211 citations · 992 across the 43 of their papers we have counts for
43 papers
Symmetries and overparametrization properties of Hamiltonian variational ansatzes for the d lattice gauge theory
Kanta Yamanaka, Takanori Daiza, Katsumi Imaizumi +4
We perform detailed studies of five Hamiltonian variational ansatzes (HVA) based on the Hamiltonian of the d lattice gauge theory. The ansatzes are designed t…
Double Descent in Quantum Kernel Ridge Regression
Kensuke Kamisoyama, Lento Nagano, Koji Terashi
Various classical machine learning models, including linear regression, kernel methods, and deep neural networks, exhibit double descent, in which the test risk peaks near the inte…
Comprehensive Numerical Studies of Barren Plateau and Overparametrization in Variational Quantum Algorithm
Himuro Hashimoto, Akio Nakabayashi, Lento Nagano +4
The variational quantum algorithm (VQA) with a parametrized quantum circuit is widely applicable to near-term quantum computing, but its fundamental issues that limit optimization…
Search for Dark Photon Dark Matter with a Mass around 36.1 μeV Using a Frequency-tunable Cavity Controlled through a Coupled Superconducting Qubit
Kan Nakazono, Shion Chen, Hajime Fukuda +10
We report the results of a search for dark photon dark matter using a cavity that employs a transmon qubit as a frequency tuner. The tuning mechanism utilizes the energy level shif…
Quantum decision trees with information entropy
Zhelun Li, Koji Terashi
We present a classification algorithm for quantum states, inspired by decision-tree methods. To adapt the decision-tree framework to the probabilistic nature of quantum measurement…
Qudit-Generalization of the Qubit Echo and Its Application to a Qutrit-Based Toffoli Gate
Yutaro Iiyama, Wonho Jang, Naoki Kanazawa +3
The fidelity of certain gates on noisy quantum computers may be improved when they are implemented using more than two levels of the involved transmons. The main impediments to ach…
Enforcing exact permutation and rotational symmetries in the application of quantum neural network on point cloud datasets
Zhelun Li, Lento Nagano, Koji Terashi
Recent developments in the field of quantum machine learning have promoted the idea of incorporating physical symmetries in the structure of quantum circuits. A crucial milestone i…
Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
Alberto Di Meglio, Karl Jansen, Ivano Tavernelli +43
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage…
Quantum data learning for quantum simulations in high-energy physics
Lento Nagano, Alexander Miessen, Tamiya Onodera +3
Quantum machine learning with parametrised quantum circuits has attracted significant attention over the past years as an early application for the era of noisy quantum processors.…
Resolution enhancement of one-dimensional molecular wavefunctions in plane-wave basis via quantum machine learning
Rei Sakuma, Yutaro Iiyama, Lento Nagano +2
Super-resolution is a machine-learning technique in image processing which generates high-resolution images from low-resolution images. Inspired by this approach, we perform a nume…
Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer
Wonho Jang, Koji Terashi, Masahiko Saito +5
There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivity, and coherence times, a quantum circuit optimization is essenti…
The Phase-I Trigger Readout Electronics Upgrade of the ATLAS Liquid Argon Calorimeters
G. Aad, A. V. Akimov, K. Al Khoury +195
The Phase-I trigger readout electronics upgrade of the ATLAS Liquid Argon calorimeters enhances the physics reach of the experiment during the upcoming operation at increasing Larg…
Measurement-Free Ultrafast Quantum Error Correction by Using Multi-Controlled Gates in Higher-Dimensional State Space
Toshiaki Inada, Wonho Jang, Yutaro Iiyama +4
Quantum error correction is a crucial step beyond the current noisy-intermediate-scale quantum device towards fault-tolerant quantum computing. However, most of the error correctio…
Event Classification with Multi-step Machine Learning
Masahiko Saito, Tomoe Kishimoto, Yuya Kaneta +6
The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single lar…
Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications
Wonho Jang, Koji Terashi, Masahiko Saito +7
There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to ma…
An Improvement of Object Detection Performance using Multi-step Machine Learnings
Tomoe Kishimoto, Masahiko Saito, Junichi Tanaka +3
Connecting multiple machine learning models into a pipeline is effective for handling complex problems. By breaking down the problem into steps, each tackled by a specific componen…
Quantum Machine Learning in High Energy Physics
Wen Guan, Gabriel Perdue, Arthur Pesah +4
Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early…
Event Classification with Quantum Machine Learning in High-Energy Physics
Koji Terashi, Michiru Kaneda, Tomoe Kishimoto +3
We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applicat…
Studying gaugino masses in supersymmetric model at future 100 TeV collider
Shoji Asai, So Chigusa, Toshiaki Kaji +6
We discuss prospects of studying supersymmetric model at future circular collider (FCC) with its centre-of-mass energy of . We pay particular attention to…
Discovery reach for wino and higgsino dark matter with a disappearing track signature at a 100 TeV collider
Masahiko Saito, Ryu Sawada, Koji Terashi +1
Within the theory of supersymmetry, the lightest neutralino is a dark matter candidate and is often assumed to be the lightest supersymmetric particle (LSP) as well. If the neutral…
HE-LHC prospects for diboson resonance searches and electroweak WW/WZ production via vector boson scattering in the semi-leptonic final states
V. Cavaliere, R. Les, T. Nitta +1
This note presents the prospects of searches for new heavy resonances decaying to diboson (WW) and measurements of electroweak WW/WZ production via vector boson scattering (VBS) in…
Beyond the Standard Model Physics at the HL-LHC and HE-LHC
X. Cid Vidal, M. D'Onofrio, P. J. Fox +294
This is the third out of five chapters of the final report [1] of the Workshop on Physics at HL-LHC, and perspectives on HE-LHC [2]. It is devoted to the study of the potential, in…
LHC Dark Matter Working Group: Next-generation spin-0 dark matter models
Tomohiro Abe, Yoav Afik, Andreas Albert +50
Dark matter (DM) simplified models are by now commonly used by the ATLAS and CMS Collaborations to interpret searches for missing transverse energy (). The coher…
Measurement of the azimuthal angle distribution of leptons from W boson decays as a function of the W transverse momentum in proton-antiproton collisions at sqrt(s)=1.8 TeV
D. Acosta
We present the first measurement of the A_2 and A_3 angular coefficients of the W boson produced in proton-antiproton collisions. We study W-> e+nu and W-> mu+nu candidate events p…