Quantum-analogy-based solutions for robust photonics

Joseph Meyer, Yuval Reches, Georgi Gary Rozenman +3 more

While quantum key distribution (QKD) shows much potential as a solution to the looming threat that quantum computers place on today’s encryption protocols, efficient and robust systems that operate in free space are still limited. In this paper, we present a classical light analogy of QKD using spatial modes of light, which can provide a higher bit per photon rate than the more commonly used polarization state encoding.We start by demonstrating the BB84 protocol indoors, where we achieve a secure key rate of 1.55 bits per sifted photon. Then, using our outdoor environment, we propagate the signal outdoors over a distance of up to 90 m, modeling the real-world challenges of QKD in free-space applications and successfully demonstrating BB84 for dimensions 2 and 4.

Germano Montemezzani

Thomas P. Pearsall

A Ortiz, Raúl López-March, Romero, Pablo Martínez Carrasco +2 more

The limitations of digital electronics in handling real-time matrix operations for emerging computational tasks - such as artificial intelligence, drug design, and medical imaging - have prompted renewed interest in analog computing. Programmable Integrated Photonics (PIP) has emerged as a promising technology for scalable, low-power, and high-bandwidth analog computation. While prior work has explored PIP implementations of quantum and neuromorphic computing, both approaches face significant limitations due to misalignments between their mathematical models and the native capabilities of photonic hardware. Building on the recently proposed Analog Programmable-Photonic Computation (APC) - a computation theory explicitly matched to the technological features of PIP - we introduce its critical missing component: an information theory. We present Analog Programmable-Photonic Information (API), a mathematical framework that addresses fundamental concepts beyond APC by examining the amount of information that can be generated, computed and recovered in a PIP platform. API also demonstrates the robustness of APC against errors arising from system noise and hardware imperfections, enabling scalable computation without the extensive error-correction overhead required in quantum computing. Together, APC and API provide a unified foundation for on-chip photonic computing, offering a complementary alternative to digital, quantum and neuromorphic paradigms, and positioning PIP as a cornerstone technology for next-generation information processing.

Andrés Macho, Raúl López-March, Pablo Martínez-Carrasco Romero +2 more

The limitations of digital electronics in handling real-time matrix operations for emerging computational tasks—such as artificial intelligence, drug design, and medical imaging—have prompted renewed interest in analog computing. Programmable integrated photonics (PIP) has emerged as a promising technology for scalable, low-power, and high-bandwidth analog computation. Although prior work has explored PIP implementations of quantum and neuromorphic computing, both approaches face significant limitations due to misalignments between their mathematical models and the native capabilities of photonic hardware. Building on the recently proposed analog programmable-photonic computation (APC)—a computation theory explicitly matched to the technological features of PIP—we introduce its critical missing component: an information theory. We present analog programmable-photonic information (API), a mathematical framework that addresses fundamental concepts beyond APC by examining the amount of information that can be generated, computed, and recovered in a PIP platform. API also demonstrates the robustness of APC against errors arising from system noise and hardware imperfections, enabling scalable computation without the extensive error correction overhead required in quantum computing. Together, APC and API provide a unified foundation for on-chip photonic computing, offering a complementary alternative to digital, quantum, and neuromorphic paradigms, and positioning PIP as a cornerstone technology for next-generation information processing.

Thomas P. Pearsall