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Codesigned counterdiabatic quantum optimization on a photonic quantum processor

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arxiv 2409.17930 v1 pith:BNXRLI2P submitted 2024-09-26 quant-ph physics.optics

classification quant-phphysics.optics
keywords quantumcounterdiabaticoptimizationalgorithmcodesignedimplementphotonicprocessor
verification ladder T0 review T1 audit T2 compute T3 formal
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Codesign, an integral part of computer architecture referring to the information interaction in hardware-software stack, is able to boost the algorithm mapping and execution in the computer hardware. This well applies to the noisy intermediate-scale quantum era, where quantum algorithms and quantum processors both need to be shaped to allow for advantages in experimental implementations. The state-of-the-art quantum adiabatic optimization algorithm faces challenges for scaling up, where the deteriorating optimization performance is not necessarily alleviated by increasing the circuit depth given the noise in the hardware. The counterdiabatic term can be introduced to accelerate the convergence, but decomposing the unitary operator corresponding to the counterdiabatic terms into one and two-qubit gates may add additional burden to the digital circuit depth. In this work, we focus on the counterdiabatic protocol with a codesigned approach to implement this algorithm on a photonic quantum processor. The tunable Mach-Zehnder interferometer mesh provides rich programmable parameters for local and global manipulation, making it able to perform arbitrary unitary evolutions. Accordingly, we directly implement the unitary operation associated to the counterdiabatic quantum optimization on our processor without prior digitization. Furthermore, we develop and implement an optimized counterdiabatic method by tackling the higher-order many-body interaction terms. Moreover, we benchmark the performance in the case of factorization, by comparing the final success probability and the convergence speed. In conclusion, we experimentally demonstrate the advantages of a codesigned mapping of counterdiabatic quantum dynamics for quantum computing on photonic platforms.

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  1. Photonic-Implemented Efficient Deep Quantum Neural Network via Virtual-Driven Hilbert Space Expansion

    quant-ph 2026-05 unverdicted novelty 7.0 of 10

    A deep photonic QNN achieves nonlinear operations via virtual Hilbert space expansion on a linear chip with four entanglement sources, demonstrated on classification, generation, and state preparation tasks.

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