57 citations · 72 across the 12 of their papers we have counts for
12 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…
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…
Improving initial-state-dependent quantum circuit optimization by introducing state labels
Toshiaki Kaji, Koji Terashi, Ryu Sawada
While the capabilities of quantum hardware have significantly advanced in recent years, executing quantum algorithms as quantum circuits at the lowest possible cost remains crucial…
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…
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…
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…
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…
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…