arXiv subjects

Koki Aoyama

Publications and source records attributed to Koki Aoyama.

3 recordsLinked to original sources

arXiv2026

Square-root Time Atom Reconfiguration Plan for Lattice-shaped Mobile Tweezers

This paper proposes a scalable planning algorithm for creating defect-free atom arrays in neutral-atom systems. The algorithm generates a $\mathcal{O}(\sqrt N)$ time plan for $N$ atoms by parallelizing atom transport using a two-dimensional lattice pattern generated by acousto-optic deflectors. Our approach is based on a divide-and-conquer strategy that decomposes an arbitrary reconfiguration problem into at most three one-dimensional shuttling tasks, enabling each atom to be transported with a total transportation cost of $\mathcal{O}(\sqrt N)$. Using the Gale--Ryser theorem, the proposed algorithm provides a highly reliable solution for arbitrary target geometries. We further introduce a peephole optimization technique that improves reconfiguration efficiency for grid target geometries. Numerical simulations on a 632$\times$632 atom array demonstrate that the proposed algorithm achieves a grid configuration plan that reduces the total transportation cost to 1/7 of state-of-the-art algorithms, while resulting in 32%--35% more atom captures. We believe that our scalability improvement contributes to realizing large-scale quantum computers based on neutral atoms. Our experimental code is available from https://github.com/kotamanegi/sqrt-time-atom-reconfigure.

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arXiv2024

Almost optimal measurement scheduling of molecular Hamiltonian via finite projective plane

We propose an efficient and almost optimal scheme for measuring molecular Hamiltonians in quantum chemistry on quantum computers, which requires $2N^2$ distinct measurements in the leading order with $N$ being the number of molecular orbitals. It achieves the state-of-the-art by improving a previous proposal by Bonet-Monroig et al. [Phys. Rev. X 10, 031064 (2020)] which exhibits $\frac{10}{3}N^2$ scaling in the leading order. We develop a novel method based on a finite projective plane to construct sets of simultaneously-measurable operators contained in molecular Hamiltonians. Each measurement only requires a depth-$O(N)$ circuit consisting of $O(N^2)$ one- and two-qubit gates under the Jordan-Wigner and parity mapping, assuming the linear connectivity of qubits on quantum hardwares. Because evaluating expectation values of molecular Hamiltonians is one of the major bottlenecks in the applications of quantum devices to quantum chemistry, our finding is expected to accelerate such applications.

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arXiv2023

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

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 of 4,950,000 data points organized in 9 subdatasets. Building our dataset from the quantum encoding of classical information (e.g., MNIST dataset), we deliver a dataset in a dual form: in quantum form, as circuits, and in classical form, as quantum circuit descriptions (quantum programming language, QASM). In fact, also the Machine Learning research related to quantum computers undertakes a dual challenge: enhancing machine learning exploiting the power of quantum computers, while also leveraging state-of-the-art classical machine learning methodologies to help the advancement of quantum computing. Therefore, we perform circuit classification on our dataset, tackling the task with both quantum and classical models. In the quantum endeavor, we test our circuit dataset with Quantum Kernel methods, and we show excellent results up to $97\%$ accuracy. In the classical world, the underlying quantum mechanical structures within the quantum circuit data are not trivial. Nevertheless, we test our dataset on three classical models: Structured State Space sequence model (S4), Transformer and LSTM. In particular, the S4 model applied on the tokenized QASM sequences reaches an impressive $77\%$ accuracy. These findings illustrate that quantum circuit-related datasets are likely to be quantum advantageous, but also that state-of-the-art machine learning methodologies can competently classify and recognize quantum circuits. We finally entrust the quantum and classical machine learning community the fundamental challenge to build more quantum-classical datasets like ours and to build future benchmarks from our experiments. The dataset is accessible on GitHub and its circuits are easily run in qulacs or qiskit.

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