Scalable quantum dynamics compilation via quantum machine learning
Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared to deterministic methods such as Trotterization, variational quantum compilation (VQC) methods employ variational optimization to reduce gate costs while maintaining high accuracy. In this work, we explore the potential of a VQC scheme by making use of out-of-distribution generalization results in quantum machine learning (QML): By learning the action of a given many-body dynamics on a small data set of product states, we can obtain a unitary circuit that generalizes to highly entangled states such as the Haar random states. The efficiency in training allows us to use tensor network methods to compress such time-evolved product states by exploiting their low entanglement features. Our approach exceeds state-of-the-art compilation results in both system size and accuracy in one dimension ($1$D). For the first time, we extend VQC to systems on two-dimensional (2D) strips with a quasi-1D treatment, demonstrating a significant resource advantage over standard Trotterization methods, highlighting the method's promise for advancing quantum simulation tasks on near-term quantum processors.
Code (0)
등록된 구현이 없습니다.
Tasks
Out-of-Distribution GeneralizationQuantum Machine LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Out-of-distribution generalization for learning quantum dynamics
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (…
Generalization BoundsOut-of-Distribution GeneralizationQuantum Machine LearningAttention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures
Modular, distributed and multi-core architectures are currently considered a promising approach for scalability of quantum computing systems. The integration of multiple Quantum Processing Units necessitates classical an…
Deep Reinforcement LearningQuantum circuit synthesis with diffusion models
Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. In this work, we use generative ma…
DenoisingQuantum Circuit GenerationCompiler Optimization for Quantum Computing Using Reinforcement Learning
Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with man…
Compiler Optimizationreinforcement-learningReinforcement LearningReinforcement Learning (RL)Compiling quantum circuits to realistic hardware architectures using temporal planners
To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoheren…