Caleb Jordan, Jacob Bernhardt, Joseph Rahamim, Alex Kirichenko, Karthik Bharadwaj, Louis Fry-Bouriaux, Aaron Somoroff, Katie Porsch, Kan-Ting Tsai, Jason Walter, Adam Weis, Meng-Ju Yu, Mario Renzullo, Jerome Javelle, Chris Checkley, Oleg Mukhanov, Daniel Yohannes, Igor Vernik, Shu-Jen Han • Published: 2025-03-12
Current superconducting quantum computing platforms face significant scaling challenges, as individual signal lines are required for control of each qubit. This wiring overhead is a result of the low level of integration between control electronics at room temperature and qubits operating at millikelvin temperatures, which raise serious doubts among technologists about whether utility-scale quantu...
Hongdong Zhu, Qi Gao, Yin Ma, Shaobo Chen, Haixu Liu, Fengao Wang, Tinglan Wang, Chang Wu, Kai Wen • Published: 2026-02-22
This paper introduces the Kaiwu-PyTorch-Plugin (KPP) to bridge Deep Learning and Photonic Quantum Computing across multiple dimensions. KPP integrates the Coherent Ising Machine into the PyTorch ecosystem, addressing classical inefficiencies in Energy-Based Models. The framework facilitates quantum integration in three key aspects: accelerating Boltzmann sampling, optimizing training data via Acti...
Mohammed Barhoush, Tomoyuki Morimae, Ryo Nishimaki, Takashi Yamakawa • Published: 2026-02-20
Mahadev [SIAM J. Comput. 2022] introduced the first protocol for classical verification of quantum computation based on the Learning-with-Errors (LWE) assumption, achieving a 4-message interactive scheme. This breakthrough naturally raised the question of whether fewer messages are possible in the plain model. Despite its importance, this question has remained unresolved.
In this work, we prove ...
James W. Gardner, Federico Belliardo, Gideon Lee, Tuvia Gefen, Liang Jiang • Published: 2026-02-19
Quantum metrology of an incoherent signal is a canonical sensing problem related to superresolution and noise spectroscopy. We show that quantum computing can accelerate searches for a weak incoherent signal when the signal and noise are not precisely known. In particular, we consider weak Schur sampling, density matrix exponentiation, and quantum signal processing for testing the rank, purity, an...
Jacques Carette, Chris Heunen, Robin Kaarsgaard, Neil J. Ross, Amr Sabry • Published: 2026-02-18
Quantum computing improves substantially on known classical algorithms for various important problems, but the nature of the relationship between quantum and classical computing is not yet fully understood. This relationship can be clarified by free models, that add to classical computing just enough physical principles to represent quantum computing and no more. Here we develop an axiomatisation ...
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 ...