8 papers
Representation Geometry and Inductive Biases in Quantum Classifiers across Classical Visual Benchmarks
Yu Yamaoka, Weng Ian Chan, Kosuke Mitarai
Quantum Machine Learning (QML) has a theoretical advantage over classical algorithms for specific synthetic data that is computationally hard. However, it remains unclear whether s…
QSCI-CMP: Quantum-Selected Configuration Interaction with Chemically Motivated Preselection
Masahiko Kamoshita, Kosuke Mitarai
We present QSCI-CMP, a quantum-classical hybrid algorithm for molecular ground-state calculations that reduces both the query count and the gate count of sample-based quantum diago…
An LLM System for Autonomous Variational Quantum Circuit Design
Kenya Sakka, Wataru Mizukami, Kosuke Mitarai
The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs)…
Evaluating higher-order product formulae for molecular ground-state energy estimation
Hiromu Abe, Keita Kanno, Ryosuke Kimura +2
We evaluate deterministic higher-order product formulae for molecular ground-state energy estimation. Motivated by recent fault-tolerant architectures in which non-Clifford operati…
Fisher information based lower bounds on the cost of quantum phase estimation
Ryosuke Kimura, Kosuke Mitarai
Quantum phase estimation (QPE) is a cornerstone of quantum algorithms designed to estimate the eigenvalues of a unitary operator. QPE is typically implemented through two paradigms…
Efficient equivalence checking of Clifford-U circuits with shared single-qubit unitaries
Daisuke Sakamoto, Soshun Naito, Yusei Mori +1
Quantum circuit equivalence checking asks whether two circuits implement the same unitary. It guarantees compiler correctness and safe optimization, yet most existing approaches sc…
Low-Resource Quantum Energy Gap Estimation via Randomization
Hugo Pages, Chusei Kiumi, Yuto Morohoshi +2
Estimating the energy spectra of quantum many-body systems is a fundamental task in quantum physics, with applications ranging from chemistry to condensed matter. Algorithmic shado…
Learning functions of Hamiltonians with Hamiltonian Fourier features
Yuto Morohoshi, Akimoto Nakayama, Hidetaka Manabe +1
We propose a quantum machine learning task that is provably easy for quantum computers and arguably hard for classical ones. The task involves predicting quantities of the form $\m…