Quantum simulation of field-tunable spin spectroscopy of the quantum magnet Cs2CoCl4 on a trapped-ion quantum computer
Elias Kokkas, Nora Bauer, Justin Provazza, Mark E. Nowakowski, Kathleen Hamilton, Gilles Buchs, Andrew Sornborger, Travis S. Humble, Ananth Kaushik, Martin Roetteler
quant-phSubmitted Oct 2, 2026• Announced Oct 5, 2026• 0 citations
The collective excitation spectra of quantum magnets provide a direct test of quantum computers as tools for studying strongly correlated matter. Hardware noise limits the circuit depths available for these calculations, leaving the question of how much magnetic information can survive circuit compression. Using IonQ's 36 qubit Forte Enterprise processor, we simulate the magnetic excitation spectra and spin dynamics of a 36 site Heisenberg XXZ chain describing the quantum magnet Cs2CoCl4 in a transverse field using quantum circuits containing just 420 two qubit gates. The workflow combines an ancilla free local kick protocol with classical tensor network compression, encoding both ground state preparation and time evolution. The reconstructed dynamical structure factors capture the evolution from a broad two spinon continuum toward a dispersive branch with magnon character near the expected critical field. We observe an interesting field driven sign reversal of the nearest neighbor spin correlations which shows a complementary signature of this magnetic evolution. Our error mitigation methods bring the reconstructed total spectra into close agreement with density matrix renormalization group calculations, with Pearson correlation and structural similarity above 0.9 and normalized mean squared error below 0.15. We also perform comparisons with experimental inelastic neutron scattering measurements to connect the recovered excitations to the material's magnetic response. Our results demonstrate that circuit compression and error mitigation allow noisy quantum processors to recover both collective excitations and local magnetic correlations providing a quantitative benchmark for quantum spectroscopy and a basis for extending these methods to more complex magnetic systems.
The collective excitation spectra of quantum magnets provide a direct test of quantum computers as tools for studying strongly correlated matter. Hardware noise limits the circuit depths available for these calculations, leaving the question of how much magnetic information can survive circuit compression. Using IonQ's 36 qubit Forte Enterprise processor, we simulate the magnetic excitation spectra and spin dynamics of a 36 site Heisenberg XXZ chain describing the quantum magnet Cs2CoCl4 in a transverse field using quantum circuits containing just 420 two qubit gates. The workflow combines an ancilla free local kick protocol with classical tensor network compression, encoding both ground state preparation and time evolution. The reconstructed dynamical structure factors capture the evolution from a broad two spinon continuum toward a dispersive branch with magnon character near the expected critical field. We observe an interesting field driven sign reversal of the nearest neighbor spin correlations which shows a complementary signature of this magnetic evolution. Our error mitigation methods bring the reconstructed total spectra into close agreement with density matrix renormalization group calculations, with Pearson correlation and structural similarity above 0.9 and normalized mean squared error below 0.15. We also perform comparisons with experimental inelastic neutron scattering measurements to connect the recovered excitations to the material's magnetic response. Our results demonstrate that circuit compression and error mitigation allow noisy quantum processors to recover both collective excitations and local magnetic correlations providing a quantitative benchmark for quantum spectroscopy and a basis for extending these methods to more complex magnetic systems.
The elementary excitation spectrum of a many-body quantum system encodes many key properties, including phenomena as diverse as transport, thermalisation and ground state structure. Excitation spectra of strongly correlated systems are typically encoded in dynamical structure factors, which are demanding to measure experimentally and challenging to compute classically. Here we use quench spectroscopy on a superconducting quantum processor to extract excitation spectra of spin chains of L=101 spins. By tailoring the combination of quench protocol and observable, we selectively access distinct excitation sectors across several phases of the spin-1/2 XXZ chain, resolving free magnons, multi-magnon bound states, and two-spinon continua. Notably, we demonstrate that the protocol does not rely on ground state preparation: in the classically challenging gapless regime, we extract spectra directly from the quench dynamics of easily prepared product states, a procedure that is natural and straightforward on quantum hardware. Our work establishes quench spectroscopy as a fast and flexible probe of many-body excitation spectra on digital quantum hardware, introduces a novel quench protocol that does not require costly state preparation routines, and provides a scalable route towards regimes where classical simulation may become intractable.
With the latest advances in quantum computing technology, we are gradually moving from the noisy intermediate-scale quantum (NISQ) era characterized by hardware limited in the number of qubits and plagued with quantum noise, to the age of quantum utility where both the newest hardware and software methods allow for tackling problems which have been deemed difficult or intractable with conventional classical methods. One of these difficult problems is the simulation of one-dimensional (1D) nuclear magnetic resonance (NMR) spectra, a major tool to learn about the structure of molecules, helping the design of new materials or drugs. Using advanced error mitigation and error suppression techniques from Q-CTRL together with the latest commercially available superconducting-qubit quantum computer from IBM and trapped-ion quantum computer from IonQ, we present the quantum Hamiltonian simulation of liquid-state 1D NMR spectra in the high-field regime for spin systems up to 34 spins. Our pipeline has a major impact on the ability to execute deep quantum circuits with the reduction of quantum noise, improving mean square error by a factor of 22. It allows for the execution of deep quantum circuits and obtaining salient features of the 1D NMR spectra for both 16-spin and 22-spin systems, as well as a 34-spin system, which lies beyond the regime where unrestricted full Liouvillespace simulations are practical (32 spins, the Liouville limit). Our work is a step toward near-term quantum utility in NMR spectroscopy.
Quantum simulation promises to advance materials discovery by accurately simulating complex states of matter, their microscopic excitations, and macroscopic response functions. The central challenge in resolving the underlying interacting dynamics is to combine high-fidelity evolution with the sophisticated control necessary to manipulate individual quasi-particles in quantum many-body states. Here, we report on high-precision simulation of both linear and non-linear response functions in a 2D XY spin-1/2 magnet using an analog-digital superconducting processor of up to 97 qubits. By interleaving digital gates with analog evolution precisely characterized via Hamiltonian learning, we selectively excite magnons at tunable energy densities. Measuring first the linear magnon response -- a central probe in neutron-scattering experiments -- we extract temperature-dependent spectra and lifetimes. Our results reveal stark variations in magnon decay rates across the Brillouin zone, with enhancement near van Hove singularities and suppression for edge-localized modes. Next, we perform a suite of nonlinear measurements, including the study of self-scattering mechanisms, as well as pump-probe spectroscopy to directly characterize the magnon interactions. While matrix-product state simulations capture the dynamics well in either small systems or at low temperatures, their predictions become inaccurate away from these limits. This work demonstrates precise simulation of the interacting dynamics in quantum magnets, and provides key insights into quasi-particles and their microscopic scattering mechanisms.
Realistic simulation of quantum materials is a central goal of quantum computation. Although quantum processors have advanced rapidly in scale and fidelity, it has remained unclear whether pre-fault-tolerant devices can perform quantitatively reliable material simulations. We demonstrate that a superconducting quantum processor operating on up to 50 qubits can already produce meaningful, quantitative comparisons with inelastic neutron-scattering measurements of KCuF3, a canonical realization of a gapless Luttinger liquid system with a strongly correlated ground state and a spectrum of emergent spinons. The quantum simulation is enabled by a quantum-classical workflow for computing dynamical structure factors (DSFs). The resulting spectra are benchmarked against experimental measurements using multiple metrics, highlighting the impact of circuit depth and circuit fidelity on simulation accuracy. Finally, we extend our simulations to a 1D XXZ Heisenberg model with next-nearest-neighbor (NNN) interactions and a strong anisotropy, producing a gapped excitation spectrum, which could be used to describe the CsCoX3 compounds above the Néel temperature. Our results establish a framework for computing DSFs for quantum materials in classically challenging regimes of strong entanglement and long-range interactions, enabling quantum simulations that are directly testable against laboratory measurements.
Quantum computers are an ideal platform to study the ground state properties of strongly correlated systems due to the limitation of classical computing techniques particularly for systems exhibiting quantum phase transitions. While the error rates of Noisy Intermediate-Scale Quantum (NISQ) computers are still high, simulating strongly correlated systems on such devices and extracting information of possible phases may be within reach. The frustrated transverse-field Ising model (TFIM) is such a system with multiple ordered magnetic phases. In this study, we simulate a two-dimensional frustrated TFIM with next-nearest-neighbor spin-exchange interactions at zero temperature. The competition between the nearest-neighbor ferromagnetic and next-nearest-neighbor antiferromagnetic coupling gives rise to frustration in the system. Moreover, the presence of quantum fluctuations makes the ground-state phase profile even richer. We use the Variational Quantum Eigensolver (VQE) to compute the phases on a square lattice with periodic boundary conditions for a system of 16 sites (qubits). The trained VQE circuits are compared to exact diagonalization, allowing us to extract error measures of VQE. We focus on the ground-state phase transitions of this model, where VQE succeeds in finding the dominant magnetic phases. The optimized VQE circuits are then executed on the Quantinuum H1-1 trapped-ion quantum computer without using any error mitigation techniques. Our experiments show near perfect recovery of the magnetic phases of the frustrated model through ground-state energy, the energy derivative, and the spin correlation functions. Thus, we show that the trapped-ion quantum processor is able to achieve reliable simulations of a strongly correlated system within the limitations of the VQE approach.
The quantum phase transitions provide a paradigm for studying collective quantum phenomena that are a result of competing non-commuting interactions. This paper will study the ground state properties and quantum critical dynamics of the one-dimensional transverse field Ising model through a combined perspective that includes exact diagonalisation, variational quantum eigensolver (VQE) simulations, and simulations on realistic physical quantum devices. We focus on a lattice of four spins, where we calculate the ground-state energies, magnetic order parameters and correlation functions at uniformly applied conditions, which is repeated by all systems. Precise diagonalisation provides both a benchmark, which is symmetry-conserving, and a depth-two, physics inspired variational approximation, which provides simulations accessible to hardware. The circuits that have been optimised identically are then placed on the IQM Garnet quantum processor, using a resource-efficient batched protocol. We find that the ground-state energies of shallow variational circuits are reliably captured by the circuit over the entire parameter space; the magnetic arrangement parameters and observables sensitive to correlation signal significantly more noise. The error analysis of quantitative analysis reveals a strong broadening of critical crossover on hardware, which is consistent with the noise attenuation of long-range correlations. These findings highlight the current capabilities as well as the fundamental limitations of noisy intermediate-scale quantum systems in modelling quantum critical phenomena as a benchmark to future enhancements in obtaining quantum hardware and quantum algorithms development.