arXiv subjects

Rei Sakuma

Publications and source records attributed to Rei Sakuma.

12 recordsLinked to original sources

arXiv2026

Point-group symmetry analysis of many-electron wavefunctions on a quantum computer

A point group is a set of spatial symmetry operations in molecular systems and is an indispensable tool for analyzing molecular orbitals and spectroscopy experiments in chemistry. Several quantum algorithms to exploit this symmetry have been proposed, but practical implementations of point-group symmetry operations and the detailed symmetry analysis of realistic many-electron wavefunctions are still missing. In this work, we propose an ancilla-free hybrid method to analyze point-group symmetries of many-electron states, which works for both abelian and non-abelian groups. For a given wavefunction, our method calculates the projection weights of point-group irreducible representations by applying orbital rotations derived from the eigenvectors of the representation matrices, making it applicable to arbitrary basis functions. The usefulness of our approach is demonstrated through numerical simulations of benzene and ferrocene molecules. Furthermore, we perform a hardware demonstration of the weight calculation of the ground state and the first excited state of benzene in $D_{2h}$ symmetry, using up to 32 qubits of IBM's ibm_kawasaki device. By combining a tensor-network based encoding scheme and error mitigation techniques, we find the weights of irreducible representations for both states are faithfully reproduced within a few percent error. Our results suggest that the proposed method serves as a practical tool for analyzing symmetry properties of many-electron wavefunctions in realistic material simulations on near-term and early fault-tolerant quantum computers.

quant-ph↗

arXiv2026

Tensor-based phase difference estimation on time series analysis

We propose a phase-difference estimation algorithm based on the tensor-network circuit compression, leveraging time-evolution data to pursue scalability and higher accuracy on a quantum phase estimation (QPE)-type algorithm. Using tensor networks, we construct circuits composed solely of nearest-neighbor gates and extract time-evolution data by four-type circuit measurements. In addition, to enhance the accuracy of time-evolution and state-preparation circuits, we propose techniques based on algorithmic error mitigation and on iterative circuit optimization combined with merging into matrix product states, respectively. Verifications using a noiseless simulator for the 8-qubit one-dimensional Hubbard model using an ancilla qubit show that the proposed algorithm achieves accuracies with 0.4--4.7\% error from a true energy gap on an appropriate time-step size, and that accuracy improvements due to the algorithmic error mitigation are observed. We also confirm the enhancement of the overlap with matrix product states through iterative optimization. Finally, the proposed algorithm is demonstrated on IBM Heron devices with Q-CTRL error suppression for 8-, 36-, and 52-qubit models using more than 4,000 2-qubit gates. These largest-scale demonstrations for the QPE-type algorithm represent significant progress not only toward practical applications of near-term quantum computing but also toward preparation for the era of error-corrected quantum devices.

quant-ph↗

arXiv2025

Quantum phase estimation based filtering: performance analysis and application to low-energy spectral calculation

Filtering is an important technique in quantum computing used for isolating or enhancing some specific states of quantum many-body systems. In this paper, we analyze the performance of filters based on the quantum phase estimation (QPE) algorithm, in which filtering removes states associated with bitstrings in the ancilla register above a given threshold. We show that when the conventional rectangular window function is used for the QPE input state, the resulting filter function exhibits an oscillating behavior known as the Gibbs phenomenon. We also show that in the case of the sine and Kaiser windows, this phenomenon is suppressed. Furthermore, we perform numerical simulations to compare the number of necessary queries to the Hamiltonian time evolution operation of for the QPE-based filtering algorithm and the quantum eigenvalue transformation of unitary matrices with real polynomials (QETU). We find that the number of queries required for Kaiser window-based filtering is comparable to that for QETU with optimized phase angles. As an application of the QPE-based filter, we also study a two-step algorithm for low-energy spectral simulations, composed of a coarse grid for filtering and a fine grid for obtaining final high-resolution spectra. As a benchmark of the proposed scheme for realistic continuous spectra, we present the density-of-states (DOS) calculation of antiferromagnetic type-II MnO in a one-particle approximation.

quant-ph↗

arXiv2024

Hamiltonian simulation-based quantum-selected configuration interaction for large-scale electronic structure calculations with a quantum computer

Quantum-selected configuration interaction (QSCI) is an approach for quantum chemical calculations using current quantum computers. In conventional QSCI, Slater determinants used for the wave function expansion are sampled by iteratively performing approximate wave function preparation and subsequent measurement in the computational basis, and then the subspace Hamiltonian matrix is diagonalized on a classical computer. In this approach, preparation of a high-quality approximate wave function is necessary to accurately compute total energies. Here we propose a Hamiltonian simulation-based QSCI (HSB-QSCI) to avoid this difficulty, by sampling the Slater determinants from quantum states generated by the real-time evolution of approximate wave functions. We provide numerical simulations for the lowest spin-singlet and triplet states of oligoacenes (benzene, naphthalene, and anthracene), phenylene-1,4-dinitrene, and hexa-1,2,3,4,5-pentaene. We found that the HSB-QSCI is applicable not only to molecules where the Hartree--Fock provides a good approximation of the ground state, but also to strongly correlated systems where preparing a high-quality approximate wave function is hard. Hardware demonstrations of the HSB-QSCI are also reported for carbyne molecules expressed by up to 36 qubits, using an IBM Quantum processor. The HSB-QSCI captures more than 99.18\% of the correlation energies in the active space by considering about 1\% of all the Slater determinants in 36 qubit systems, illustrating the ability of the proposed method to efficiently consider important electronic configurations.

quant-ph↗

arXiv2024

Tensor-based quantum phase difference estimation for large-scale demonstration

We develop an energy calculation algorithm leveraging quantum phase difference estimation (QPDE) scheme and a tensor-network-based unitary compression method in the preparation of superposition states and time-evolution gates. Alongside its efficient implementation, this algorithm reduces depolarization noise affections exponentially. We demonstrated energy gap calculations for one-dimensional Hubbard models on IBM superconducting devices using circuits up to 32-system (plus one-ancilla) qubits, a five-fold increase over previous QPE demonstrations, at the 7242 controlled-Z gate level of standard transpilation, utilizing a Q-CTRL error suppression module. Additionally, we propose a technique towards molecular executions using spatial orbital localization and index sorting, verified linear polyene simulations up to 21 qubits. Since QPDE can handle the same objectives as QPE, our algorithm represents a leap forward in quantum computing on real devices.

quant-ph↗

arXiv2024

Entanglement-assisted phase estimation algorithm for calculating dynamical response functions

Dynamical response functions are fundamental quantities to describe the excited-state properties in quantum many-body systems. Quantum algorithms have been proposed to evaluate these quantities by means of quantum phase estimation (QPE), where the energy spectra are directly extracted from the QPE measurement outcomes in the frequency domain. Accurate estimation of excitation energies and transition probabilities with these QPE-based approaches is, however, challenging because of the problem of spectral leakage (or peak broadening) which is inherent in the QPE algorithm. To overcome this issue, in this work we consider an extension of the QPE-based approach adopting the optimal entangled input states, which is known to achieve the Heisenberg-limited scaling for the estimation precision. We show that with this method the peaks in the calculated energy spectra are more localized than those calculated by the original QPE-based approaches, suggesting the mitigation of the spectral leakage problem. By analyzing the probability distribution with the entangled phase estimation, we propose a simple scheme to better estimate both the transition energies and the corresponding transition probabilities of the peaks of interest in the spectra. The validity of our prescription is demonstrated by numerical simulations in various quantum many-body problems: the spectral function of a simple electron-plasmon model in condensed-matter physics, the dipole transitions of the H$_2$O molecule in quantum chemistry, and the electromagnetic transitions of the $^6$Li nucleus in nuclear physics.

quant-ph↗

arXiv2024

Light-cone feature selection for quantum machine learning

Feature selection plays an essential role in improving the predictive performance and interpretability of trained models in classical machine learning. On the other hand, the usability of conventional feature selection could be limited for quantum machine learning tasks; the technique might not provide a clear interpretation on embedding quantum circuits for classical data tasks and, more importantly, is not applicable to quantum data tasks. In this work, we propose a feature selection method with a specific focus on quantum machine learning. Our scheme treats the light-cones (i.e., subspace) of quantum models as features and then select relevant ones through training of the corresponding local quantum kernels. We numerically demonstrate its versatility for four different applications using toy tasks: (1) feature selection of classical inputs, (2) circuit architecture search for data embedding, (3) compression of quantum machine learning models and (4) subspace selection for quantum data. The proposed framework paves the way towards applications of quantum machine learning to practical tasks. Also, this technique could be used to practically test if the quantum machine learning tasks really need quantumness, while it is beyond the scope of this work.

quant-ph↗

arXiv2023

Quantum Computed Green's Functions using a Cumulant Expansion of the Lanczos Method

In this paper, we present a quantum computational method to calculate the many-body Green's function matrix in a spin orbital basis. We apply our approach to finite-sized fermionic Hubbard models and related impurity models within Dynamical Mean Field Theory, and demonstrate the calculation of Green's functions on Quantinuum's H1-1 trapped-ion quantum computer. Our approach involves a cumulant expansion of the Lanczos method, using Hamiltonian moments as measurable expectation values. This bypasses the need for a large overhead in the number of measurements due to repeated applications of the variational quantum eigensolver (VQE), and instead measures the expectation value of the moments with one set of measurement circuits. From the measured moments, the tridiagonalised Hamiltonian matrix can be computed, which in turn yields the Green's function via continued fractions. While we use a variational algorithm to prepare the ground state in this work, we note that the modularity of our implementation allows for other (non-variational) approaches to be used for the ground state.

cond-mat.str-el↗

arXiv2023

Quantum chemistry simulation of ground- and excited-state properties of the sulfonium cation on a superconducting quantum processor

The computational description of correlated electronic structure, and particularly of excited states of many-electron systems, is an anticipated application for quantum devices. An important ramification is to determine the dominant molecular fragmentation pathways in photo-dissociation experiments of light-sensitive compounds, like sulfonium-based photo-acid generators used in photolithography. Here we simulate the static and dynamic electronic structure of the H$_3$S$^+$ molecule, taken as a minimal model of a triply-bonded sulfur cation, on a superconducting quantum processor of the IBM Falcon architecture. To this end, we generalize a qubit reduction technique termed entanglement forging or EF [A. Eddins et al., Phys. Rev. X Quantum, 3, 010309 (2022)], currently restricted to the evaluation of ground-state energies, to the treatment of molecular properties. While, in a conventional quantum simulation, a qubit represents a spin-orbital, within EF a qubit represents a spatial orbital, reducing the number of required qubits by half. We combine the generalized EF with quantum subspace expansion [W. Colless et al, Phys. Rev. X 8, 011021 (2018)], a technique used to project the time-independent Schrodinger equation for ground and excited states in a subspace. To enable experimental demonstration of this algorithmic workflow, we deploy a sequence of error-mitigation techniques. We compute dipole structure factors and partial atomic charges along the ground- and excited-state potential energy curves, revealing the occurrence of homo- and heterolytic fragmentation. This study is an important step toward the computational description of photo-dissociation on near-term quantum devices, as it can be generalized to other photodissociation processes and naturally extended in different ways to achieve more realistic simulations.

quant-ph↗

arXiv2022

Resolution enhancement of one-dimensional molecular wavefunctions in plane-wave basis via quantum machine learning

Super-resolution is a machine-learning technique in image processing which generates high-resolution images from low-resolution images. Inspired by this approach, we perform a numerical experiment of quantum machine learning, which takes low-resolution (low plane-wave energy cutoff) one-particle molecular wavefunctions in plane-wave basis as input and generates high-resolution (high plane-wave energy cutoff) wavefunctions in fictitious one-dimensional systems, and study the performance of different learning models. We show that the trained models can generate wavefunctions having higher fidelity values with respect to the ground-truth wavefunctions than a simple linear interpolation, and the results can be improved both qualitatively and quantitatively by including data-dependent information in the ansatz. On the other hand, the accuracy of the current approach deteriorates for wavefunctions calculated in electronic configurations not included in the training dataset. We also discuss the generalization of this approach to many-body electron wavefunctions.

quant-ph↗

arXiv2014

Dynamical screening in La2CuO4

We show that the dynamical screening of the Coulomb interaction among Cu-d electrons in high-Tc cuprates is very strong and that a proper treatment of this effect is essential for a consistent description of the electronic structure. In particular, we find that ab-initio calculations for undoped La2CuO4 yield an insulator only if the frequency dependence of the Coulomb interaction is taken into account. We also identify a collective excitation in the screened interaction at 9 eV which is rather localized on the copper site, and which is responsible for a satellite structure at energy -13 eV, located below the p bands.

cond-mat.str-el↗

arXiv2008

First order metal-insulator transision in band overlap mechanism

We present a method to analyze the metal-insulator transition (MIT) due to the band overlap mechanism. It is based on a model with the knowledge of the homogeneous electron gas, combined with results based on the quasiparticle self-consistent $GW$ method. Because of the long-range nature of the Coulomb interaction, the MIT occurs as the first-order phase transition, that is, the band gap becomes negative (band overlap) suddenly at some critical lattice constant.

cond-mat.mtrl-sci↗