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Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization…

Recent advances in classical simulation of Clifford+T circuits make use of the ZX calculus to iteratively decompose and simplify magic states into stabiliser terms. We improve on this method by studying stabiliser decompositions of ZX…

Quantum Physics · Physics 2025-09-23 Mark Koch , Richie Yeung , Quanlong Wang

We introduce a method for solving the Max-Cut problem using a variational algorithm and a continuous-variables quantum computing approach. The quantum circuit consists of two parts: the first one embeds a graph into a circuit using the…

Quantum Physics · Physics 2019-06-18 Michał Stęchły , Ntwali Bashige , Przemysław Chojecki

The Quantum Approximate Optimization Algorithm (QAOA) requires considered optimization problems to be translated into a compatible format. A popular transformation step in this pipeline involves the quadratization of higher-order binary…

Quantum Physics · Physics 2025-11-26 Damian Rovara , Lukas Burgholzer , Robert Wille

X-ray absorption spectroscopy (XAS) is a leading technique for understanding structural changes in advanced battery materials such as lithium-excess cathodes. However, extracting critical information like oxidation states from the…

This letter introduces a novel channel coding design framework for short-length codewords that permits balancing the tradeoff between the bit error rate floor and waterfall region by modifying a single real-valued parameter. The proposed…

Information Theory · Computer Science 2016-11-17 Mikel Hernaez , Pedro M. crespo , Javier Del Ser

Variational quantum algorithms have emerged as a cornerstone of contemporary quantum algorithms research. Practical implementations of these algorithms, despite offering certain levels of robustness against systematic errors, show a decline…

Efficient parametrizations of quantum states are essential for trainable hybrid classical-quantum algorithms. A key challenge in their design consists in adapting to the available qubit connectivity of the quantum processor, which limits…

Quantum Physics · Physics 2026-04-15 Teodor Parella-Dilmé , Jakob S. Kottmann , Antonio Acín

Optimizing quantum circuits by reducing circuit depth is essential for improving the efficiency and scalability of quantum algorithms, particularly as quantum hardware continues to evolve. This can be achieved by restructuring quantum…

Quantum Physics · Physics 2026-05-07 Folkert de Ronde , Stephan Wong , Sebastian Feld

In the paper, we consider quantum circuits for the Quantum Fourier Transform (QFT) algorithm. The QFT algorithm is a very popular technique used in many quantum algorithms. We present a generic method for constructing quantum circuits for…

Quantum Physics · Physics 2026-01-05 Kamil Khadiev , Aliya Khadieva , Vadim Sagitov , Kamil Khasanov

With sub-threshold quantum error correction on quantum hardware still out of reach, quantum error mitigation methods are currently deemed an attractive option for implementing certain applications on near-term noisy quantum devices. One…

Quantum Physics · Physics 2024-03-01 Wenbo Shi , Robert Malaney

Variational quantum circuits (VQCs) are typically evaluated at the logical design level when analyzing trainability. However, execution on real quantum devices requires hardware-aware compilation (transpilation) to satisfy qubit…

Quantum Physics · Physics 2026-04-21 Muhammad Kashif , Muhammad Shafique

We propose and validate on real quantum computing hardware a new method for extended two-qubit gate set design, replacing iterative, fine calibration with fast characterization of a small number of gate parameters which are then tracked and…

Implementing a quantum circuit on specific hardware with a reduced available gate set is often associated with a substantial increase in the length of the equivalent circuit. This process is also known as transpilation and due to…

Quantum Physics · Physics 2025-02-21 Bodo Rosenhahn , Tobias J. Osborne , Christoph Hirche

Predicting the optimum SWAP depth of a quantum circuit is useful because it informs the compiler about the amount of necessary optimization. Fast prediction methods will prove essential to the compilation of practical quantum circuits. In…

Quantum Physics · Physics 2021-06-29 Evan E. Dobbs , Robert Basmadjian , Alexandru Paler , Joseph S. Friedman

X-ray absorption spectroscopy is a crucial experimental technique for elucidating the mechanisms of structural degradation in battery materials. However, extracting information from the measured spectrum is challenging without high-quality…

Quantum variational circuits have gained significant attention due to their applications in the quantum approximate optimization algorithm and quantum machine learning research. This work introduces a novel class of classical probabilistic…

Quantum Physics · Physics 2025-09-17 Gal Weitz , Lirandë Pira , Chris Ferrie , Joshua Combes

Distributed quantum computing (DQC) connects many small quantum processors into a single logical machine, offering a practical route to scalable quantum computation. However, most existing DQC paradigms are structure-agnostic. Circuit…

Quantum Physics · Physics 2026-03-10 Yuwen Huang , Xiaojun Lin , Bin Luo , John C. S. Lui

The scalability of neutral-atom quantum computing is increasingly limited by a compiler--architecture challenge: logical circuits must be mapped onto dynamically reconfigurable atom arrays while controlling crosstalk, transport overhead,…

Quantum Physics · Physics 2026-05-25 Chen Huang , Xi Zhao , Hongze Xu , Weifeng Zhuang , Meng-Jun Hu , Dong E. Liu , Jingbo Wang

Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manually designed…

Neural and Evolutionary Computing · Computer Science 2026-02-04 Devroop Kar , Daniel Krutz , Travis Desell