arXiv subjects

Kiichiro Toyoizumi

Publications and source records attributed to Kiichiro Toyoizumi.

4 recordsLinked to original sources

arXiv2024

Quantum Calculation for Two-Stream Instability and Advection Test of Vlasov-Maxwell Equations: Numerical Evaluation of Hamiltonian Simulation

The Vlasov-Maxwell equations provide kinetic simulations of collisionless plasmas, but numerically solving them on classical computers is often impractical. This is due to the computational resource constraints imposed by the time evolution in the 6-dimensional phase space, which requires broad spatial and temporal scales. In this study, we develop a quantum-classical hybrid Vlasov-Maxwell solver. Specifically, the Vlasov solver implements the Hamiltonian simulation based on Quantum Singular Value Transformation (QSVT), coupled with a classical Maxwell solver. We perform numerical simulation of a 1D advection test and a 1D1V two-stream instability test on the Qiskit-Aer-GPU quantum circuit emulator with an A100 GPU. The computational complexity of our quantum algorithm can potentially be reduced from the classical $O(N^6T^2)$ to $O(\text{poly}(\log(N),N,T))$ for the $N$ grid system and simulation time $T$. Furthermore, the numerical analysis reveals that our quantum algorithm is robust under larger time steps compared with classical algorithms with the constraint of Courant-Friedrichs-Lewy (CFL) condition.

physics.plasm-ph↗

arXiv2024

Quantum conjugate gradient method using the positive-side quantum eigenvalue transformation

Quantum algorithms are still challenging to solve linear systems of equations on real devices. This challenge arises from the need for deep circuits and numerous ancilla qubits. We introduce the quantum conjugate gradient (QCG) method using the quantum eigenvalue transformation (QET). The circuit depth of this algorithm depends on the square root of the coefficient matrix's condition number $κ$, representing a square root improvement compared to the previous quantum algorithms, while the total query complexity worsens. The number of ancilla qubits is constant, similar to other QET-based algorithms. Additionally, to implement the QCG method efficiently, we devise a QET-based technique that uses only the positive side of the polynomial (denoted by $P(x)$ for $x\in[0,1]$). We conduct numerical experiments by applying our algorithm to the one-dimensional Poisson equation and successfully solve it. Based on the numerical results, our algorithm significantly improves circuit depth, outperforming another QET-based algorithm by three to four orders of magnitude.

quant-ph↗

arXiv2023

Hamiltonian simulation using quantum singular value transformation: complexity analysis and application to the linearized Vlasov-Poisson equation

Quantum computing can be used to speed up the simulation time (more precisely, the number of queries of the algorithm) for physical systems; one such promising approach is the Hamiltonian simulation (HS) algorithm. Recently, it was proven that the quantum singular value transformation (QSVT) achieves the minimum simulation time for HS. An important subroutine of the QSVT-based HS algorithm is the amplitude amplification operation, which can be realized via the oblivious amplitude amplification or the fixed-point amplitude amplification in the QSVT framework. In this work, we execute a detailed analysis of the error and number of queries of the QSVT-based HS and show that the oblivious method is better than the fixed-point one in the sense of simulation time. Based on this finding, we apply the QSVT-based HS to the one-dimensional linearized Vlasov-Poisson equation and demonstrate that the linear Landau damping can be successfully simulated.

quant-ph↗

arXiv2023

Perturbation theory with quantum signal processing

Perturbation theory is an important technique for reducing computational cost and providing physical insights in simulating quantum systems with classical computers. Here, we provide a quantum algorithm to obtain perturbative energies on quantum computers. The benefit of using quantum computers is that we can start the perturbation from a Hamiltonian that is classically hard to solve. The proposed algorithm uses quantum signal processing (QSP) to achieve this goal. Along with the perturbation theory, we construct a technique for ground state preparation with detailed computational cost analysis, which can be of independent interest. We also estimate a rough computational cost of the algorithm for simple chemical systems such as water clusters and polyacene molecules. To the best of our knowledge, this is the first of such estimates for practical applications of QSP. Unfortunately, we find that the proposed algorithm, at least in its current form, does not exhibit practical numbers despite of the efficiency of QSP compared to conventional quantum algorithms. However, perturbation theory itself is an attractive direction to explore because of its physical interpretability; it provides us insights about what interaction gives an important contribution to the properties of systems. This is in sharp contrast to the conventional approaches based on the quantum phase estimation algorithm, where we can only obtain values of energy. From this aspect, this work is a first step towards ``explainable'' quantum simulation on fault-tolerant quantum computers.

quant-ph↗