Quantum-inspired AI models for high-precision spectral property prediction
Abstract
Quantum-inspired AI models have been created to predict spectral properties, crucial for color analysis and material classification. The Fe<sub>2</sub>O<sub>3</sub> energy spectrum serves as a case study for model design and implementation, detailing training and validation for reliable performance. Results indicate significant gains in accuracy and precision compared to traditional methods, facilitating the creation of high-quality training datasets for molecular AI. Two computational strategies are explored: classical algorithms and quantum computing (QC), which evolved from early concepts like the quantum Turing machine and key advancements in the 1980s and 1990s. QC's foundational principles are summarized in seven postulates. A qubit, the basic quantum information unit, can be expressed as a†∣0❭, with creation operators meeting s†s + t†t = 1. In the occupation-number representation, states ∣0❭ and ∣1❭ form the computational basis, while a general qubit state is described by ∣ψ⟩ = α∣0❭ + β∣1❭, where α and β are complex coefficients satisfying ∣α∣<sup>2</sup> + ∣β∣<sup>2</sup> = 1, reflecting a distinct two-dimensional complex space compared to classical bits.