Quantum computing offers transformative potential for simulating real-world materials, providing a powerful platform to investigate complex quantum systems across quantum chemistry and condensed matter physics. In this work, we leverage this capability to simulate the Hubbard model on a six-site graphene hexagon using Qiskit, employing the Iterative Quantum Phase Estimation (IQPE) to determine its groundstate energies (GSEs). Noiseless simulations yield accurate GSEs in excellent agreement with exact diagonalization, underscoring the precision and reliability of quantum simulation for strongly correlated electron systems.
Quantum computing offers transformative potential for simulating real-world materials, providing a powerful platform to investigate complex quantum systems across quantum chemistry and condensed matter physics. In this work, we leverage this capability to simulate the Hubbard model on a six-site graphene hexagon using Qiskit, employing the Iterative Quantum Phase Estimation (IQPE) to determine its groundstate energies (GSEs). Noiseless simulations yield accurate GSEs in excellent agreement with exact diagonalization, underscoring the precision and reliability of quantum simulation for strongly correlated electron systems.
Abstract Quantum computing offers transformative potential for simulating real-world materials, providing a powerful platform to investigate complex quantum systems across quantum chemistry and condensed matter physics. In this work, we leverage this capability to simulate the Hubbard model on a six-site graphene hexagon using Qiskit, employing the Iterative Quantum Phase Estimation (IQPE) and adiabatic evolution algorithms to determine its ground-state properties. Our results show that a single Slater determinant is sufficient to initialize IQPE and accurately recover ground-state energies (GSEs) in small-scale Hubbard systems. In noiseless simulations, IQPE converges within a few iterations to exact GSEs, while adiabatic simulations yield charge and spin densities and correlation functions in excellent agreement with exact diagonalization. However, deploying IQPE and adiabatic evolution on today's noisy quantum hardware remains highly challenging. To investigate these limitations in IQPE, we use the Qiskit Aer simulator with a custom noise model tailored to the characteristics of IBM's real hardware. This model includes realistic depolarizing gate errors, thermal relaxation, and readout noise, allowing us to explore how these factors degrade simulation accuracy. Further, we implement the IQPE algorithm on IBM's ibm_strasbourg and ibm_fez devices for a reduced three-site Hubbard model, enabling direct comparison between simulated and real hardware noise. While ibm_fez runs closely match exact results, discrepancies highlight the gap between modeled and physical noise. This study demonstrates both the IQPE's potential and current limitations for simulating strongly correlated systems under realistic conditions.
Graphene, a material of extraordinary interest in condensed matter physics due to its unique electronic properties, presents a fundamental challenge: the precise calculation of its band structure. This Master’s Thesis addresses this problem by proposing the use of one of the most widely used algorithms in quantum computing. To achieve this, a tight-binding model is used that describes the electron hopping between the atoms of the single-layer hexagonal lattice that defines graphene. This model is extended to include not only the interactions of each atom in the lattice with adjacent atoms (first neighbors), but also with the next closest atoms (second neighbors), allowing for the capture of more subtle physical effects. The resulting hamiltonian is then prepared for execution in the quantum simulators of IBM’s Qiskit platform. The results under ideal conditions demonstrate that the algorithm reproduces analytical solutions with high fidelity, capturing essential features such as Dirac points, where valence and conduction bands intersect, causing electrons to behave as massless particles, and electron-hole symmetry breaking, which reflects the difference in the energetic behavior of electrons and their absences (holes). However, when introducing a noise model that emulates current quantum processors (inherently noisy, intermediate-scale devices, the socalled NISQ era), a systematic deviation is observed that underscores the limiting impact of the hardware. The study concludes that VQE is a conceptually viable and powerful tool for materials simulation, but its practical application to achieve quantum advantage will critically depend on the development of advanced error mitigation techniques.
The Fermi–Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum many-body systems. In this work, we demonstrate the quantum simulation of the one-dimensional Fermi–Hubbard model using IBM's superconducting quantum computers, employing over 100 qubits. We introduce a first-order Trotterization scheme and extend it to an optimized second-order Trotterization for the time evolution in the Fermi–Hubbard model, specifically tailored for the limited qubit connectivity of quantum architectures, such as IBM's platforms. Notably, both Trotterization approaches are scalable and maintain a constant circuit depth at each Trotter step, regardless of the qubit count, enabling us to precisely investigate the relaxation dynamics in the Fermi–Hubbard model by measuring the expectation value of the Néel observable (staggered magnetization) for time-evolved quantum states. Finally, our successful measurement of expectation values in such large-scale quantum many-body systems, especially at longer time scales with larger entanglement, highlights the quantum utility of superconducting quantum platforms over conventional classical approximation methods.
Quantum simulation and near-term quantum algorithms have gained significant momentum over the past few years, with researchers exploring quantum-based approaches to solve classically intractable problems. Out of many research problems, certain problems are well suited for quantum algorithms. Fermi Hubbard model based on lattice structures is one of the quantum-mechanical models used in many-body and condensed matter physics to capture the essence of electron-correlated systems. In this work, we show one of the first kinds of experiments on simulating the ground state properties of 1D and 2D Fermi Hubbard-based lattice models compared with the reference ground state energy values using Qiskit Nature. We utilize the Variational Quantum Eigensolver (VQE) and different gradient-based optimizers to achieve results that closely converge towards the exact values, showcasing the potential of variational quantum algorithms.
Exploratory Benchmark — Applied Quantum Computing Track This record presents an exploratory benchmark in the applied quantum computing track of the Radial Coherential Dynamics (RCD) research program. Building upon the previously published decoherence suppression model (Topological Decoherence Suppression via Coherential Geometry, Zenodo, December 2025, DOI: 10.5281/zenodo.18000641) and its first algorithmic application (Coherence-Regulated Variational Quantum Eigensolver: An Exploratory H₄ Benchmark, Zenodo, December 2025, DOI: 10.5281/zenodo.18049302), this work explores the impact of a fixed decoherence regulation law on variational quantum algorithms applied to a graphene-inspired system. An effective six-site graphene hexagon is modeled using a Fermi–Hubbard Hamiltonian with periodic boundary conditions. Variational Quantum Eigensolver (VQE) simulations are performed for both the neutral (N = 6) and hole-doped (N = 5) cases over a range of interaction strengths U/t. A controlled A/B methodology is employed: identical VQE simulations are executed using standard noise parameters (control) and RCD-regulated noise parameters (treatment), where the dephasing time is scaled according to the previously published law T2→T2/αT_2 \rightarrow T_2/\sqrt{\alpha}T2→T2/α with fixed α≈1.3×10−4\alpha \approx 1.3 \times 10^{-4}α≈1.3×10−4. No additional parameters are introduced. This work does not claim ab initio graphene results. The “exact” reference corresponds to the ground state of the effective Hubbard model used here. The benchmark is intended solely to assess whether a previously established decoherence suppression law produces consistent effects when transferred, without tuning, to multi-qubit variational algorithms. All code, data, and figures are provided for independent evaluation by the scientific community.
Quantum computers are the ideal platform for quantum simulations. Given enough coherent operations and qubits, such machines can be leveraged to simulate strongly correlated materials, where intricate quantum effects give rise to counterintuitive macroscopic phenomena such as high-temperature superconductivity. In this paper, we provide a gate decomposition and an architecture for a quantum simulator used to simulate the Fermi-Hubbard model in a hybrid variational quantum-classical algorithm. We propose a simple planar implementation-independent layout of qubits that can also be used to simulate more general fermionic systems. By working through a concrete application, we show the gate decomposition used to simulate the Hamiltonian of a cluster of the Fermi-Hubbard model. We briefly analyze the Trotter-Suzuki errors and estimate the scaling properties of the algorithm for more complex applications.