107 citations · 541 across the 26 of their papers we have counts for
26 papers
Generative Circuit Design for Quantum-Selected Configuration Interaction
Ryota Kemmoku, Qi Gao, Shu Kanno +4
Quantum-selected configuration interaction (QSCI) has emerged as a feasible approach for approximating electronic ground states on noisy quantum devices toward large-system demonst…
Auger Spectroscopy via Generative Quantum Eigensolver: A Quantum Approach to Molecular Excitations
Kimberlee Keithley, Shunsuke Yamamoto, Ryota Kenmoku +16
Auger electron spectroscopy, a way of characterizing electronic structure through core-level decay processes, is widely used in materials characterization; however direct calculati…
Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
Luca Thiede, Abdulrahman Aldossary, Andreas Burger +9
Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupl…
Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays
Kouhei Nakaji, Jonathan Wurtz, Haozhe Huang +4
We introduce the "quantum circuit daemon" (QC-Daemon), a reinforcement learning agent for compiling quantum device operations aimed at efficient quantum hardware execution. We appl…
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver
Shunya Minami, Kouhei Nakaji, Yohichi Suzuki +2
Quantum computing is entering a transformative phase with the emergence of logical quantum processors, which hold the potential to tackle complex problems beyond classical capabili…
Artificial Intelligence for Quantum Computing
Yuri Alexeev, Marwa H. Farag, Taylor L. Patti +25
Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extend…
Quantum Transformer: Accelerating model inference via quantum linear algebra
Naixu Guo, Zhan Yu, Matthew Choi +5
Powerful generative artificial intelligence from large language models (LLMs) harnesses extensive computational resources for inference. In this work, we investigate the transforme…
The generative quantum eigensolver (GQE) and its application for ground state search
Kouhei Nakaji, Lasse Bjørn Kristensen, Ryota Kemmoku +14
We introduce the generative quantum eigensolver (GQE), a new quantum computational framework that operates outside the variational quantum algorithm paradigm by applying classical…
Fast quantum algorithm for differential equations
Mohsen Bagherimehrab, Kouhei Nakaji, Nathan Wiebe +3
Partial differential equations (PDEs) are ubiquitous in science and engineering. Prior quantum algorithms for solving the system of linear algebraic equations obtained from discret…
Machine learning of measurement schemes for efficient quantum observable estimation
Zi-Jian Zhang, Kouhei Nakaji, Matthew Choi +1
Estimation of the expectation value of observables is a key subroutine in quantum computing and is also the bottleneck of the performance of many near-term quantum algorithms. Many…
Expressive Quantum Supervised Machine Learning using Kerr-nonlinear Parametric Oscillators
Yuichiro Mori, Kouhei Nakaji, Yuichiro Matsuzaki +1
Quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era. Recen…
qSWIFT: High-order randomized compiler for Hamiltonian simulation
Kouhei Nakaji, Mohsen Bagherimehrab, Alan Aspuru-Guzik
Hamiltonian simulation is known to be one of the fundamental building blocks of a variety of quantum algorithms such as its most immediate application, that of simulating many-body…
Approximate complex amplitude encoding algorithm and its application to data classification problems
Naoki Mitsuda, Tatsuhiro Ichimura, Kouhei Nakaji +6
Quantum computing has a potential to accelerate the data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The…
Deterministic and random features for large-scale quantum kernel machine
Kouhei Nakaji, Hiroyuki Tezuka, Naoki Yamamoto
Quantum machine learning (QML) is the spearhead of quantum computer applications. In particular, quantum neural networks (QNN) are actively studied as the method that works both in…
Measurement optimization of variational quantum simulation by classical shadow and derandomization
Kouhei Nakaji, Suguru Endo, Yuichiro Matsuzaki +1
Simulating large quantum systems is the ultimate goal of quantum computing. Variational quantum simulation (VQS) gives us a tool to achieve the goal in near-term devices by distrib…
Overfitting in quantum machine learning and entangling dropout
Masahiro Kobayashi, Kouhei Nakaji, Naoki Yamamoto
The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model functi…
Grover search revisited; application to image pattern matching
Hiroyuki Tezuka, Kouhei Nakaji, Takahiko Satoh +1
The landmark Grover algorithm for amplitude amplification serves as an essential subroutine in various type of quantum algorithms, with guaranteed quantum speedup in query complexi…
Quantum-enhanced neural networks in the neural tangent kernel framework
Kouhei Nakaji, Hiroyuki Tezuka, Naoki Yamamoto
Recently, quantum neural networks or quantum-classical neural networks (qcNN) have been actively studied, as a possible alternative to the conventional classical neural network (cN…
Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicator
Kouhei Nakaji, Shumpei Uno, Yohichi Suzuki +6
Efficient methods for loading given classical data into quantum circuits are essential for various quantum algorithms. In this paper, we propose an algorithm called Approximate Amp…
Quantum semi-supervised generative adversarial network for enhanced data classification
Kouhei Nakaji, Naoki Yamamoto
In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier…
Expressibility of the alternating layered ansatz for quantum computation
Kouhei Nakaji, Naoki Yamamoto
The hybrid quantum-classical algorithm is actively examined as a technique applicable even to intermediate-scale quantum computers. To execute this algorithm, the hardware efficien…
Faster Amplitude Estimation
Kouhei Nakaji
In this paper, we introduce an efficient algorithm for the quantum amplitude estimation task which works in noisy intermediate-scale quantum(NISQ) devices. The quantum amplitude es…
Identifying the Origin of Longevity of Metastable Stau at the LHC
Takumi Ito, Kouhei Nakaji, Satoshi Shirai
In the framework of the supersymmetric standard model, the lighter stau often becomes long-lived. Such longevity of the stau is realized in three well-motivated scenarios: (A) the…
LHC signature with long-lived stau in high reheating temperature scenario
Motoi Endo, Koichi Hamaguchi, Kouhei Nakaji
We study the possibility of observing the stau signal at LHC in case that the gravitino is the lightest supersymmetric particle and the stau is the next lightest supersymmetric par…