Towards a dissipative quantum classifier
- URL: http://arxiv.org/abs/2310.10254v1
- Date: Mon, 16 Oct 2023 10:26:24 GMT
- Title: Towards a dissipative quantum classifier
- Authors: He Wang, Chuanbo Liu, and Jin Wang
- Abstract summary: We propose a novel quantum classifier utilizing dissipative engineering.
By subjecting the auxiliary qubits to carefully tailored strong dissipations, we establish a one-to-one mapping between classical data and dissipative modes.
We train the dissipative central spin-qubit system to perform specific classification tasks akin to classical neural networks.
- Score: 10.528587399925938
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a novel quantum classifier utilizing dissipative
engineering. Unlike standard quantum circuit models, the classifier consists of
a central spin-qubit model. By subjecting the auxiliary qubits to carefully
tailored strong dissipations, we establish a one-to-one mapping between
classical data and dissipative modes. This mapping enables the encoding of
classical data within a decoherence-free subspace, where the central qubit
undergoes evolution. The dynamics of the central qubit are governed by an
effective Lindblad master equation, resulting in relaxation towards a steady
state. We first demonstrate the capability of our model to prepare arbitrary
single-qubit states by training the inter-coupling of the system and the
external dissipations. By elucidating the underlying classification rule, we
subsequently derive a quantum classifier. Leveraging a training set with
labeled data, we train the dissipative central spin-qubit system to perform
specific classification tasks akin to classical neural networks. Our study
illuminates the untapped potential of the dissipative system for efficient and
effective classification tasks in the realm of quantum machine learning.
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