arXiv subjects

Seiji Yunoki

Publications and source records attributed to Seiji Yunoki.

At least 19 recordsLinked to original sources

arXiv2026

Sample-based Quantum Diagonalization via Density Matrix Renormalization Group

We introduce SqDMRG, which combines sample-based quantum diagonalization (SQD) with the density matrix renormalization group (DMRG). SqDMRG expands the sampled determinant space into a product of fragment spaces constructed from the samples, and solves the Hamiltonian in this product space using DMRG. We demonstrate the method on benzene pentamer and decamer clusters using classical reference samples, and on the pentamer using quantum samples obtained from the IBM Quantum Nighthawk r2 processor ibm phoenix. It achieves sub-kcal/mol accuracy with far fewer configurations than direct diagonalization.

quant-ph↗

arXiv2026

Broken-Symmetry Reference States for Sample-Based Quantum Diagonalization with Near-DMRG Accuracy

Sample-based quantum diagonalization (SQD) accuracy remains limited for strongly correlated systems like iron-sulfur clusters. This accuracy heavily depends on the mean-field reference state, which determines the sampling ansatz and configuration-recovery target. We extend the qcsc-prefect workflow to support a broken-symmetry unrestricted Hartree-Fock (BS-UHF) starting point, derived from atom-localized antiferromagnetic initial guesses. For the $[Fe_4S_4(SCH_3)_4]^{2-}$ cubane (54 electrons, 36 orbitals, 72 data qubits; 88 total qubits), this lowers the mean-field reference by $\approx 534$ mHa relative to the Restricted Hartree-Fock (RHF) solution (-327.0809 vs. -326.5474 Ha). Combined with deep multi-step configuration recovery on ROQUO GPU nodes at subspace dimension $d = 3 \times 10^{10}$, we reach $E = -327.235076$ Ha. This is 4.3 mHa above the finite-$D = 4000$ DMRG value (-327.2393952 Ha) and $\sim 512-517$ mHa below a previous same-active-space RHF-reference SQD calculation. Extrapolating to the $d \to \infty$ limit via a Gaussian-in-log $d$ fitting ansatz gives an asymptotic estimate $E_\infty = -327.244468$ Ha $\pm 1.05$ mHa, just 0.33 mHa above our discarded-weight-extrapolated DMRG estimate ($E_{D\to\infty} = -327.244797$ Ha $\pm 0.46$ mHa), well within their combined $1σ$ uncertainty. We characterize the determinant-subspace composition driving convergence, show matching validation on $[Fe_2S_2(SCH_3)_4]^{2-}$, and report multi-HPC execution on Fugaku, Miyabi-G, and ROQUO. We conclude that reference-state preparation, rather than subspace size alone, is a key driver of SQD energy accuracy under strong electronic correlation.

quant-ph↗

arXiv2026

Tensor computing application programming interface: An application-oriented, lightweight API for portable high-performance tensor network computations

Tensor networks (TNs) are a central computational tool in quantum science and artificial intelligence. However, the lack of a unified software interface across tensor-computing frameworks severely limits the portability of TN applications, coupling algorithmic development to specific hardware and software back ends. To address this challenge, we introduce the Tensor Computing Application Programming Interface (TCAPI)---an application-oriented, lightweight API designed to enable framework-independent, high-performance TN applications, currently specified for \texttt{C++} and \texttt{Python}. TCAPI provides well-defined type-system conventions that abstract tensor objects, together with a minimal yet expressive set of core functions for essential tensor manipulations and tensor linear-algebra operations, with operation semantics shared across language specifications. Through numerical demonstrations of representative tensor-network applications implemented in \texttt{C++} using TCAPI, we show that code written against TCAPI can be migrated seamlessly across heterogeneous hardware and software platforms while achieving performance comparable to native framework implementations. We further provide open-source implementations of TCAPI based on \textit{Cytnx} and \textit{ITensor} in \texttt{C++}, and on \textit{NumPy} in \texttt{Python}, demonstrating its practicality and ease of integration with existing tensor-computing frameworks.

quant-ph↗

arXiv2026

Vibrational Sample-Based Quantum Diagonalization with Unitary-Cluster-Jastrow Ansätze on IBM QPUs

We extend sample-based quantum diagonalization (SQD) to anharmonic vibrational spectra and use it to benchmark how vibrational ansätze scale on present-day hardware under a direct one-hot encoding (up to 72 qubits). Five particle-conserving circuits are warm-started through vibrational coupled-cluster (VCC) t1, t2 amplitudes: UVCCSD, the compact heuristic circuit (CHC), and three unitary-cluster-Jastrow ansätze introduced here, VLUCJ, Vg-uCJ, and VIm-uCJ. A self-consistent configuration-recovery loop reconstructs correlated states from hardware samples in the low-retention regime where naïve post-selection breaks down, enabling the calculation of ground-state energies for H$_2$O, CH$_2$O, and CH$_2$ClF (3-9 modes). We compare harmonic-oscillator and vibrational self-consistent-field (VSCF, "modal") reference bases. In the small-modal-basis regime relevant for near-term devices, the modal basis is typically more accurate than the harmonic-oscillator representation; empirically, it also yields substantially better-converged hardware sampling for deep circuits at these qubit counts. Across ansätze, we find a clear retention/accuracy tradeoff when scaling: the new VLUCJ circuit is the most hardware-robust, retaining the largest fraction of physically valid one-hot outcomes, whereas the more expressive uCJ variants and UVCCSD can recover more correlation when retention is sufficient. Finally, we extend the recovery protocol to excited states by targeting a chosen per-mode VSCF occupation and warm-starting it with a state-specific VCC calculation, and we demonstrate the calculation of one representative fundamental vibrational frequency per molecule on IBM QPUs.

quant-ph↗

arXiv2026

Extending the Handover-Iterative VQE to Challenging Strongly Correlated Systems: $N_2$ and Fe-S Cluster

Accurately describing strongly correlated electronic systems remains a central challenge in quantum chemistry, as electron-electron interactions give rise to complex many-body wavefunctions that are difficult to capture with conventional approximations. Classical wavefunction-based approaches, such as the Semistochastic Heat-bath Configuration Interaction (SHCI) and the Density Matrix Renormalization Group (DMRG), currently define the state of the art, systematically converging toward the Full Configuration Interaction (FCI) limit, but at a rapidly increasing computational cost. Quantum computing algorithms promise to alleviate this scaling bottleneck by leveraging entanglement and superposition to represent correlated states more compactly. We introduced the Handover-Iterative Variational Quantum Eigensolver (HI-VQE) as a practical quantum computing algorithm with an iterative "handover" mechanism that dynamically exchanges information between quantum and classical computers, even using Noisy Intermediate-Scale Quantum (NISQ) computers. In this work, we extend the HI-VQE to benchmark two prototypical strongly correlated systems, the nitrogen molecule $N_2$ and iron-sulfur (Fe-S) cluster, which serve as stringent tests for both classical and quantum electronic-structure methods. By comparing HI-VQE results against Heat-bath Configuration Interaction (HCI) benchmarks, we assess its accuracy, scalability, and ability to capture multireference correlation effects. Achieving quantitative agreement on these canonical systems demonstrates a viable pathway toward quantum-enhanced simulations of complex bioinorganic molecules, catalytic mechanisms, and correlated materials.

quant-ph↗

arXiv2026

Dissipative ground-state preparation of a quantum spin chain on a trapped-ion quantum computer

We demonstrate a dissipative protocol for ground-state preparation of a quantum spin chain on a trapped-ion quantum computer. As a first step, we derive a Kraus representation of a dissipation channel for the protocol recently proposed by Ding et al. [Phys. Rev. Res. 6, 033147 (2024)] that still holds for arbitrary temporal discretization steps, extending the analysis beyond the Lindblad dynamics regime. The protocol guarantees that the fidelity with the ground state monotonically increases (or remains unchanged) under repeated applications of the channel to an arbitrary initial state, provided that the ground state is the unique steady state of the dissipation channel. Using this framework, we implement dissipative ground-state preparation of a transverse-field Ising chain for up to 19 spins on the trapped-ion quantum computer Reimei provided by Quantinuum. Despite the presence of hardware noise, the dynamics consistently converges to a low-energy state far away from the maximally mixed state even when the corresponding quantum circuits contain as many as 4110 entangling gates, demonstrating the intrinsic robustness of the protocol. By applying zero-noise extrapolation, the resulting energy expectation values are systematically improved to agree with noiseless simulations within statistical uncertainties.

quant-ph↗

arXiv2026

Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes

We develop a workflow decomposing a molecule into fragments via quantum embedding and simulating them with a heterogeneous quantum-classical (HQC) method. We sample fragment electronic configurations on two 156-qubit quantum processors (ibm$\_$cleveland, ibm$\_$kobe), using up to 94 qubits, running 21,006 circuits for over 239 hours, collecting $3.0 \cdot 10^9$ measurement outcomes -- the most resource-intensive HQC computation for quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on supercomputers Fugaku, Miyabi-G, and ROQUO, achieving 72.5$\%$ parallel efficiency with scalable distributed linear algebra kernels. We simulate two protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608, 12,635 atoms), demonstrate $>40\times$ increase in system size and up to $210\times$ improvement in accuracy over previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies. We present the first HQC protein-ligand binding prediction using a mixed-basis set and an automated end-to-end workflow enabling practical HQC calculations of large protein systems.

quant-ph↗

arXiv2026

Dipolar and quadrupolar spin supersolid states in a spin-1 triangular antiferromagnet

We present a systematic numerical study of the spin-1 antiferromagnetic Heisenberg model on the triangular lattice in an out-of-plane magnetic field, using Density Matrix Renormalization Group (DMRG) methods. By mapping out the quantum phase diagram as a function of the single-ion anisotropy $D_z$ and magnetic field, we identify distinct dipolar and quadrupolar spin supersolid states, characterized by spontaneous U(1) symmetry breaking with finite spin superfluid stiffness coexisting with longitudinal translational symmetry breaking. At zero field, the dipolar spin supersolid with a 'Y'-type spin configuration persists down to $D_z = 0$, whereas the quadrupolar spin supersolid prevails at large $D_z$. At intermediate fields, the phase diagram is dominated by an up-up-down phase. At high fields below saturation, a quadrupolar spin superfluid emerges in the large-$D_z$ regime, whereas a dipolar spin supersolid with a 'V'-type spin configuration dominates at small $D_z$. These phases are characterized through their order parameters and spin superfluid stiffness using calculations on various system sizes. Furthermore, the dynamical spin structure factor is obtained across the phase diagram, where characteristic spectral signatures of different phases are observed, including the gapless Goldstone mode and the roton-like minima. These features are directly accessible to inelastic neutron scattering experiments. Our results provide a theoretical understanding of the interplay between frustrations, anisotropy, and Zeeman interactions in driving distinct spin supersolid phases in the spin-1 system, which are relevant to various triangular-lattice antiferromagnets such as Na$_2$BaNi(PO$_4$)$_2$ and K$_2$Ni(SeO$_3$)$_2$.

cond-mat.str-el↗

arXiv2026

Resolving Structure in Prethermal Floquet Dynamics with Precision Quantum Computation

Periodically driven interacting quantum many-body systems can exhibit long-lived prethermal dynamics, where local observables retain coherent structure even as entanglement and operator complexity grow. Accessing this regime at the system sizes and times needed to determine physical properties of the prethermal state remains a central challenge: state-of-the-art classical methods become unreliable, while noise in quantum hardware degrades observable expectation values. Here we overcome these limitations for a Floquet Ising magnet realized on a heavy-hex lattice. Using the advanced error mitigation software QESEM on an IBM Heron r3 superconducting quantum processor, we measure magnetization dynamics with percent-level precision and resolve long-lived subharmonic prethermal oscillations in systems of up to 74 qubits. These experiments reach regimes for which leading tensor-network simulations fail to converge, while sparse Pauli-path simulations remain strongly truncation dependent despite extensive computations on advanced GPUs and the Fugaku supercomputer. Leveraging this quantum-accessible regime, we extend finite-size scaling to larger systems and find an unexpectedly slow decrease of the oscillation amplitude with system size, providing strong evidence that this oscillatory response persists in the thermodynamic limit of heavy-hex ladders. A hierarchy of mitigation and validation tests, including unbiased error mitigation, agreement between independent mitigation estimators, noise-model validation on the superconducting hardware, and cross-platform corroboration at selected Floquet cycles on Quantinuum System Model H2 and Quantinuum Helios trapped-ion hardware, supports the reliability of these findings. Our work establishes error-mitigated quantum processors as quantitative scientific instruments for discovering new physics in non-equilibrium quantum matter.

quant-ph↗

arXiv2026

Dissipationless dynamics of spin supersolid states in a spin-1/2 triangular antiferromagnet with impurities

Motivated by recent experimental evidence for spin supersolid states in triangular-lattice compounds, we numerically investigate the dynamical properties of magnetic field-induced phases in the spin-1/2 easy-axis triangular antiferromagnetic Heisenberg model in the presence of magnetic impurities. In both weak- and strong-field spin supersolid states, the gapless Goldstone mode at the $K$ points remains robust against impurities, which is a direct manifestation of spin superfluidity. By contrast, at the same impurity density, impurities induce a splitting of the magnon bands in the conventional magnetic state, the so-called up-up-down state. In addition, the finite superfluid stiffness probed by the twisted phase in the spin supersolid states is consistent with the excitation spectrum. We argue that the excitation spectrum with impurities provides direct spectroscopic evidence for dissipationless spin dynamics in the spin supersolid states, which is experimentally accessible via inelastic neutron scattering.

cond-mat.str-el↗

arXiv2026

Observation of Improved Accuracy over Classical Sparse Ground-State Solvers using a Quantum Computer

Demonstrating quantum advantage over classical algorithms for ground state energy problems is an outstanding open problem in quantum computation. We experimentally demonstrate that a quantum algorithm can outperform classical selected configuration interaction (SCI) methods, a key family of techniques used in computational chemistry and condensed matter physics. We construct a class of local Hamiltonian problems with sparse ground states, and show that SCI fails to find the ground state of a 49-qubit instance. We then show that sample-based Krylov quantum diagonalization, run on an IBM Heron R3 processor, succeeds at the same task. While the problem is solvable classically using iterative solvers designed to target our Hamiltonian construction, this work resolves the question of whether a sample-based quantum diagonalization algorithm can outperform standard SCI heuristics.

quant-ph↗

arXiv2026

Simulating the Dynamics of Markovian Quantum Processes by Quantum Collision Models on Quantum Computers

Hamiltonian dynamics have been widely implemented on noisy intermediate-scale quantum devices in recent years. In contrast, experimental demonstrations of Markovian quantum dynamics remain limited, because implementing nonunitary evolution on quantum computers is challenging. Quantum collision models provide a natural approach to this problem by coupling the system to ancillas to realize dissipation. However, previous implementations of quantum collision models on quantum computers have typically been restricted to one or two system qubits and fewer than 12 time steps, owing to noise, circuit depth, the overhead of ancilla reset, and limited qubit resources. In this work, we experimentally simulate Markovian quantum processes with local and nonlocal dissipation on both trapped-ion and superconducting quantum computers. By employing hardware-specific ancilla strategies, we realize simulations with up to seven system qubits, corresponding to 13 qubits in total, and 40 time steps. Our results demonstrate that, even for the same physical model, the optimal implementation strategy depends strongly on the hardware characteristics of the quantum computer.

quant-ph↗

arXiv2026

Filter-assisted quantum subspace diagonalization via wavefunction sparsity engineering

Subspace diagonalization techniques based on quantum sampling, such as quantum selected configuration interaction (QSCI) and sample-based quantum diagonalization (SQD), have recently emerged as promising quantum-centric approaches for approximating ground-state energies of many-body systems. However, their performance is fundamentally limited by an intrinsic trade-off between sampling efficiency and the sparsity of the ground-state wavefunction, which becomes particularly severe in strongly correlated systems. Here, we introduce a filter-assisted SQD protocol that engineers wavefunction sparsity via a quantum filter, i.e., a unitary transformation of the Hamiltonian designed to concentrate the ground-state weight onto a small number of computational basis states. Using the Gini coefficient as a robust sparsity measure, we establish a quantitative relationship between wavefunction sparsity and the resource requirements of SQD, providing theoretical bounds on the required subspace dimension and sampling cost. To realize the quantum filter, we employ a tensor-network-based circuit-encoding algorithm that maps target states to quantum circuits with controllable fidelity. We benchmark our approach on the quantum Ising model with transverse and longitudinal fields using both numerical simulations and quantum hardware experiments. Our results demonstrate that, compared with standard SQD, the proposed protocol significantly enhances wavefunction sparsity, reduces ground-state energy estimation errors by orders of magnitude, and substantially lowers sampling overhead. These findings establish filter-assisted subspace diagonalization as a powerful and scalable framework for quantum many-body calculations in the strongly correlated regime.

quant-ph↗

arXiv2026

Emergent Spin Supersolids in Frustrated Quantum Materials

Recent years have witnessed the emergence of spin supersolids in frustrated quantum magnets, establishing a material-based platform for supersolidity beyond its original context in solid helium. A spin supersolid is characterized by the coexistence of longitudinal spin order that breaks lattice translational symmetry and transverse spin order associated with the spontaneous breaking of the spin U(1) symmetry. Extensive experimental investigations, together with advanced numerical studies, have now revealed a coherent and internally consistent picture of these phases, substantially deepening our understanding of supersolidity in quantum magnetic materials. Beyond their fundamental interest as exotic quantum states, potential applications in highly efficient demagnetization cooling have been supported by a giant magnetocaloric effect observed in candidate materials. Moreover, the possible dissipationless spin supercurrents could open promising perspectives for spin transport and spintronic applications. This review summarizes recent progress on emergent spin supersolids in frustrated triangular-lattice quantum antiferromagnets, surveys experimental evidence from thermodynamic and spectroscopic measurements, and compares these results with theoretical studies of minimal models addressing global phase diagrams, ground state properties, and collective excitations. In addition, this review discusses characteristic spin-transport phenomena and outlines future directions for exploring spin supersolids as functional quantum materials.

cond-mat.str-el↗

arXiv2026

Noise and Configuration Recovery Impact on Quantum Selected Configuration Interaction

Quantum-selected configuration interaction (QSCI) is a promising hybrid quantum-classical approach in which a quantum device generates configurations for subsequent classical diagonalization. Here, we analyze the performance of QSCI combined with the local unitary cluster Jastrow (LUCJ) ansatz, focusing on the interplay between ansatz expressivity, sampling, noise, and configuration recovery. Using the dissociation of N2 in a large active space as a benchmark, we show that noiseless LUCJ sampling produces compact and biased configurational spaces, limiting the accuracy of the resulting CI energies, particularly in strongly correlated regimes. By introducing a simple noise model, we demonstrate that sampling noise can enhance Hilbert-space exploration by generating additional configurations beyond those supported by the ideal ansatz. When combined with configuration recovery, this leads to systematically improved energies. Moreover, recovery alone (starting from randomly generated configurations) can efficiently construct accurate CI spaces, highlighting its central role in QSCI.

quant-ph↗

arXiv2026

High-Precision Variational Quantum SVD via Classical Orthogonality Correction

Evaluating the entanglement spectrum is essential for characterizing exotic quantum phases such as quantum criticality and topological order. However, for large quantum many-body systems, this task is hindered by the exponential measurement complexity of standard tomographic techniques. To address this challenge, we introduce a hybrid quantum-classical variational framework for partial singular value decomposition of bipartite states, built on the canonical form of matrix product states. We employ a deflation-based optimization approach to sequentially extract dominant and subdominant Schmidt components of target states. Because hardware noise and finite circuit depth can compromise the mutual orthogonality of these extracted vectors, we propose an improved deflation algorithm incorporating explicit classical orthogonality correction. This classical post-processing acts as an error-filtering mechanism, enabling shallow and suboptimal quantum circuits. As a result, numerical accuracy is decoupled from quantum circuit optimization, mitigating optimization difficulties caused by barren plateaus and hardware noise. Furthermore, shallow ansatzes enable a concurrent execution strategy. Overlap matrices are evaluated by classical tensor network contractions, while cross terms between the target state and the extracted vectors are computed using an auxiliary reference state. This concurrent hybrid design improves computational throughput and bypasses the overhead of controlled target-state preparation. Numerical benchmarks on the ground states of one- and two-dimensional Heisenberg models demonstrate improved accuracy and numerical stability. By mitigating hurdles of circuit depth, optimization hardness, and measurement complexity, our framework provides a robust pathway for large-scale entanglement spectrum estimation on advanced near-term quantum devices.

quant-ph↗

arXiv2026

Noise-stabilized discrete time crystals on digital quantum processors

Floquet many-body phases such as discrete time crystals (DTCs) are typically fragile to imperfections, and stabilizing them on noisy quantum hardware remains a central challenge in nonequilibrium quantum physics. Here, we use IBM Eagle and Heron superconducting processors to implement Floquet dynamics of a kicked Ising model on two-dimensional Kagome lattices, engineered via ancilla-assisted embeddings into the heavy-hex connectivity of the devices. By combining error-mitigated measurements on quantum hardware with matrix-product-state simulations incorporating an ancilla-noise model constructed from experimental device data, we observe long-lived subharmonic magnetization oscillations that are stabilized -- rather than destroyed -- by structured quantum noise. Across different two-dimensional lattice geometries, increasing cases beyond Kagome lattices, and with or without boundary symmetry-charge pumping, ancilla errors effectively act as spatiotemporal disorder that induces stochastic sign flips of the Ising couplings, providing a unified mechanism for robust period-doubling responses. When symmetry-charge pumping is present, intrinsic boundary-localized $π$ modes cooperate with this disorder to yield a boundary-assisted DTC characterized by suppressed scrambling and sharply localized dynamics. In contrast, in implementations without pumping, the noiseless dynamics rapidly thermalize and exhibit no subharmonic order, whereas the same noise process alone generates a DTC-like long-lived subharmonic response over experimentally accessible time windows. These results identify engineered ancilla noise as a practical control knob for inducing, stabilizing, and geometrically tailoring nonequilibrium dynamical order on scalable superconducting quantum processors.

quant-ph↗

arXiv2026

Phase transitions in parametrized quantum circuits

Phase transitions are among the most intriguing phenomena in physical systems, yet their behavior near criticality remain challenging to study using classical algorithms. Parameterized quantum circuits (PQCs) offer a promising approach to investigating such regimes on practical quantum computers. However, in order to use it to probe critical behavior, a PQC itself should be non-trivial and exhibit a phase transition and non-analyticity -- a property that has not yet been clearly identified. In this work, we identify a mechanism for generating non-analyticities intrinsically in PQCs. As a concrete realization, we construct a class of sequential PQCs whose observable expectation value is a non-analytic function of the circuit parameter in the infinite volume limit, showing that the prepared PQC states undergo a phase transition at the non-analytic points. The entanglement and the identified order parameter have distinct behaviors in different phases, revealing a phase diagram of the PQC state. We show that classical simulation of this PQC based on tensor networks and Pauli propagation gets less efficient in the vicinity of the phase transition point, indicating a physically motivated route towards practical quantum advantage using PQCs with phase transitions.

quant-ph↗