Physicists at Louisiana State University have made a breakthrough in quantum information processing by using classical light—common light sources like lasers or LEDs—instead of the more fragile quantum states typically required for such tasks. Their findings, published in the journal Advanced Science, introduce a new platform that harnesses the complex behavior of multiple photons from classical light. This approach is easier to implement since classical light is more readily available and stable than the delicate quantum states usually needed for these kinds of experiments. The system also operates at room temperature and can handle noise and signal loss, making it more practical for real-world applications. The platform relies on specialized detectors called photon-number-resolving detectors, which can count how many photons arrive at a given time. Using this technology, the researchers were able to access different quantum-like behaviors from the same classical light source. By combining three properties of light—its polarization (the direction of its wave), its spatial structure (how it spreads out), and the number of photons present—they created a vast array of possible states and connections. This formed what they call a "multiphoton quantum reservoir," which provided 861 distinct components for processing information. Reservoir computing is a computational technique that uses the natural complexity of a physical system to perform complex tasks. In this case, the light itself carried out the complex transformations, while the researchers used measurements of the number of photons to read out the information encoded in the resulting patterns. Instead of trying to control every interaction in the system, they focused on training a simple readout mechanism to interpret the final output. This method simplifies the process of using complex systems for computation. The researchers tested their platform in two ways: first as a quantum simulator, where it successfully recreated behaviors like quantum random walks and synthetic lattices that show thermalization and anti-thermalization. Then, they used it to learn mathematical functions by encoding inputs in the polarization of light. The system transformed each input into a more complex pattern using spatial modes and photon numbers. Remarkably, it learned six very different mathematical functions without needing to physically reconfigure the setup. The results showed that using the full distribution of photon numbers—especially for highly nonlinear tasks—led to more accurate predictions, suggesting that higher-order photon interactions are valuable for computation. This new approach blends classical and quantum optics, using bright classical light as a photon source and advanced measurements to access specific quantum-like features. The fact that the system works at room temperature and performs well even in noisy environments suggests a practical path toward quantum technologies that can function in typical laboratory conditions. The research was conducted by a team from Louisiana State University, along with collaborators from the Universidad Nacional Autónoma de México and the Universidad Politécnica de Pachuca.