Three-dimensional photonic chips are superior in handling $N\phantom{\rule{0}{0ex}}P$-complete problems, but their large-scale application is hindered by the difficulty of programming them. The authors experimentally demonstrate a programmable three-dimensional photonic processor capable of solving the maximum clique problem by encoding injection conditions of photons. The prototype shows the potential of photonic computing, and is applied to solve molecular docking problems. The combination of non-von Neumann parallel computing architectures with fully programmable capabilities paves the way for wider practical applications and photonic computational advantages.
Three-dimensional photonic chips are superior in handling N\phantom{\rule{0}{0ex}}P-complete problems, but their large-scale application is hindered by the difficulty of programming them. The authors experimentally demonstrate a programmable three-dimensional photonic processor capable of solving the maximum clique problem by encoding injection conditions of photons. The prototype shows the potential of photonic computing, and is applied to solve molecular docking problems. The combination of non-von Neumann parallel computing architectures with fully programmable capabilities paves the way for wider practical applications and photonic computational advantages.
The general-purpose programmable photonic processors offer a scalable and reconfigurable solution for a wide range of RF and optical applications. Therefore, implementing photonic Ising machines using programmable processors leverages the advantages of high speed and parallelism, enabling efficient hardware acceleration for finding ground-state solutions to combinatorial optimization problems. In this work, we demonstrate a novel programmable photonic Ising solver based on a hexagonal mesh general-purpose programmable photonic platform. The integrated system allows reconfigurable matrix multiplication and computes the Hamiltonian iteratively using an annealing algorithm that facilitates spin updates and effectively searches for the ground state. As a proof of concept, we experimentally solve two benchmark optimization problems, a fundamental three-node ferromagnetic coupling problem with external bias that demonstrates nontrivial spin interactions, and a four-node Max-Cut problem with arbitrary coupling matrices. Furthermore, to establish a large-scale capability, we emulated Ising problems with sizes up to N = 50, achieving success probabilities exceeding 80\%. Additionally, we examined the impact of errors, such as phase and coupling, on the performance of the programmable photonic Ising machine. Our general-purpose photonic Ising machine paves the way for implementing large-scale, programmable architectures for solving optimization problems.
NP-complete problems are widely and deeply involved in various real-life scenarios while still intractable to solve efficiently on conventional computers. It is of great practical significance to construct versatile computing architectures that solve NP-complete problems with computational advantage. Here, we present a reconfigurable photonic processor to efficiently solve a benchmark NP-complete problem, the subset sum problem (SSP). We show that in the case of successive primes, the photonic processor has genuinely surpassed commercial electronic processors launched recently by taking advantages of the high propagation speed and vast parallelism of photons and state-of-the-art integrated photonic technology. Moreover, we are able to program the photonic processor to tackle different problem instances relying on the tunable integrated modules, variable split junctions, which can be used to build a fully reconfigurable architecture potentially allowing 2N configurations at most. Our experiments confirm the potential of the photonic processor as a versatile and efficient computing platform, suggesting a possible practical route to solving computationally hard problems at large scale.
A variety of complicated computational scenarios have made unprecedented demands on the computing power and energy efficiency of electronic computing systems, including solving intractable nondeterministic polynomial-time (NP)-complete problems and dealing with large-scale artificial intelligence models. Optical computing emerges as a promising paradigm to meet these challenges, whereas current optical computing architectures have limited versatility. Their applications are usually either constrained to a specialized domain or restricted to general-purpose matrix computation. Here, we implement a fully-programmable integrated photonic processor that can be configured to tackle both specific computational problems and general-purpose matrix computation. We achieve complete end-to-end control of the photonic processor by utilizing a self-developed integrated programmable optoelectronic computing platform. For domain-specific computing, our photonic processor can efficiently solve two kinds of NP-complete problems: subset sum problem (far more than 2^N different instances) and exact cover problem. For general-purpose computation, we experimentally demonstrate high-precision optical dot product and further realize accurate image edge detection and MNIST handwritten image classification task with an accuracy of 97%. Our work enhances the versatility and capability of optical computing architecture, paving the way for its practical application in future high-performance and complex computing scenarios.
Nondeterministic-polynomial-time (NP)-complete problems are widely involved in various real-life scenarios but are still intractable in being solved efficiently on conventional computers. It is of great practical significance to construct versatile computing architectures that solve NP-complete problems with computational advantage. Here, we present a reconfigurable integrated photonic processor to efficiently solve a benchmark NP-complete problem, the subset sum problem. We show that in the case of successive primes, the photonic processor has genuinely surpassed electronic processors launched recently by taking advantage of the high propagation speed and vast parallelism of photons and state-of-the-art integrated photonic technology. Moreover, we are able to program the photonic processor to tackle different problem instances, relying on the tunable integrated modules, variable split junctions, which can be used to build a fully reconfigurable architecture potentially allowing 2N configurations at most. Our experiments confirm the potential of the photonic processor as a versatile and efficient computing platform, suggesting a possible practical route to solving computationally hard problems at a large scale.
Controlling and programming quantum devices to process quantum information by the unit of qudit provides the possibilities for noise-resilient quantum communications, delicate quantum molecular simulations, and efficient quantum computations, showing great potential to enhance the capabilities of qubit-based quantum technologies. Here, we report a programmable high-dimensional quantum processor in a silicon-photonic in-tegrated circuit, which monolithically integrates all the key functionalities and capabilities of initialisation, manipulation, and measurement of the two quantum quart (ququart) states, and demonstrate its enhancement of quantum computational paral-lelism. By reprogramming the processor, we implemented the basic quantum Fourier transform algorithms, all in quaternary, to benchmark the enhancement of quantum parallelism using qudits. The monolithic integration and high programmability have allowed the implementations of more than one million high-fidelity preparations, operations and projections of qudit states in the processor. Our work shows an integrated photonic quantum technology for qudit-based quantum computing with enhanced capacity, accuracy, and efficiency, which could lead to the acceleration of building a large-scale Quantum computer.
We present the design and benchmarking of RP000, a quantum photonic processor capable of encoding a quantum system in the degrees of freedom of single photons, based on standard CMOS-compatible manufacturing processes, and working at room temperature. We benchmark it against machine learning tasks, evaluating three quantum-classical architectures of increasing complexity. Our experimental results and simulations show that RP000 achieves higher accuracy than classical networks of comparable size in multiple use cases. Compared to a superconducting quantum processor, RP000 exhibits superior noise tolerance. These findings demonstrate that RP000 can provide a scalable route toward efficient quantum applications.