<p>Quantum computing utilizes the superposition and entanglement properties of quantum mechanics, and is expected to overcome the “curse of dimensionality” faced by classical computing when dealing with the exponential growth of Hilbert space. In the field of hadron physics, the study of hadron structure helps to deepen our understanding of strong interaction, the current study of the energy spectrum structure of exotic hadron states is a hot topic in high-energy physics research. In this work, we implement a hardware-efficient Variational Quantum Eigensolver to investigate the application of Jordan-Wigner transformation and Gray code encoding in simulating the ground state energy eigenvalues of the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\Lambda _cD\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi mathvariant="normal">Λ</mi> <mi>c</mi> </msub> <mi>D</mi> </mrow> </math></EquationSource> </InlineEquation> system. First, we employ the statevector simulator to validate the theoretical accuracy of both encoding schemes and the variational algorithm, then QASM simulations demonstrate that the results exhibit significant sensitivity to statistical fluctuations at low sampling rates, which can be effectively suppressed by increasing the number of shots. Finally, we use a real hardware noise model and find that Gray code encoding shows robust noise resilience under small-scale basis with single measurement error mitigation. As the basis size increases, Jordan-Wigner transformation exhibits better performance under the combined strategy of measurement error mitigation and suppression.</p>