Chandan Sarma and Paul Stevenson at University of Surrey have developed a qubit-efficient variational algorithm, comparing three distinct qubit-mapping strategies within the Variational Quantum Eigensolver (VQE) approach. Their algorithm, focused on the nuclei 10B and 12C, shows that a Slater determinant to qubit mapping achieves the most accurate results on quantum hardware, with a 0.21% error for the ground state of 10B following error mitigation. The algorithm offers a pathway to scaling VQE algorithms for increasingly complex nuclei, potentially advancing our understanding of nuclear physics through quantum computation. Boron-10 and carbon-12 ground state energies calculated with unprecedented quantum precision Error rates in calculating the ground state of the 10B nucleus have fallen to 0.21 per cent, a substantial improvement over previous results of 3.37 and 8.88 per cent achieved with alternative quantum computing strategies. Previously unattainable due to the limitations of classical computational power, this level of precision was attained using a Slater determinant to qubit mapping within the Variational Quantum Eigensolver approach. Simulating the behaviour of even relatively simple atomic nuclei demands immense processing resources, stemming from the many-body problem inherent in quantum chromodynamics and the strong nuclear force. The shell model, a quantum mechanical model describing the structure of the atomic nucleus, provides a framework for these calculations, but its computational cost scales exponentially with the number of nucleons (protons and neutrons). At the University of Surrey, the team extended this qubit-efficient mapping to successfully model the ground state of 12C, showing a 6.82 per cent deviation from the exact result and paving the way for calculations on increasingly complex nuclei. The nucleus of carbon-12 served as a test case, verifying the accuracy of this quantum computing approach and demonstrating its potential beyond boron-10. A Variational Quantum Eigensolver, a hybrid quantum-classical algorithm, was employed by the team, utilising