Gerhard Stenzel, Tobias Rohe, Michael Kölle, Leo Sünkel, Jonas Stein, Claudia Linnhoff-Popien • Published: 2026-02-18
Variational Quantum Circuits (VQCs) have emerged as a promising paradigm for quantum machine learning in the NISQ era. While parameter sharing in VQCs can reduce the parameter space dimensionality and potentially mitigate the barren plateau phenomenon, it introduces a complex trade-off that has been largely overlooked. This paper investigates how parameter sharing, despite creating better global o...
Yuanjie Ren, Jinzheng Li, Yidi Qi • Published: 2026-02-18
We introduce MerLean, a fully automated agentic framework for autoformalization in quantum computation. MerLean extracts mathematical statements from \LaTeX{} source files, formalizes them into verified Lean~4 code built on Mathlib, and translates the result back into human-readable \LaTeX{} for semantic review. We evaluate MerLean on three theoretical quantum computing papers producing 2,050 Lean...
Zhichen Liu, Richard A. Klemm • Published: 2026-02-11
Modern experimental techniques can generate magnetic fields of the form H(t) = H0 z-hat + H1 [x-hat cos(ωt) + y-hat sin(ωt)], at frequencies within an order of magnitude of the nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) frequencies, ωn0 and ωe0, respectively, when acting on atoms or molecules. We derive simple closed-form expressions for the exact nuclear- and elect...
Eleftherios Mastorakis, Muhammad Umer, Milena Guevara-Bertsch, Juris Ulmanis, Felix Rohde, Dimitris G. Angelakis • Published: 2025-07-25
Resource-efficient, low-depth implementations of quantum circuits remain a promising strategy for achieving reliable and scalable computation on quantum hardware, as they reduce gate resources and limit the accumulation of noisy operations. Here, we propose a low-depth implementation of a class of Hadamard test circuits, complemented by the development of a parameterized quantum ansatz specificall...
Etienne Granet, Sheng-Hsuan Lin, Kevin Hémery, Reza Haghshenas, Pablo Andres-Martinez, David T. Stephen, Anthony Ransford, Jake Arkinstall, M. S. Allman, Pete Campora, Samuel F. Cooper, Robert D. Delaney, Joan M. Dreiling, Brian Estey, Caroline Figgatt, Cameron Foltz, John P. Gaebler, Alex Hall, Ali Husain, Akhil Isanaka, Colin J. Kennedy, Nikhil Kotibhaskar, Ivaylo S. Madjarov, Michael Mills, Alistair R. Milne, Annie J. Park, Adam P. Reed, Brian Neyenhuis, Justin G. Bohnet, Michael Foss-Feig, Andrew C. Potter, Ramil Nigmatullin, Mohsin Iqbal, Henrik Dreyer • Published: 2025-11-03
The Fermi-Hubbard model is the starting point for the simulation of many strongly correlated materials, including high-temperature superconductors, whose modelling is a key motivation for the construction of quantum simulation and computing devices. However, the detection of superconducting pairing correlations has so far remained out of reach, both because of their off-diagonal character - which ...
Sara Tarquini, Matteo Vandelli, Francesco Ferrari, Daniele Dragoni, Francesco Tudisco • Published: 2026-02-17
Quantum architectures based on neutral atoms have gained significant attention in recent years as specialized computational machines due to their ability to directly encode the independent set constraint on graphs, exploiting the Rydberg blockade mechanism. In this work, we address the Drone Delivery Packing Problem via a hybrid quantum-classical framework leveraging a neutral-atom quantum process...
Asma Taheri Monfared, Andrea Bombarda, Angelo Gargantini, Majid Haghparast • Published: 2026-02-17
The growing complexity of healthcare systems requires advanced computational models for real-time monitoring, secure data exchange, and intelligent decision-making. Digital Twins (DTs) provide virtual representations of physical healthcare entities, enabling continuous patient monitoring and personalized care. However, classical DT frameworks face limitations in scalability, computational efficien...
Liam Doyle, Fargol Seifollahi, Chandralekha Singh • Published: 2026-02-16
The rapid growth of quantum information science and technology (QIST) in the 21st century has created both excitement and uncertainty about the field's trajectory. This qualitative study presents perspectives from leading quantum researchers, who are educators, on fundamental questions frequently posed by students, the public, and the media regarding QIST. Through in-depth interviews, we explored ...
Chris Fields, James F. Glazebrook, Antonino Marcianò, Emanuele Zappala • Published: 2025-09-24
We study the relationship between computation and scattering both operationally (hence phenomenologically) and formally. We show how topological quantum neural networks (TQNNs) enable universal quantum computation, using the Reshetikhin-Turaev and Turaev-Viro models to show how TQNNs implement quantum error-correcting codes. We then exhibit a formal correspondences between TQNNs and amplituhedra t...
Thibaud Louvet, Thomas Ayral, Xavier Waintal • Published: 2023-06-05
Quantum chemistry is envisioned as an early and disruptive application for quantum computers. Yet, closer scrutiny of the proposed algorithms shows that there are considerable difficulties along the way. Here, we propose two criteria for evaluating two leading quantum approaches for finding the ground state of molecules. The first criterion applies to the variational quantum eigensolver (VQE) algo...