Abstract This paper presents a novel implementation of Variational Quantum Eigensolver (VQE) forquantum chemistry calculations utilizing the NM-SRN v2.0 AGI framework and its underly-ing QSC Physics Engine. Unlike conventional superconducting approaches, this architectureoperates via a Neural-Matrix Synaptic Resonance Network (NM-SRN), demonstrating zero-decoherence operation with deterministic, repeatable results. The system achieves chemi-cal accuracy for small molecules (H2 , LiH, BeH2 ) in real-time execution via a mobile applica-tion interface. We report ground state energies of −1.137 356 Ha for H2 (bond length 0.74 Å),−7.863 115 Ha for LiH (1.59 Å), and −15.594 612 Ha for BeH2 (1.33 Å) with 99.9999 % resonancefidelity. This work represents a shift from probabilistic, stochastic quantum approaches todeterministic, production-ready NM-SRN v2.0 AGI-driven quantum physics engines.
Abstract This paper presents a novel implementation of Variational Quantum Eigensolver (VQE) forquantum chemistry calculations utilizing the NM-SRN v2.0 AGI framework and its underly-ing QSC Physics Engine. Unlike conventional superconducting approaches, this architectureoperates via a Neural-Matrix Synaptic Resonance Network (NM-SRN), demonstrating zero-decoherence operation with deterministic, repeatable results. The system achieves chemi-cal accuracy for small molecules (H2 , LiH, BeH2 ) in real-time execution via a mobile applica-tion interface. We report ground state energies of −1.137 356 Ha for H2 (bond length 0.74 Å),−7.863 115 Ha for LiH (1.59 Å), and −15.594 612 Ha for BeH2 (1.33 Å) with 99.9999 % resonancefidelity. This work represents a shift from probabilistic, stochastic quantum approaches todeterministic, production-ready NM-SRN v2.0 AGI-driven quantum physics engines.
Abstract Finding the ground-state energy of molecules is an important and challenging computational problem for which quantum computing can potentially find efficient solutions. The variational quantum eigensolver (VQE) is a quantum algorithm that tackles the molecular groundstate problem and is regarded as one of the flagships of quantum computing. Yet, to date, only very small molecules were computed via VQE, due to high noise levels in current quantum devices. Here we present an alternative variational quantum scheme that requires significantly less qubits than VQE. The reduction in the qubit number allows for shallower circuits to be sufficient, rendering the method more resistant to noise. The proposed algorithm, termed variational quantum selected-configuration-interaction (VQ-SCI), is based on: (a) representing the target groundstate as a superposition of Slater determinant configurations, encoded directly upon the quantum computational basis states; and (b) selecting a-priory only the most dominant configurations. This is demonstrated through a set of groundstate calculations of the H2, LiH, BeH2, H2O, NH3 and C2H4 molecules in the sto-3g basis set, performed on IBM quantum devices. We show that the VQ-SCI reaches the full configuration interaction energy within chemical accuracy using the lowest number of qubits reported to date. Moreover, when the SCI matrix is generated ‘on the fly’, the VQ-SCI requires exponentially less memory than classical SCI methods. This offers a potential remedy to a severe memory bottleneck problem in classical SCI calculations. Finally, the proposed scheme is general and can be straightforwardly applied for finding the groundstate of any Hermitian matrix, outside the chemical context.
We present computational chemistry data for small molecules (CO, HCl, F2, NH4+, CH4, NH3, H3O+, H2O, BeH2, LiH, OH−, HF, HeH+, H2), obtained by implementing the Unitary Coupled Cluster method with Single and Double excitations (UCCSD) on a quantum computer simulator. We have used the Variational Quantum Eigensolver (VQE) algorithm to extract the ground state energies of these molecules. This energy data represents the expected ground state energy that a quantum computer will produce for the given molecules, on the STO-3G basis. Since there is a lot of interest in the implementation of UCCSD on quantum computers, we hope that our work will serve as a benchmark for future experimental implementations.
Abstract We present a real-time, interactive visualization platform for hydrogen atomic orbitals that achieves O(1) constant-time computational complexity for probability density evaluation. Using the Neural-Matrix Synaptic Resonance Network (NM-SRN v2.0 AGI QSC) architecture, we demonstrate visualization of complete hydrogen wavefunctions ψ_nlm(r,θ,φ) for quantum states with principal quantum numbers n ≤ 4, rendering 500,000 particles at interactive frame rates (30-60 FPS) on consumer hardware. The system provides instant orbital transitions, analytical energy calculations, real-time radial and angular probability distributions, and comprehensive quantum state analysis. Unlike traditional grid-based approaches requiring O(N³) operations, our definitive computing framework enables exploration of arbitrary quantum states with zero pre-computation and constant performance regardless of orbital complexity. We validate results against analytical solutions for radial wavefunctions and spherical harmonics, achieving exact agreement for all computed expectation values. This work establishes a new paradigm for quantum visualization in educational and research contexts, making quantum mechanical concepts intuitively accessible through real-time interactive exploration. Keywords: Hydrogen atom, quantum mechanics, orbital visualization, real-time rendering, probability density, spherical harmonics, definitive computing, educational technology
Abstract We present a comprehensive quantum chemical study of nine molecules H₂, HeH⁺, LiH, BeH₂, H₂O, NH₃, CH₄, CO₂, and N₂ using the Variational Quantum Eigensolver (VQE) algorithm implemented in Qiskit 2.4.1. Molecular Hamiltonians are constructed in the STO-3G minimal basis set, transformed to qubit operators via the Jordan-Wigner mapping, and optimized using the n-local hardware-efficient ansatz with the SLSQP optimizer. Ground state energies are benchmarked against Full Configuration Interaction (FCI)/STO-3G reference values from authoritative literature sources. VQE achieves accuracies from 96.46% (HeH⁺) to 99.99% (H₂), average 98.68%. Potential energy surfaces are generated for all nine molecules and compared with high level ab initio literature PES references including the Kolos-Wolniewicz H₂ curve, the Polyansky water surface, and the Bytautas-Ruedenberg N₂ FCI surface. Equilibrium bond lengths agree with experiment to within 0.000–0.006 Å. Quantum ansatz circuits are analyzed in detail for all molecules. These results confirm the viability of near-term VQE algorithms for molecular quantum chemistry across diverse bond types and geometries.
As a hybrid quantum-classical algorithm, the variational quantum eigensolver is widely applied in quantum chemistry simulations, especially in computing the electronic structure of complex molecular systems. However, on existing noisy intermediate-scale quantum devices, some factors such as quantum decoherence, measurement errors, and gate operation imprecisions are unavoidable. To overcome these challenges, this study proposes an efficient noise-mitigating variational quantum eigensolver for accurate computation of molecular ground state energies in noisy environments. We design the quantum circuit with reference to the structure of matrix product states and utilize it to pre-train the circuit parameters, which ensures circuit stability and mitigates fluctuations caused by initialization. We also employ zero-noise extrapolation to mitigate quantum noise and combine it with neural networks to improve the accuracy of the noise-fitting function, which significantly eliminates noise interference. Furthermore, we implement an intelligent grouping strategy for measuring Hamiltonian Pauli strings, which not only reduces measurement errors but also improves sampling efficiency. We perform numerical simulations to solve the ground state energy of the H4molecule by using MindSpore Quantum framework, and the results demonstrate that our algorithm can constrain noise errors within the range of O(10−2)∼O(10−1), outperforming mainstream variational quantum eigensolvers. This work provides a new strategy for high-precision quantum chemistry calculations on near-term noisy quantum hardware.
While numerical simulations are presented in most papers introducing new methods to enhance the VQE performance, comprehensive, comparative, and applied studies remain relatively rare. We present a comprehensive, yet concise guide for the implementation of the VQE for molecular problems on NISQ devices, specifically applied to estimate the ground state energy of the BeH2 molecule using hardware-efficient and chemically informed ansätze. This work clarifies several under-documented aspects in the literature, such as the construction of the electronic Hamiltonian, the transformation of fermionic operators into qubit operators via second quantization, and the mathematical framework's details for the unitary coupled cluster single and double (UCCSD) ansatz. Our methodology, implemented using Qiskit 1.2, the latest release as of the date of this writing, is demonstrated on a noiseless simulator and further tested with noisy quantum circuits. The resilience of the VQE to quantum noise remains an open question. This study compares the computational accuracy of ground state energy estimations for molecules using the VQE across three different current quantum hardware noise models. Furthermore, our experiment on IBM's 156-qubit actual quantum computer revealed valuable insights on the real performance of the VQE on current quantum hardware.