Realizing quantum linear regression with auxiliary qumodes
This work addresses the implementation of quantum machine learning algorithms for researchers in quantum computing, though it appears incremental as it builds on existing quantum linear regression methods.
The authors tackled quantum linear regression by proposing a hybrid approach using both discrete qubits and continuous qumodes, which is more efficient and feasible than all-qubit methods while retaining exponential quantum speed-up.
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.