Limitations of Quantum Hardware for Molecular Energy Estimation Using VQE
arXiv:2506.03995 · doi:10.1039/D5CP03907J
Abstract
Variational quantum eigensolvers (VQEs) are among the most promising quantum algorithms for solving electronic structure problems in quantum chemistry, particularly during the Noisy Intermediate-Scale Quantum (NISQ) era. In this study, we investigate the capabilities and limitations of VQE algorithms implemented on current quantum hardware for determining molecular ground-state energies, focusing on the adaptive derivative-assembled pseudo-Trotter ansatz VQE (ADAPT-VQE). To address the significant computational challenges posed by molecular Hamiltonians, we explore various strategies to simplify the Hamiltonian, optimize the ansatz, and improve classical parameter optimization through modifications of the COBYLA optimizer. These enhancements are integrated into a tailored quantum computing implementation designed to minimize the circuit depth and computational cost. Using benzene as a benchmark system, we demonstrate the application of these optimizations on an IBM quantum computer. Despite these improvements, our results highlight the limitations imposed by current quantum hardware, particularly the impact of quantum noise on state preparation and energy measurement. The noise levels in today's devices prevent meaningful evaluations of molecular Hamiltonians with sufficient accuracy to produce reliable quantum chemical insights. Finally, we extrapolate the requirements for future quantum hardware to enable practical and scalable quantum chemistry calculations using VQE algorithms. This work provides a roadmap for advancing quantum algorithms and hardware toward achieving quantum advantage in molecular modeling.
References in corpus (21)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Array Programming with NumPy
- Quantum Computing in the NISQ era and beyond
- A variational eigenvalue solver on a quantum processor
- The theory of variational hybrid quantum-classical algorithms
- Noisy intermediate-scale quantum (NISQ) algorithms
- Quantum Chemistry in the Age of Quantum Computing
- Simulated Quantum Computation of Molecular Energies
- The Variational Quantum Eigensolver: a review of methods and best practices
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor
- Qubit-excitation-based adaptive variational quantum eigensolver
- Quantum Computation of Finite-Temperature Static and Dynamical Properties of Spin Systems Using Quantum Imaginary Time Evolution
- Efficient quantum circuits for quantum computational chemistry
- ADAPT-VQE is insensitive to rough parameter landscapes and barren plateaus
- Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
- Mutual information-assisted Adaptive Variational Quantum Eigensolver
- Benchmarking of Different Optimizers in the Variational Quantum Algorithms for Applications in Quantum Chemistry
- Benchmarking adaptive variational quantum eigensolvers
- Physically motivated improvements of Variational Quantum Eigensolvers
- SHARC-VQE: Simplified Hamiltonian Approach with Refinement and Correction enabled Variational Quantum Eigensolver for Molecular Simulation