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A Dynamically Programmable Quantum Photonic Microprocessor for Graph Computation
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DOI：10.1002/lpor.202300304.png)
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Abstract
En 中文
Quantum computing has grown extensively, especially in system design and development, and the current research focus has gradually evolved from validating quantum advantage to practical applications. In particular, nondeterministic-polynomial-time (NP)-complete problems are central in numerous important application areas. Still, in practice, it is difficult to solved efficiently with conventional computers, limited by the exponential jump in hardness. Here, a quantum photonic microprocessor based on Gaussian boson sampling (GBS) that offers dynamic programmability to solve various graph-related NP-complete problems is demonstrated. The system with optical, electrical, and thermal packaging implements a GBS with 16 modes of single-mode squeezed vacuum states, a universal programmable 16-mode interferometer, and a single photon readout on all outputs with high accuracy, generality, and controllability. The developed system is applied to demonstrate applications in solving NP-complete problems, manifesting the ability of photonic quantum computing to realize practical applications for conventionally intractable computations. The GBS-based quantum photonic microprocessor is applied to solve task assignment, Boolean satisfiability, graph clique, max cut, and vertex cover. These demonstrations suggest an excellent benchmarking platform, paving the way toward large-scale combinatorial optimization. A dynamic programmable quantum photonic microprocessor using Gaussian boson sampling (GBS) is developed. The microprocessor addresses NP-complete graph-related problems through a system that includes optical, electrical, and thermal components. It employs a 16-mode interferometer, single-mode squeezed vacuum states, and accurate single-photon readout on all outputs, providing high accuracy, versatility, and control.image
Keywords:
graph-related NP-complete problems
integrated photonics
optical quantum computing
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L
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3.7K
Citations:
2.1W
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