HHL algorithm in the qudit formalism, benchmarking its performance against exact inversion and Qiskit implementations while analyzing its sensitivity to hyperparameters to establish a noise-free upper bound for the algorithm's computational efficiency.
Using ab initio quantum Monte Carlo simulations, this study demonstrates that the long-time spatial density profile of a Lieb-Liniger gas following a geometric quench directly encodes the system's underlying Bethe rapidity distribution, thereby establishing ballistic expansion as a practical method for mapping momentum-space structures to real-space observables.
This paper presents a mathematically rigorous framework for the Markovian dynamics of quantum/classical hybrid systems using coupled stochastic differential equations, demonstrating that information flow from the quantum to the classical component necessitates dissipation and establishing a connection to a hybrid dynamical semigroup that unifies quantum and classical evolution equations.
This paper evaluates the applicability, feasibility, and scalability of quantum-inspired tensor network algorithms for industrial use cases by reviewing existing literature and analyzing potential applications alongside their inherent limitations.
This paper introduces a series of tensor network-based algorithms for anomaly detection that leverage Tensor Train data compression to preserve normal data structures while eliminating anomalous ones, demonstrating their effectiveness across digit, face, and cybersecurity datasets.