arXiv subjects

Wataru Mizukami

Publications and source records attributed to Wataru Mizukami.

At least 19 recordsLinked to original sources

arXiv2026

From sparse quantum-computing data to atomistic simulation with universal machine-learning interatomic potentials

We propose a framework for incorporating quantum-computing-based electronic-structure calculations into universal machine-learning interatomic potentials (uMLIPs). Rather than constructing an interatomic potential from scratch, we refine a pretrained DFT-based uMLIP using a small set of accurate reference energies obtained from quantum computing. We demonstrate the approach for three chemically distinct applications: the Menshutkin reaction, water adsorption in the metal-organic framework HKUST-1, and CO hopping on a high-entropy-alloy nanoparticle. For the Menshutkin reaction, fine-tuning on gas-phase configurations improves the transition-state energy inside a carbon nanotube but not the product energy. For water adsorption in HKUST-1, fine-tuning with only 14 reference configurations brings adsorption thermodynamics obtained from millions of configurations sampled by Widom insertion into closer agreement with reference values. For CO hopping on an IrPdPtRhRu nanoparticle, the preference for on-top over bridge adsorption is recovered in the finite-temperature free-energy profile obtained from enhanced-sampling molecular dynamics, even though the reference data contain only energies. These results demonstrate that the proposed framework provides a practical route for incorporating quantum-computing calculations into realistic atomistic simulations and that quantum-computing reference data can improve pretrained uMLIPs.

physics.chem-ph↗

arXiv2026

Quantum machine learning interatomic potential: Application of variational quantum algorithm

This study applied quantum circuit learning, a commonly used hybrid quantum-classical machine learning algorithm, to a machine learning interatomic potential (MLIP) for predicting the energies of molecules in molecular datasets. We retrained the ANI model using the quantum transfer learning architecture [Mari et al., Quantum, 4:340, 2020] and evaluated numerical accuracy with a quantum circuit simulator. The evaluation confirmed that inserting a quantum circuit into the classical neural network of the MLIP yielded slightly higher accuracy than the fully classical neural network under certain conditions. In particular, the model incorporating a quantum circuit was more effective when the pretraining model had room for improvement in accuracy. These findings may contribute to advancing the application of quantum machine learning for MLIPs.

quant-ph↗

arXiv2026

An LLM System for Autonomous Variational Quantum Circuit Design

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) to conduct iterative quantum circuit designs under explicit design constraints. Our system integrates seven components: Exploration, Generation, Discussion, Validation, Storage, Evaluation, and Review. These components form a closed-loop workflow that combines web-based knowledge acquisition, literature-grounded critique, executable code generation, and experimental feedback. We evaluate the framework on two tasks: quantum feature map construction for quantum machine learning and ansatz generation for variational quantum eigensolver applications in quantum chemistry. In image classification benchmarks, the best generated feature map outperforms representative quantum feature maps and, when scaled to larger qubit counts, surpasses the classical radial basis function kernel. In molecular ground state estimation across seven molecules, the generated ansatz attains competitive accuracy with widely used chemically inspired and hardware-efficient constructions while satisfying the imposed scaling constraints. These results establish LLM driven agentic system as a viable paradigm for automated quantum circuit design and illustrate how AI systems can participate in iterative scientific optimization workflows across scientific domains.

quant-ph↗

arXiv2026

Quantum computing for accurate large-scale electronic-structure calculations: DFT-embedded, post-processed quantum-selected configuration interaction

We present a multilevel embedding framework for quantum chemistry calculations on a quantum computer. In our framework, a quantum algorithm treats the strongly correlated active space, while a high-level wave-function method such as coupled cluster theory or multireference perturbation theory recovers the remaining correlation in the surrounding region. A sampling-based quantum algorithm, quantum-selected configuration interaction, bridges the quantum and classical treatments. The entire calculation is embedded in a low-cost density functional theory description of the surrounding environment using Manby's projection technique. We apply the framework to organic, metal-organic, and metallic systems, computing bond dissociation energies, adsorption energies, and reaction barriers using only the subset of qubits of a 144-qubit superconducting quantum computer at the University of Osaka and achieving $\sim$1 kcal/mol agreement with classical references for a Menshutkin $\mathrm{S_N2}$ reaction inside a carbon nanotube. Our results may open the way to quantitatively reliable quantum-classical hybrid calculations for large-scale chemical systems.

physics.chem-ph↗

arXiv2026

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications

Machine learning interatomic potentials (MLIPs) evaluate potential energy surfaces orders of magnitude faster while maintaining accuracy comparable to first-principles calculations, and universal MLIPs that cover most of the periodic table are becoming increasingly commonplace. However, existing large-scale datasets have limited or no coverage of heavy elements such as minor actinides crucial in the nuclear field, and universal MLIPs are typically limited to 89 elements. Here, we constructed a heavy element dataset HE26 containing minor actinides, based on experimental and computational literature data. By integrating this with existing molecular and crystal datasets, we developed an open-source universal MLIP covering 97 elements, the broadest elemental coverage to date. The resulting model showed strong performance on the inorganic MPtrj and organic OFF23 test sets and promising accuracy on HE26. The dataset and model open a pathway toward the development of energy resources and the design of novel materials, such as actinide-based high-entropy ceramics, in the nuclear field.

physics.chem-ph↗

arXiv2026

Doubling the size of quantum selected configuration interaction based on seniority-zero space and its application to QC-QSCI-AFQMC

We propose doubly occupied configuration interaction-quantum selected configuration interaction (DOCI-QSCI), which samples from the seniority-zero space. While the use of this space effectively doubles the qubit budget, equaling the number of spatial orbitals, this sector restriction can compromise quantitative accuracy. To compensate for this, we expand sampled bitstrings via their Cartesian product into a larger space that includes seniority-breaking determinants. The resulting wave function is also proposed using the trial state in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) to recover dynamical correlations across the full orbital space (DOCI-QSCI-AFQMC). We evaluate the proposed methods on the H6 chain, N2 dissociation, and the addition of singlet O2 to a BODIPY dye. For the H6 chain, DOCI-QSCI-AFQMC reproduces the accuracy of the level of the complete-active-space counterpart with the quantum device ibm kobe. For N2 and BODIPY-O2, with (14e, 28o) and up to (20e, 20o) active spaces, it yields reasonable results, whereas single-reference CCSD(T) fails qualitatively. These results demonstrate that the DOCI-QSCI doubles the orbital space accessible to conventional QSCI and subsequent ph-AFQMC post-processing delivers reasonably high accuracy.

quant-ph↗

arXiv2026

Symmetry-Adapted State Preparation for Quantum Chemistry on Fault-Tolerant Quantum Computers

We present systematic and resource-efficient constructions of continuous symmetry projectors, particularly $U(1)$ particle number and $SU(2)$ total spin, tailored for fault-tolerant quantum computations. Our approach employs a linear combination of unitaries (LCU) as well as generalized quantum signal processing (GQSP and GQSVT) to implement projectors. These projectors can then be coherently applied as state filters prior to quantum phase estimation (QPE). We analyze their asymptotic gate complexities for explicit circuit realizations. For the particle number and $S_z$ symmetries, GQSP offers favorable resource usage features owing to its low ancilla qubit requirements and robustness to finite precision rotation gate synthesis. For the total spin projection, the structured decomposition of $\hat{P}_{S,M_S}$ reduces the projector T gate count. Numerical simulations show that symmetry filtering substantially increases the QPE success probability, leading to a lower overall cost compared to that of unfiltered approaches across representative molecular systems. Resource estimates further indicate that the cost of symmetry filtering is $3$ to $4$ orders of magnitude lower than that of the subsequent phase estimation step This advantage is especially relevant in large, strongly correlated systems, such as FeMoco, a standard strongly correlated open-shell benchmark. For FeMoco, the QPE cost is estimated at ${\sim}10^{10}$ T gates, while our symmetry projector requires only ${\sim}10^{6}$--$10^{7}$ T gates. These results establish continuous-symmetry projectors as practical and scalable tools for state preparation in quantum chemistry and provide a pathway toward realizing more efficient fault-tolerant quantum simulations.

quant-ph↗

arXiv2025

Quantum Power Iteration Unified Using Generalized Quantum Signal Processing

We propose a unifying framework for the state preparation using quantum power method algorithms based on generalized quantum signal processing (GQSP). We apply GQSP to realize quantum analogs of classical power iteration, power Lanczos, inverse iteration, and folded spectrum methods, all within a single coherent framework. GQSP allows efficient realization of methods that require complex polynomials, while avoiding the limitations of approaches based on linear combinations of time-evolution operators. Our constructions, including a Trotter-decomposition-free quantum inverse iteration, achieve near-optimal query scaling, together with reduced qubit requirements. The same formalism yields a quantum folded spectrum method for excited state preparation that avoids explicitly forming powers of the Hamiltonian or performing variational optimization. We provide a theoretical analysis of success probabilities and resource scaling, and we validate the methods numerically using molecular Hamiltonians. The results show that quantum power Lanczos lowers the computational cost and provides robust convergence compared to naive quantum power iteration. Our findings reveal that GQSP-based implementations of power methods combine scalability, flexibility, and robust convergence, paving the way for practical initial state preparations on fault-tolerant quantum devices.

quant-ph↗

arXiv2025

Taming Multi-Domain, -Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations

Machine learning interatomic potentials (MLIPs) are changing atomistic simulations in the field of chemistry and materials science. However, constructing a single universal MLIP that can accurately model molecular and crystalline systems remains challenging. A central obstacle is the integration of diverse datasets generated under different computational conditions. We present Total Energy Alignment (TEA), which is an approach that enables the seamless integration of heterogeneous quantum chemical datasets without redundant calculations. Using TEA, we trained MACE-Osaka24, the first open-source MLIP model based on a unified dataset covering molecular and crystalline systems. This universal model displays strong performances across diverse chemical systems, exhibiting similar or improved accuracies in predicting organic reaction barriers compared to those of specialized models, while effectively maintaining state-of-the-art accuracies for inorganic systems. These advancements pave the way for accelerated discoveries in the fields of chemistry and materials science via genuine foundation models for chemistry.

physics.chem-ph↗

arXiv2025

Enhancing quantum computations with the synergy of auxiliary field quantum Monte Carlo and computational basis tomography

We introduce QC-CBT-AFQMC, a hybrid algorithm that incorporates computational basis tomography (CBT) into the quantum-classical auxiliary-field quantum Monte Carlo (QC-AFQMC) method proposed by Huggins et al. [Nature 603, 416-420 (2022)], replacing the use of classical shadows. While the original QC-AFQMC showed high accuracy for quantum chemistry calculations, it required exponentially costly post-processing. Subsequent work using Matchgate shadows [Commun. Math. Phys. 404, 629 (2023)] improved scalability, but still suffers from prohibitive computational requirements that limit practical applications. Our QC-CBT-AFQMC approach uses shallow Clifford circuits with a quadratic reduction of two-qubit gates over the original algorithm, significantly reducing computational requirements and enabling accurate calculations under limited measurement budgets. We demonstrate its effectiveness on the hydroxyl radical, ethylene, and nitrogen molecule, producing potential energy curves that closely match established benchmarks. We also examine the influence of CBT measurement counts on accuracy, showing that subtracting the active space AFQMC energy mitigates measurement-induced errors. Furthermore, we apply QC-CBT-AFQMC to estimate reaction barriers in [3+2]-cycloaddition reactions, achieving agreement with high-level references and successfully incorporating complete basis set extrapolation techniques. These results highlight QC-CBT-AFQMC as a practical quantum-classical hybrid method that bridges the capabilities of quantum devices and accurate chemical simulations.

quant-ph↗

arXiv2025

Optimizing adsorption configurations on alloy surfaces using Tensor Train Optimizer

Understanding how molecules arrange on surfaces is fundamental to surface chemistry and essential for the rational design of catalytic and functional materials. In particular, the energetically most stable configuration provides valuable insight into adsorption-related processes. However, the search for this configuration is a global optimization problem with exponentially growing complexity as the number of adsorbates and possible adsorption sites increases. To address this, we express the adsorption energy as a sum of multi-adsorbate interaction terms, evaluated using our in-house trained machine learning interatomic potential MACE-Osaka24, and formulate the search for the most stable configuration as a higher-order unconstrained binary optimization (HUBO) problem. We employ a tensor-train-based method, Tensor Train Optimizer (TTOpt), to solve the HUBO problem and identify optimal adsorption configurations of CO and NO molecules on various alloys up to full surface coverage. Our results show that including interaction terms up to third order may be sufficient to approximate adsorption energies within chemical accuracy and to identify optimal configurations. We also observed that TTOpt performs better with the HUBO formulation, suggesting that third-order terms help preserve correlations between adsorption sites, which allow TTOpt to optimize configurations more effectively. The extensive benchmarks across various alloys, surface geometries, and adsorbates demonstrate the robustness and applicability of using TTOpt to solve HUBO-type global optimization problems in surface chemistry. In contrast to quantum and digital annealers, which have recently been applied to similar global optimization tasks but are restricted to cost functions with at most quadratic terms, our approach can incorporate higher-order terms in a straightforward manner and does not require specialized hardware.

physics.chem-ph↗

arXiv2025

Integrating Classical and Quantum Software for Enhanced Simulation of Realistic Chemical Systems

We demonstrate the feasibility of quantum computing for large-scale, realistic chemical systems through the development of a new interface using a quantum circuit simulator and CP2K, a highly efficient first-principles calculation software. Quantum chemistry calculations using quantum computers require Hamiltonians prepared on classical computers. Moreover, to compute forces beyond just single-point energy calculations, one- and two-electron integral derivatives and response equations are also to be computed on classical computers. Our developed interface allows for efficient evaluation of forces with the quantum-classical hybrid framework for large chemical systems. We performed geometry optimizations and first-principles molecular dynamics calculations on typical condensed-phase systems. These included liquid water, molecular adsorption on solid surfaces, and biological enzymes. In water benchmarks with periodic boundary conditions, we confirmed that the cost of preparing second-quantized Hamiltonians and evaluating forces scales almost linearly with the simulation box size. This research marks a step towards the practical application of quantum-classical hybrid calculations, expanding the scope of quantum computing to realistic and complex chemical phenomena.

physics.chem-ph↗

arXiv2025

Coupled cluster method tailored by quantum selected configuration interaction

We present the quantum-selected configuration interaction-tailored coupled-cluster (QSCI-TCC) method, a hybrid quantum-classical scheme that tailors coupled-cluster (CC) theory with a quantum-selected configuration interaction (QSCI) wave function. QSCI provides a scalable, shot-efficient approach to reconstructing the many-electron state prepared on quantum hardware on a classical computer. The resulting active-space CI coefficients, which are free from additive shot noise, are mapped to fixed cluster amplitudes within the tailored coupled-cluster framework, after which a conventional CC calculation optimizes the remaining amplitudes. This workflow embeds static (strong) correlation from the quantum device and subsequently recovers dynamical (weak) correlation, yielding a balanced description of both. The method is classically simulated and applied to the simultaneous O-H bond dissociation in H$_2$O and the triple-bond dissociation in N$_2$. QSCI-TCC and its perturbative-triples variant, QSCI-TCC(T), provide accurate results even where CCSD or CCSD(T) begin to break down. Shot-count tests for the N$_2$ (6e, 6o) active space demonstrate that, with the (c) correction, chemically sufficient precision ($\leq 1$ kcal/mol) is achieved with only $1.0 \times 10^5$ shots in the strongly correlated regime ($r=2.2$ Å) -- an order of magnitude fewer than required by an earlier matchgate-shadows implementation [J. Chem. Theory Comput., 20, 5068 (2024)]. By pairing resource-efficient quantum sampling with the CC theory, QSCI-TCC provides a promising pathway to quantum-chemical calculations of classically intractable systems.

physics.chem-ph↗

arXiv2025

Auxiliary-field quantum Monte Carlo method with seniority-zero trial wave function

We present an approach that uses the doubly occupied configuration interaction (DOCI) wave function as the trial wave function in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC). DOCI is a seniority-zero method focused on electron pairs. Although DOCI considers much fewer electron configurations than the complete active space (CAS) configuration interaction method, it efficiently captures the static correlation, while the consequent ph-AFQMC recovers the dynamical correlation across all orbitals. We also explore an orbital-optimized version (OO-DOCI) to further improve accuracy. We test this approach on several chemical systems, including single O-H bond breaking in water and polymer additives. In these cases, OO-DOCI-AFQMC closely matches CAS-based ph-AFQMC and even outperforms coupled-cluster singles, doubles, and perturbative triples. However, for strongly correlated systems, such as the carbon dimer and multi-bond dissociation in hydrogen systems and water, the method's accuracy drops. This suggests that seniority-zero space models may be insufficient as trial wave functions in ph-AFQMC for strongly correlated systems, suggesting the need for trial wave functions in an extended space. Despite such a limitation, our study demonstrates that DOCI- and OO-DOCI-based ph-AFQMC can reduce the steep cost of CAS approaches, offering a path to accurate multi-reference calculations for larger, more complex systems.

physics.chem-ph↗

arXiv2025

Faster Quantum Algorithm for Multiple Observables Estimation in Fermionic Problems

Achieving quantum advantage in efficiently estimating collective properties of quantum many-body systems remains a fundamental goal in quantum computing. While the quantum gradient estimation (QGE) algorithm has been shown to achieve doubly quantum enhancement in the precision and the number of observables, it remains unclear whether one benefits in practical applications. In this work, we present a generalized framework of adaptive QGE algorithm, and further propose two variants which enable us to estimate the collective properties of fermionic systems using the smallest cost among existing quantum algorithms. The first method utilizes the symmetry inherent in the target state, and the second method enables estimation in a single-shot manner using the parallel scheme. We show that our proposal offers a quadratic speedup compared with prior QGE algorithms in the task of fermionic partial tomography for systems with limited particle numbers. Furthermore, we provide the numerical demonstration that, for a problem of estimating fermionic 2-RDMs, our proposals improve the number of queries to the target state preparation oracle by a factor of 100 for the nitrogenase FeMo cofactor and by a factor of 500 for Fermi-Hubbard model of 100 sites.

quant-ph↗

arXiv2025

Comprehensive Study on Heisenberg-limited Quantum Algorithms for Multiple Observables Estimation

In the accompanying paper of arXiv:2505.00697, we have presented a generalized scheme of adaptive quantum gradient estimation (QGE) algorithm, and further proposed two practical variants which not only achieve doubly quantum enhancement in query complexity regarding estimation precision and number of observables, but also enable minimal cost to estimate $k$-RDMs in fermionic systems among existing quantum algorithms. Here, we provide full descriptions on the algorithm, and provide theoretical guarantee for the estimation precision in terms of the root mean squared error. Furthermore, we analyze the performance of the quantum amplitude estimation algorithm, another variant of the Heisenberg-limited scaling algorithm, and show how the estimation error is minimized under the circuit structure that resembles the phase estimation algorithm. We finally describe the details for the numerical evaluation of the query complexity of the Heisenberg-limited algorithms and sampling-based methods to make a thorough comparison in the task of estimating fermionic $k$-RDMs.

quant-ph↗

arXiv2025

Auxiliary-field quantum Monte Carlo method with quantum selected configuration interaction

We propose using the wave function generated by the quantum selected configuration interaction (QSCI) method as the trial wave function in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC). In the QSCI framework, electronic configurations are sampled from the quantum state realized on a quantum computer. These configurations serve as basis states for constructing an effective Hamiltonian, which is then diagonalized to obtain the corresponding eigenstate. Using this wave function, ph-AFQMC is performed to recover the dynamical electron correlation across the whole orbital space. The use of the QSCI trial wave function is expected to improve the feasibility of the quantum-classical (QC) hybrid quantum Monte Carlo approach [Nature, 603, 416 (2022)]. We call this integrated approach QC-QSCI-AFQMC, or QSCI-AFQMC for short. This method is validated across several molecular systems. For H2O and a linear H4 chain, we achieved chemical accuracy in most investigations relative to full configuration interaction while utilizing superconducting quantum computers at Osaka University and RIKEN. Additionally, the application of QSCI-AFQMC to the O-H bond dissociation in an organic molecule highlights the complementary synergy between capturing static correlation on quantum hardware and incorporating dynamical correlation via classical post-processing. For the N2, when QSCI-AFQMC is executed with a noiseless simulator, it ranks among the most accurate methods compared to various multireference electronic structure theories. Although the proposed method is demonstrated using small active spaces on current quantum devices, the concept is not limited to few-qubit problems. The QSCI-AFQMC can compete with state-of-the-art classical computational techniques, particularly in larger active spaces, displaying considerable potential for resolving classically intractable problems in quantum chemistry.

quant-ph↗

arXiv2025

Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials

Computational screening is indispensable for the efficient design of high-entropy alloys (HEAs), which hold considerable potential for catalytic applications. However, the chemical space of HEAs is exponentially vast with respect to the number of constituent elements, making even machine learning-based screening calculations time-intensive. To address this challenge, we propose a rapid method for predicting HEA properties using data from monometallic systems (or few-component alloys). Central to our approach is the newly introduced local surface energy (LSE) descriptor, which captures local surface reactivity at atomic resolution. We established a correlation between LSE and adsorption energies using monometallic systems. Using this correlation in a linear regression model, we successfully estimated molecular adsorption energies on HEAs with significantly higher accuracy than a conventional descriptor (i.e., generalized coordination numbers). Furthermore, we developed high-precision models by employing both classical and quantum machine learning. Our method enabled CO adsorption-energy calculations for 1000 quinary nanoparticles, comprising 201 atoms each, within a few days, considerably faster than density functional theory, which would require hundreds of years or neural network potentials, which would have taken hundreds of days. The proposed approach accelerates the exploration of the vast HEA chemical space, facilitating the design of novel catalysts.

quant-ph↗