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Ryo Sakurai

Publications and source records attributed to Ryo Sakurai.

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Koopman--von Neumann Molecular Dynamics for Green--Kubo Transport Coefficients

We formulate the Green--Kubo transport coefficients of classical molecular dynamics as a readout problem for quantum algorithms using the Koopman--von Neumann (KvN) representation. Both NVE and Nos\'e--Hoover-type NVT dynamics are derived as unitary evolutions on Hilbert spaces associated with the corresponding classical phase spaces. Numerical benchmarks on finite grids show that the discretization error in the correlation function decreases as a power law in the number of grid points $N_z$. Equivalently, with $N_z=2^{n_z}$, the error decreases exponentially in the register size $n_z$, so a target accuracy $\epsilon$ requires $n_z=\mathcal{O}(\log(1/\epsilon))$ qubits. To read out a transport coefficient, we input a flux-excited state to quantum phase estimation (QPE). The probability $P_0$ of measuring the QPE ancilla register in the all-zero state corresponds to a Bartlett-windowed Green--Kubo integral. With maximum-likelihood amplitude estimation, the statistical estimation of $P_0$ defined by this QPE oracle improves from the $N_{\rm queries}^{-1/2}$ scaling of direct shot sampling to scaling close to $N_{\rm queries}^{-1}$. Our circuit-resource analysis shows that one step of the NVE propagator can be built with $\mathcal{O}(n^2)$ CX gates, where $n=n_x+n_p$ is the total number of position and momentum qubits. For the NVT propagator, the centered-difference Pauli-decomposition implementation of the Nos\'e--Hoover friction term scales as $\mathcal{O}(n_\xi n_p\,2^{n_p})$, where $n_p$ and $n_\xi$ are the numbers of momentum and thermostat qubits, respectively. The proposed framework is a concrete step toward translating the principles of quantum algorithms into the transport-coefficient calculations required in practical molecular simulation.

quant-ph

End-to-End Molecular Dynamics with a Langevin Thermostat on Quantum Circuits

We construct a quantum-circuit framework for finite-temperature molecular dynamics in the canonical ensemble (NVT) with a Langevin thermostat, connecting canonical state preparation to subsequent physical-property readouts. The classical nuclear phase-space distribution is encoded as a Koopman--von Neumann (KvN) wave function, and canonical state preparation is formulated as Langevin-type Fokker--Planck relaxation. The Hamiltonian Liouville flow, momentum friction, and momentum diffusion are decomposed into separate circuit blocks. The friction block is represented by a symmetrized momentum-space dilation, whereas the diffusion block is implemented as a cosine filter realized by probabilistic imaginary-time evolution (PITE). We analytically quantify the leading-order temperature bias caused by replacing the Gaussian diffusion kernel with this PITE-realized cosine filter. This analysis yields an internal-temperature correction that targets the desired physical equilibrium distribution. As a proof-of-concept demonstration connecting quantum chemistry to KvN nuclear dynamics, we study the H$_2$ molecule. Numerical simulations show relaxation from a nonequilibrium phase-space distribution to a canonical KvN state. From this canonical state, we demonstrate two complementary readouts: a dynamical quantum-phase-estimation readout of the vibrational density of states associated with the H--H stretch coordinate and a static canonical evaluation of the transition-state-theory (TST) rate constant. This work demonstrates, in a minimal molecular system, a circuit-level protocol that connects Langevin canonical state preparation to physical-property calculations, providing a concrete step toward quantum--classical hybrid molecular dynamics on quantum computers.

quant-ph

Embedding bifurcations into pneumatic artificial muscle

Harnessing complex body dynamics has been a long-standing challenge in robotics. Soft body dynamics is a typical example of high complexity in interacting with the environment. An increasing number of studies have reported that these dynamics can be used as a computational resource. This includes the McKibben pneumatic artificial muscle, which is a typical soft actuator. This study demonstrated that various dynamics, including periodic and chaotic dynamics, could be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics that are not presented in training data could be embedded by using this capability of bifurcation embeddment. This implies that it is possible to embed various qualitatively different patterns into pneumatic artificial muscle by learning specific patterns, without the need to design and learn all patterns required for the purpose. Thus, this study sheds new light on a novel pathway to simplify the robotic devices and training of the control by reducing the external pattern generators and the amount and types of training data for the control.

cs.RO