Probing complex materials with quantum spectroscopy

Matthew Strasberg

Albert Liu, Matthew W. Day, Steven T. Cundiff

The primary consideration in developing new material platforms for quantum applications is to optimize coherence. Despite its importance, decoherence processes remain challenging to experimentally interrogate and quantify. In this Perspective, we first introduce the concept of decoherence in quantum systems and conventional techniques to assess decoherence at optical frequencies. We then introduce multidimensional coherent spectroscopy as a unique probe capable of revealing the full complexity of decoherence dynamics in realistic circumstances. To contextualize the techniques discussed here, demonstrative examples in two prototypical quantum systems, namely colloidal nanocrystals and vacancy centers in diamond, are provided.

Chonghao Zhai, Jinzhao Sun, Jieshan Huang +6 more

Spectroscopy underpins modern scientific discovery across diverse disciplines. While experimental spectroscopy probes material properties through scattering or radiation measurements, computational spectroscopy combines theoretical models with experimental data to predict spectral properties, essential for advancements in physics, chemistry, and materials science. However, quantum systems present unique challenges for computational spectroscopy due to their inherent complexity, and current quantum algorithms remain largely limited to static and closed quantum systems. Here, we present and demonstrate a generalised quantum computational spectroscopy that lifts these limitations by reconstructing the quantum autocorrelation function via an ancilla-assisted Hadamard test quantum circuit. Our method is applicable to a broad range of quantum systems, including closed, open, and time-dependent driven quantum systems. We experimentally validate this approach, which leverages arbitrary controlled quantum dynamics and efficient classical noise-mitigation strategy, on a programmable silicon-photonic quantum processing chip, capable of high-fidelity time-evolution simulations. The versatility of our method is demonstrated through spectroscopic computations for diverse quantum systems, revealing novel phenomena such as parity-time symmetry breaking and topological holonomy that are inaccessible to conventional spectroscopy or quantum eigenstate algorithms. This work establishes a noise-robust methodology for quantum spectral analysis.

Ole Keller

Hakioğlu, Tahsin Tuğrul, A.S. Shumovsky

Chonghao Zhai, Jinzhao Sun, Jieshan Huang +7 more

Spectroscopy underpins modern scientific discovery across diverse disciplines. While experimental spectroscopy probes material properties through scattering or radiation measurements, computational spectroscopy combines theoretical models with experimental data to predict spectral properties, essential for advancements in physics, chemistry, and materials science. However, quantum systems present unique challenges for computational spectroscopy due to their inherent complexity, and current quantum algorithms remain largely limited to static and closed quantum systems. Here, we present and demonstrate a universal quantum computational spectroscopy framework that lifts these limitations. Through leveraging coherently controlled quantum dynamics, our method efficiently reconstructs the spectral information for both closed and open systems, furtherly for time-dependent driven systems. We experimentally validate this approach using a programmable silicon-photonic quantum processing chip, capable of high-fidelity time-evolution simulations. The versatility of our framework is demonstrated through spectroscopic computations for diverse quantum systems -- including spin systems, non-Hermitian systems, and quantum Floquet systems -- revealing novel phenomena such as parity-time symmetry breaking and topological holonomy that are inaccessible to conventional spectroscopy or quantum eigenstate algorithms. {Furthermore, systematic benchmarking of UQCS against existing quantum algorithms is numerically performed to demonstrate its unprecedented capabilities and superior performance. This work establishes a noise-robust and transformative paradigm for quantum spectral analysis.