54 citations · 83 across the 14 of their papers we have counts for
15 papers
Quantum Algorithms and Hardness for Point-Count Approximation over Finite Fields
Yota Maeda, Hiroshi Yano
We study the approximation of the number of solutions of Laurent polynomials over finite fields. For a Laurent polynomial \[f(x)=\sum_{j=1}^{s}a_jx^{u_j}\in \mathbb{F}_q[x_1^{\pm1}…
Hamiltonian simulation for 3D elastic wave equations in homogeneous elastic media
Kosuke Nakanishi, Hiroshi Yano, Yuki Sato
We present an explicit quantum circuit construction for Hamiltonian simulation of a first-order velocity--stress formulation of the three-dimensional elastic wave equation in homog…
Random Access Codes: Explicit Constructions, Optimality, and Classical-Quantum Gaps
Ruho Kondo, Yuki Sato, Hiroshi Yano +3
A random access code (RAC) encodes an -bit string into a -bit message, , so that any requested bit can be recovered with high probability; a quantum RAC (QRAC) uses …
Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows
Kosuke Ito, Akira Tanji, Hiroshi Yano +2
Learning from quantum data using classical machine learning models has emerged as a promising paradigm toward realizing quantum advantages. Despite extensive analyses on their perf…
Quantum framework for parameterizing partial differential equations via diagonal block-encoding
Hiroshi Yano, Yuki Sato
We study a quantum-algorithmic framework for parameterizing partial differential equations (PDEs). For a broad class of problems in which the discretized parameter field admits a d…
Explicit block-encoding for partial differential equation-constrained optimization
Yuki Sato, Jumpei Kato, Hiroshi Yano +2
Partial differential equation (PDE)-constrained optimization, where an optimization problem is subject to PDE constraints, arises in various applications such as design, control, a…
Quantum phase classification via partial tomography-based quantum hypothesis testing
Akira Tanji, Hiroshi Yano, Naoki Yamamoto
Quantum phase classification is a fundamental problem in quantum many-body physics, traditionally approached using order parameters or quantum machine learning techniques such as q…
Quantum expectation value estimation by doubling the number of qubits
Hiroshi Yano, Masaya Kohda, Shoichiro Tsutsui +4
Expectation value estimation is ubiquitous in quantum algorithms. The expectation value of a Hamiltonian, which is essential in various practical applications, is often estimated b…
Statistical inference for quantum singular models
Hiroshi Yano, Yota Maeda, Naoki Yamamoto
Deep learning has seen substantial achievements, with numerical and theoretical evidence suggesting that singularities of statistical models are considered a contributing factor to…
Accelerated variational quantum eigensolver with joint Bell measurement
Chenfeng Cao, Hiroshi Yano, Yuya O. Nakagawa
The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid algorithm for near-term quantum computers to obtain the ground states of molecular Hamilton…
Quantum information criteria for model selection in quantum state estimation
Hiroshi Yano, Naoki Yamamoto
Quantum state estimation (or state tomography) is an indispensable task in quantum information processing. Because full state tomography that determines all elements of the density…
Normalized Gradient Descent for Variational Quantum Algorithms
Yudai Suzuki, Hiroshi Yano, Rudy Raymond +1
Variational quantum algorithms (VQAs) are promising methods that leverage noisy quantum computers and classical computing techniques for practical applications. In VQAs, the classi…
Trainable Discrete Feature Embeddings for Variational Quantum Classifier
Napat Thumwanit, Chayaphol Lortaraprasert, Hiroshi Yano +1
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs i…
Efficient Discrete Feature Encoding for Variational Quantum Classifier
Hiroshi Yano, Yudai Suzuki, Kohei M. Itoh +2
Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resou…
Analysis and synthesis of feature map for kernel-based quantum classifier
Yudai Suzuki, Hiroshi Yano, Qi Gao +4
A method for analyzing the feature map for the kernel-based quantum classifier is developed; that is, we give a general formula for computing a lower bound of the exact training ac…