Realizing quantum linear regression with auxiliary qumodes

Abstract

In order to exploit quantum advantages, quantum algorithms are indispensable for operating machine learning with quantum computers. We here propose an intriguing hybrid approach of quantum information processing for quantum linear regression, which utilizes both discrete and continuous quantum variables, in contrast to existing wisdoms based solely upon discrete qubits. In our framework, data information is encoded in a qubit system, while information processing is tackled using auxiliary continuous qumodes via qubit-qumode interactions. Moreover, it is also elaborated that finite squeezing is quite helpful for efficiently running the quantum algorithms in realistic setup. Comparing with an all-qubit approach, the present hybrid approach is more efficient and feasible for implementing quantum algorithms, still retaining exponential quantum speed-up.

7 pages, 2 figures. Add implementation with trapped ions

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Cited by in corpus (10)

• Adiabatic Quantum Linear Regression
• Selected topics of quantum computing for nuclear physics
• Continuous-variable assisted thermal quantum simulation
• Protocol for implementing quantum nonparametric learning with trapped ions
• Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions
• Quantum kernels with squeezed-state encoding for machine learning
• Communication-efficient Quantum Algorithm for Distributed Machine Learning
• Inverse iteration quantum eigensolvers assisted with a continuous variable
• Qumode transfer between continuous and discrete variable devices
• Benchmarking of quantum fidelity kernels for Gaussian process regression