Simulating near-infrared spectroscopy on a quantum computer for enhanced chemical detection

• URL: http://arxiv.org/abs/2504.10602v1
• Date: Mon, 14 Apr 2025 18:02:48 GMT
• Title: Simulating near-infrared spectroscopy on a quantum computer for enhanced chemical detection
• Authors: Ignacio Loaiza, Stepan Fomichev, Danial Motlagh, Pablo A. M. Casares, Daniel Honciuc Menendez, Serene Shum, Alain Delgado, Juan Miguel Arrazola,
• Abstract summary: Near-infrared (NIR) spectroscopy is a non-invasive, low-cost, reagent-less, and rapid technique to measure chemical concentrations.<n>We propose a method for simulating NIR spectra on a quantum computer, as part of a larger workflow to improve NIR-based chemical detection.
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• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Near-infrared (NIR) spectroscopy is a non-invasive, low-cost, reagent-less, and rapid technique to measure chemical concentrations in a wide variety of sample types. However, extracting concentration information from the NIR spectrum requires training a statistical model on a large collection of measurements, which can be impractical, expensive, or dangerous. In this work, we propose a method for simulating NIR spectra on a quantum computer, as part of a larger workflow to improve NIR-based chemical detection. The quantum algorithm is highly optimized, exhibiting a cost reduction of many orders of magnitude relative to prior approaches. The main optimizations include the localization of vibrational modes, an efficient real-space-based representation of the Hamiltonian with a quantum arithmetic-based implementation of the time-evolution, optimal Trotter step size determination, and specific targeting of the NIR region. Overall, our algorithm achieves a O(M^2) scaling, compared with the O(M^12) coming from equivalent high-accuracy classical methods. As a concrete application, we show that simulating the spectrum of azidoacetylene (HC2N3), a highly explosive molecule with strong anharmonicities consisting of M = 12 vibrational modes, requires circuits with a maximum 8.47 x 10^8 T gates and 173 logical qubits. By enhancing the training datasets of detection models, the full potential of vibrational spectroscopy for chemical detection could be unlocked across a range of applications, including pharmaceuticals, agriculture, environmental monitoring, and medical sensing.

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