Researchers are tackling the challenge of designing advanced quantum technologies using quantum computers themselves. Juan Naranjo, Thi Ha Kyaw, and Gaurav Saxena, all from LG Electronics Toronto AI Lab, alongside colleagues including Kevin Ferreira and Jack S. Baker, present a novel computer-aided framework for optimising solid-state spin systems, the foundation of devices ranging from sensors to quantum processors. Their work is significant because it demonstrates a pathway to accelerate the design and comparison of these technologies under realistic experimental conditions, achieving substantial reductions in the computational cost of simulating complex spin dynamics and paving the way for more efficient quantum hardware development.
Baker, present a novel computer-aided framework for optimising solid-state spin systems, the foundation of devices ranging from sensors to quantum processors. This research extends the paradigm by incorporating both electronic and nuclear spins alongside spin, phonon interactions, describing a collection of interacting spin defects within a solid with vibrational degrees of freedom. As illustrated in their framework, the process begins by defining a quantum system, specifying spin-defect species, host material, nuclear spin species, applied magnetic fields, and the geometry of the spin ensemble. The system Hamiltonian is then constructed, with parameters obtained computationally or experimentally, followed by execution of the sQKFF algorithm. The outputs enable estimation of quantum resource requirements and computation of key system properties, such as autocorrelation functions, microwave absorption spectra, and time-dependent coherence. This mapping was then integrated with qubit-wise commuting aggregation, a process that reorganizes quantum operations to allow for parallel execution, significantly reducing circuit depth. The team engineered a system where the Hamiltonian parameters were either computationally derived or obtained from experimental data, allowing for realistic modeling of spin-defect ensembles within a solid material. The sQKFF algorithm proved particularly effective in balancing accuracy with hardware constraints, allowing for more detailed modelling of spin dynamics. Specifically, the study revealed that careful selection of reference states is critical for maintaining precision in the simulations. Three distinct NV-center configurations were simulated, each with varying parameters, to assess the framework’s adaptability and robustness. Parameters such as the zero-field splitting parameter, measured at 2.87GHz, and the axial hyperfine coupling constant, at -2.16MHz, were precisely incorporated into the Hamiltonian models.
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