activity
20202026
most citedQuantum error mitigation as a universal error-minimization technique: applications from NISQ to FTQC eras

138 citations · 349 across the 25 of their papers we have counts for

collaborators

27 papers

quant-ph2026

Low-Depth Initial-State Preparation for Ground-State Energy Estimation of Two-Dimensional Strongly Correlated Systems

Ryo Watanabe, Keisuke Fujii

Quantum algorithms for ground-state energy estimation require initial states with non-negligible fidelity to the ground state. Guided by classical simulations, we design shallow ci…

quant-ph2023★ 4 cited

Classical variational optimization of PREPARE circuit for quantum phase estimation of quantum chemistry Hamiltonians

Hayata Morisaki, Kosuke Mitarai, Keisuke Fujii +1

We propose a method for constructing circuits for quantum phase estimation of a molecular Hamiltonian in quantum chemistry by using variational optimization of q…

quant-ph2023

Algorithmic error mitigation for quantum eigenvalues estimation

Adam Siegel, Kosuke Mitarai, Keisuke Fujii

When estimating the eigenvalues of a given observable, even fault-tolerant quantum computers will be subject to errors, namely algorithmic errors. These stem from approximations in…

quant-ph2023

Quantum error correction with an Ising machine under circuit-level noise

Jun Fujisaki, Kazunori Maruyama, Hirotaka Oshima +4

Efficient decoding to estimate error locations from outcomes of syndrome measurement is the prerequisite for quantum error correction. Decoding in presence of circuit-level noise i…

quant-ph2023★ 24 cited

A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes?

Tsubasa Ichikawa, Hideaki Hakoshima, Koji Inui +18

Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring…

quant-ph2023★ 7 cited

Measuring Trotter error and its application to precision-guaranteed Hamiltonian simulations

Tatsuhiko N. Ikeda, Hideki Kono, Keisuke Fujii

Trotterization is the most common and convenient approximation method for Hamiltonian simulations on digital quantum computers, but estimating its error accurately is computational…

quant-ph2023★ 11 cited

Experimental demonstration of a high-fidelity virtual two-qubit gate

Akhil Pratap Singh, Kosuke Mitarai, Yasunari Suzuki +4

We experimentally demonstrate a virtual two-qubit gate and characterize it using quantum process tomography~(QPT). The virtual two-qubit gate decomposes an actual two-qubit gate in…

quant-ph2023

MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era

Leonardo Placidi, Ryuichiro Hataya, Toshio Mori +4

We introduce the first large-scale dataset, MNISQ, for both the Quantum and the Classical Machine Learning community during the Noisy Intermediate-Scale Quantum era. MNISQ consists…

quant-ph2023★ 11 cited

QAOA-MC: Markov chain Monte Carlo enhanced by Quantum Alternating Operator Ansatz

Yuichiro Nakano, Hideaki Hakoshima, Kosuke Mitarai +1

Quantum computation is expected to accelerate certain computational task over classical counterpart. Its most primitive advantage is its ability to sample from classically intracta…

quant-ph2023★ 5 cited

Variational quantum eigensolver with embedded entanglement using a tensor-network ansatz

Ryo Watanabe, Keisuke Fujii, Hiroshi Ueda

In this paper, we introduce a tensor network (TN) scheme into the entanglement augmentation process of the synergistic optimization framework by Rudolph et al. [arXiv:2208.13673] t…

quant-ph2023

A Polynomial Time Quantum Algorithm for Exponentially Large Scale Nonlinear Differential Equations via Hamiltonian Simulation

Yu Tanaka, Keisuke Fujii

Quantum computers have the potential to efficiently solve a system of nonlinear ordinary differential equations (ODEs), which play a crucial role in various industries and scientif…

quant-ph2023

Recursive Quantum Eigenvalue/Singular-Value Transformation: Analytic Construction of Matrix Sign Function by Newton Iteration

Kaoru Mizuta, Keisuke Fujii

Quantum eigenvalue transformation (QET) and its generalization, quantum singular value transformation (QSVT), are versatile quantum algorithms that allow us to apply broad matrix f…

quant-ph2023★ 40 cited

Partially Fault-tolerant Quantum Computing Architecture with Error-corrected Clifford Gates and Space-time Efficient Analog Rotations

Yutaro Akahoshi, Kazunori Maruyama, Hirotaka Oshima +2

Quantum computers are expected to bring drastic acceleration to several computing tasks against classical computers. Noisy intermediate-scale quantum (NISQ) devices, which have ten…

quant-ph2023★ 10 cited

Ising model formulation for highly accurate topological color codes decoding

Yugo Takada, Yusaku Takeuchi, Keisuke Fujii

Quantum error correction is an essential ingredient for reliable quantum computation for theoretically provable quantum speedup. Topological color codes, one of the quantum error c…

quant-ph2023★ 3 cited

VQE-generated quantum circuit dataset for machine learning

Akimoto Nakayama, Kosuke Mitarai, Leonardo Placidi +2

Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical prob…

quant-ph2022

Quantum-inspired algorithm applied to extreme learning

Iori Takeda, Souichi Takahira, Kosuke Mitarai +1

Quantum-inspired singular value decomposition (SVD) is a technique to perform SVD in logarithmic time with respect to the dimension of a matrix, given access to the matrix embedded…

quant-ph2022★ 15 cited

Optimal Hamiltonian simulation for time-periodic systems

Kaoru Mizuta, Keisuke Fujii

The implementation of time-evolution operators , called Hamiltonian simulation, is one of the most promising usage of quantum computers. For time-independent Hamiltonians, qu…

quant-ph2022★ 1 cited

Pricing multi-asset derivatives by variational quantum algorithms

Kenji Kubo, Koichi Miyamoto, Kosuke Mitarai +1

Pricing a multi-asset derivative is an important problem in financial engineering, both theoretically and practically. Although it is suitable to numerically solve partial differen…

quant-ph2022★ 10 cited

Fast Classical Simulation of Hamiltonian Dynamics by Simultaneous Diagonalization Using Clifford Transformation with Parallel Computation

Yoshiaki Kawase, Keisuke Fujii

Simulating quantum many-body dynamics is important both for fundamental understanding of physics and practical applications for quantum information processing. Therefore, classical…

quant-ph2022★ 16 cited

Simulation and performance analysis of quantum error correction with a rotated surface code under a realistic noise model

Mitsuki Katsuda, Kosuke Mitarai, Keisuke Fujii

The demonstration of quantum error correction (QEC) is one of the most important milestones in the realization of fully-fledged quantum computers. Toward this, QEC experiments usin…

quant-ph2022★ 31 cited

Local variational quantum compilation of a large-scale Hamiltonian dynamics

Kaoru Mizuta, Yuya O. Nakagawa, Kosuke Mitarai +1

Implementing time evolution operators on quantum circuits is important for quantum simulation. However, the standard way, Trotterization, requires a huge numbers of gates to achiev…

quant-ph2022

A Practical and Scalable Decoder for Topological Quantum Error Correction with Digital Annealer

Jun Fujisaki, Hirotaka Oshima, Shintaro Sato +1

Quantum error correction is one of the most important milestones for realization of large-scale quantum computation. To achieve this, it is essential not only to integrate a large…

quant-ph2022★ 10 cited

Identification of topological phases using classically-optimized variational quantum eigensolver

Ken N. Okada, Keita Osaki, Kosuke Mitarai +1

Variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid quantum-classical algorithm for the near-term quantum computers. Meanwhile, VQE is confronted w…

quant-ph2022★ 1 cited

Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network

Yoshiaki Kawase, Kosuke Mitarai, Keisuke Fujii

t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dim…