Simulating near-infrared spectroscopy on a quantum computer for enhanced chemical detection
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909579924733952 |
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| author | Loaiza, Ignacio Fomichev, Stepan Motlagh, Danial Casares, Pablo A. M. Menendez, Daniel Honciuc Shum, Serene Delgado, Alain Arrazola, Juan Miguel |
| author_facet | Loaiza, Ignacio Fomichev, Stepan Motlagh, Danial Casares, Pablo A. M. Menendez, Daniel Honciuc Shum, Serene Delgado, Alain Arrazola, Juan Miguel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10602 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulating near-infrared spectroscopy on a quantum computer for enhanced chemical detection Loaiza, Ignacio Fomichev, Stepan Motlagh, Danial Casares, Pablo A. M. Menendez, Daniel Honciuc Shum, Serene Delgado, Alain Arrazola, Juan Miguel Quantum Physics 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. |
| title | Simulating near-infrared spectroscopy on a quantum computer for enhanced chemical detection |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.10602 |