Solving computationally hard problems with 3D integrated photonics

As technology advances, the limitations of conventional electronic computers are becoming increasingly apparent, especially when tackling complex computational challenges. NP-complete problems, which grow exponentially with size, represent some of the toughest puzzles in computer science. These issues have significant implications across various fields, including biomedicine, transportation, and manufacturing. In the quest for more effective solutions, researchers are exploring alternatives to traditional computing methods, with optical computing emerging as a promising avenue.

As reported in Advanced Photonics, a team from Shanghai Jiao Tong University has made strides in this area by developing a reconfigurable three-dimensional integrated photonic processor specifically designed to tackle the subset sum problem (SSP), a classic NP-complete problem. Using an innovative technique called femtosecond laser direct writing, the researchers constructed a photonic chip composed of 1,449 standardized optical components. This technology allows for rapid prototyping and offers greater design flexibility, crucial for addressing the complexities of the SSP. 

The subset sum problem involves determining whether a specific subset of numbers can sum to a given target. By mapping this problem onto their photonic processor, the researchers can encode the behavior of light to perform calculations. The processor operates by allowing photons in a light beam to explore all possible paths simultaneously, providing answers in parallel. This design not only speeds up the computation but also maintains high accuracy—demonstrated by the processor’s ability to solve different instances of the SSP with 100 percent reliability.

Computing results of the SSP instances where the set is {2, 3, 5, 7, 11, 13, 17} and {2, 5, 7, 11, 13, 17}, respectively. (a) and (c) The experimental read-out displays as a line of spots, which certify the existence of the corresponding subset sums (i.e., the numbers below the spots). (b) and (d) The experimental and theoretical intensity distribution. In the theoretical cases, nonzero intensity certifies the existence of a subset sum. By applying a reasonable intensity threshold, the experimental signals can be correctly classified into valid (beyond the threshold) and invalid certifications (below the threshold). The tolerance intervals of the thresholds are marked by the black bands. Credit: Xu et al., doi 10.1117/1.AP.6.5.056011

The potential applications for this technology extend beyond the subset sum problem. The reconfigurable nature of the processor could be adapted for tasks such as optical neural networks and photonic quantum computing, indicating a versatile future for photonic systems. Remarkably, this new processor has shown improved performance compared to existing electronic counterparts, particularly in computing time and efficiency as problem size increases.

This development marks a significant step toward harnessing the capabilities of light for practical computing, providing a pathway to address computationally demanding problems at a larger scale. As researchers continue to explore optical computing, this breakthrough could reshape how we approach complex challenges across various scientific and industrial domains.

For details, see the original Gold Open Access article by X.-Y. Xu et al., “Reconfigurable integrated photonic processor for NP-complete problems,” Adv. Photon. 6(5), 056011 (2024), doi 10.1117/1.AP.6.5.056011