arXiv subjects

Hirotoshi Hirai

Publications and source records attributed to Hirotoshi Hirai.

13 recordsLinked to original sources

arXiv2026

Tailored coupled cluster method with sample-based quantum diagonalization: Application to titanium-based metallocene catalytic reactions for 1-hexene production

Sample-based quantum diagonalization (SQD) is a hybrid quantum-classical method for electronic-structure calculations. We applied SQD to a titanium-based metallocene catalyst system for 1-hexene production, where free-energy differences must be predicted within 1 kcal/mol to accurately determine product selectivity. However, currently accessible active spaces on quantum computers are limited in size, leaving significant dynamical correlation effects outside the active space. To address this challenge, we combined SQD with Tailored Coupled Cluster (TCC) theory. In this SQD-TCC framework, static correlation is treated by SQD, while dynamical correlation is incorporated through CCSD and its perturbative triples extension, TCC(T). We calculated the relative energies of two transition states governing 1-hexene selectivity. While SQD alone did not yield converged relative energies within practically accessible active spaces, SQD-TCC and SQD-TCC(T) provided reasonably converged results. Importantly, the relative energy obtained from SQD-TCC(T) differed by more than 1 kcal/mol from the corresponding CCSD(T) result, demonstrating the significance of static correlation in this system and its impact on predicted selectivity. Although TCC(T) calculations based on CASCI are feasible for small active spaces, the present system requires substantially larger active spaces that are beyond the reach of CASCI. By using SQD, we were able to access active spaces impractical for classical CASCI calculations. These results demonstrate that both static and dynamical electron correlation are essential for a reliable description of this chemistry and highlight the importance of incorporating dynamical correlation into quantum-computing approaches targeting chemically accurate simulations.

physics.chem-ph↗

arXiv2025

Enhanced fill probability estimates in institutional algorithmic bond trading using statistical learning algorithms with quantum computers

The estimation of fill probabilities for trade orders represents a key ingredient in the optimization of algorithmic trading strategies. It is bound by the complex dynamics of financial markets with inherent uncertainties, and the limitations of models aiming to learn from multivariate financial time series that often exhibit stochastic properties with hidden temporal patterns. In this paper, we focus on algorithmic responses to trade inquiries in the corporate bond market and investigate fill probability estimation errors of common machine learning models when given real production-scale intraday trade event data, transformed by a quantum algorithm running on IBM Heron processors, as well as on noiseless quantum simulators for comparison. We introduce a framework to embed these quantum-generated data transforms as a decoupled offline component that can be selectively queried by models in low-latency institutional trade optimization settings. A trade execution backtesting method is employed to evaluate the fill prediction performance of these models in relation to their input data. We observe a relative gain of up to ~ 34% in out-of-sample test scores for those models with access to quantum hardware-transformed data over those using the original trading data or transforms by noiseless quantum simulation. These empirical results suggest that the inherent noise in current quantum hardware contributes to this effect and motivates further studies. Our work demonstrates the emerging potential of quantum computing as a complementary explorative tool in quantitative finance and encourages applied industry research towards practical applications in trading.

quant-ph↗

arXiv2025

Enhancing Accuracy of Quantum-Selected Configuration Interaction Calculations using Multireference Perturbation Theory: Application to Aromatic Molecules

Quantum-selected configuration interaction (QSCI) is a novel quantum-classical hybrid algorithm for quantum chemistry calculations. This method identifies electron configurations having large weights for the target state using quantum devices and allows CI calculations to be performed with the selected configurations on classical computers. In principle, the QSCI algorithm can take advantage of the ability to handle large configuration spaces while reducing the negative effects of noise on the calculated values. At present, QSCI calculations are limited by qubit noise during the input state preparation and measurement process, restricting them to small active spaces. These limitations make it difficult to perform calculations with quantitative accuracy. The present study demonstrates a computational scheme based on multireference perturbation theory calculations on a classical computer, using the QSCI wavefunction as a reference. This method was applied to ground and excited state calculations for two typical aromatic molecules, naphthalene and tetracene. The incorporation of the perturbation treatment was found to provide improved accuracy. Extension of the reference space based on the QSCI-selected configurations as a means of further improvement was also investigated.

physics.chem-ph↗

arXiv2024

Theoretical analysis of chemical reactions using a variational quantum eigensolver method without specifying molecular charge

Quantum chemical calculations have attracted much attention as a practical application of quantum computing. Quantum computers can prepare superpositions of electronic states with various numbers of electrons on qubits. This special feature could be used to construct an efficient method for analyzing the structural variations of molecules and chemical reactions involving changes in molecular charge. The present work demonstrates a variational quantum eigensolver (VQE) algorithm based on a cost function ($L_{cost}$) having the same form as the grand potential of the grand canonical ensemble of electrons. The chemical potential of the electrons ($w$) is used as an input to these VQE calculations, whereas the molecular charge is not specified in advance but rather is a physical quantity that results from the calculations. Calculations involving model systems are carried out to show the viability of this new approach. Calculations for typical electron-donating and electron-accepting molecules using this technique yielded cationic, neutral or anionic species depending on the value of $w$. Models representing the adsorption of water or ammonia on copper-based catalysts predicted that oxidation would be associated with such adsorption. The molecular structures in which such reactions occurred were found to be dependent on the catalyst model, the adsorbed molecular species, and the value of $w$. These results arise because the electronic state that gives the lowest $L_{cost}$ value depends on the value of $w$ and the molecular structure. This behaviour was successfully simulated by the present VQE calculations.

physics.chem-ph↗

arXiv2023

Practical application of quantum neural network to materials informatics: prediction of the melting points of metal oxides

Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for materials informatics (MI) problems. However, there are only a few examples of the application of QNN to multivariate regression models, and little is known about how these models are constructed. This study aims to construct a QNN model to predict the melting points of metal oxides as an example of a multivariate regression task for the MI problem. Different architectures (encoding methods and entangler arrangements) are explored to create an effective QNN model. Shallow-depth ansatzs could achieve sufficient expressibility using sufficiently entangled circuits. The "linear" entangler was adequate for providing the necessary entanglement. The expressibility of the QNN model could be further improved by increasing the circuit width. The generalization performance could also be improved, outperforming the classical NN model. No overfitting was observed in the QNN models with a well-designed encoder. These findings suggest that QNN can be a useful tool for MI.

quant-ph↗

arXiv2023

Application of quantum neural network model to a multivariate regression problem

Since the introduction of the quantum neural network model, it has been widely studied due to its strong expressive power and robustness to overfitting. To date, the model has been evaluated primarily in classification tasks, but its performance in practical multivariate regression problems has not been thoroughly examined. In this study, the Auto-MPG data set (392 valid data points, excluding missing data, on fuel efficiency for various vehicles) was used to construct QNN models and investigate the effect of the size of the training data on generalization performance. The results indicate that QNN is particularly effective when the size of training data is small, suggesting that it is especially suitable for small-data problems such as those encountered in Materials Informatics.

quant-ph↗

arXiv2023

Construction of Antisymmetric Variational Quantum States with Real-Space Representation

Electronic state calculations using quantum computers are mostly based on second quantization, which is suitable for qubit representation. Another way to describe electronic states on a quantum computer is first quantization, which is expected to achieve smaller scaling with respect to the number of basis functions than second quantization. Among basis functions, a real-space basis is an attractive option for quantum dynamics simulations in the fault-tolerant quantum computation (FTQC) era. A major difficulty in first quantization with a real-space basis is state preparation for many-body electronic systems. This difficulty stems from of the antisymmetry of electrons, and it is not straightforward to construct antisymmetric quantum states on a quantum circuit. In the present paper, we provide a design principle for constructing a variational quantum circuit to prepare an antisymmetric quantum state. The proposed circuit generates the superposition of exponentially many Slater determinants, that is, a multi-configuration state, which provides a systematic approach to approximating the exact ground state. We implemented the variational quantum eigensolver (VQE) to obtain the ground state of a one-dimensional hydrogen molecular system. As a result, the proposed circuit well reproduced the exact antisymmetric ground state and its energy, whereas the conventional variational circuit yielded neither an antisymmetric nor a symmetric state. Furthermore, we analyzed the many-body wave functions based on quantum information theory, which illustrated the relation between the electron correlation and the quantum entanglement.

quant-ph↗

arXiv2023

Computational analysis of chemical reactions using a variational quantum eigensolver algorithm without specifying spin multiplicity

The analysis of a chemical reaction along the ground state potential energy surface in conjunction with an unknown spin state is challenging because electronic states must be separately computed several times using different spin multiplicities to find the lowest energy state. However, in principle, the ground state could be obtained with just a single calculation using a quantum computer without specifying the spin multiplicity in advance. In the present work, ground state potential energy curves for PtCO were calculated as a proof-of-concept using a variational quantum eigensolver (VQE) algorithm. This system exhibits a singlet-triplet crossover as a consequence of the interaction between Pt and CO. VQE calculations using a statevector simulator were found to converge to a singlet state in the bonding region, while a triplet state was obtained at the dissociation limit. Calculations performed using an actual quantum device provided potential energies within $\pm$2 kcal/mol of the simulated energies after adopting error mitigation techniques. The spin multiplicities in the bonding and dissociation regions could be clearly distinguished even in the case of a small number of shots. The results of this study suggest that quantum computing can be a powerful tool for the analysis of the chemical reactions of systems for which the spin multiplicity of the ground state and variations in this parameter are not known in advance.

physics.chem-ph↗

arXiv2022

Excited-state molecular dynamics simulation based on variational quantum algorithms

We propose an excited-state molecular dynamics simulation method based on variational quantum algorithms at a computational cost comparable to that of ground-state simulations. We utilize the feature that excited states can be obtained as metastable states in the restricted variational quantum eigensolver calculation with a hardware-efficient ansatz. To demonstrate the effectiveness of the method, molecular dynamics simulations are performed for the S1 excited states of H2 and CH2NH molecules. The results are consistent with those of the exact adiabatic simulations in the S1 states, except for the CH2NH system, after crossing the conical intersection, where the proposed method causes a nonadiabatic transition.

physics.chem-ph↗

arXiv2021

Calculation of core-excited and core-ionized states using variational quantum deflation method and applications to photocatalyst modelling

The possibility of performing quantum chemical calculations using quantum computers has attracted much interest. In this regard, variational quantum deflation (VQD) is a quantum-classical hybrid algorithm for the calculation of excited states with noisy intermediate-scale quantum (NISQ) devices. Although the validity of this method has been demonstrated, there have been few practical applications, primarily because of the uncertain effect of calculation conditions on the results. In the present study, calculations of the core-excited and core-ionized states for common molecules based on the VQD method were simulated using a classical computer, focusing on the effects of the weighting coefficients applied in the penalty terms of the cost function. Adopting a simplified procedure for estimating the weighting coefficients based on molecular orbital levels allowed these core-level states to be successfully calculated. The O 1s core-ionized state for a water molecule was calculated with various weighting coefficients and the resulting ansatz states were systematically examined. The application of this technique to functional materials was demonstrated by calculating the core-level states for titanium dioxide (TiO2) and nitrogen-doped TiO2 models. The results demonstrate that VQD calculations employing an appropriate cost function can be applied to the analysis of functional materials in conjunction with an experimental approach.

physics.chem-ph↗

arXiv2021

Non-adiabatic Quantum Wavepacket Dynamics Simulation Based on Electronic Structure Calculations using the Variational Quantum Eigensolver

A non-adiabatic nuclear wavepacket dynamics simulation of the H$_2$O$^+$ de-excitation process is performed based on electronic structure calculations using the variational quantum eigensolver. The adiabatic potential energy surfaces and non-adiabatic coupling vectors are computed with algorithms for noisy intermediate-scale quantum devices, and time propagation is simulated with conventional methods for classical computers. The results of non-adiabatic transition dynamics from the $\tilde{B}$ state to $\tilde{A}$ state reproduce the trend reported in previous studies, which suggests that this quantum-classical hybrid scheme may be a useful application for noisy intermediate-scale quantum devices.

quant-ph↗

arXiv2021

Missing Rung Problem in Vibrational Ladder Climbing

We observed vanishing of the transition dipole moment, interrupting vibrational ladder climbing (VLC) in molecular systems. We clarified the mechanism of this phenomenon and present a method to use an additional chirped pulse to preserve the VLC. To show the effectiveness of our method, we conducted wavepacket dynamics simulations for LiH dissociations with chirped pulses. The results indicate that the efficiency of LiH dissociation is significantly improved by our method compared to conventional methods. We also revealed the quantum interference effect behind the excitation process of VLC.

quant-ph↗

arXiv2021

Molecular Structure Optimization based on Electrons-Nuclei Quantum Dynamics Computation

A new concept of the molecular structure optimization method based on quantum dynamics computations is presented. Nuclei are treated as quantum mechanical particles, as are electrons, and the many-body wave function of the system is optimized by the imaginary time evolution method. A demonstration with a 2-dimensional H$^+_2$ molecule shows that the optimized nuclear positions can be specified with a small number of observations. This method is considered to be suitable for quantum computers, the development of which will realize its application as a powerful method.

quant-ph↗