Federated learning with distributed fixed design quantum chips and
quantum channels
- URL: http://arxiv.org/abs/2401.13421v2
- Date: Mon, 5 Feb 2024 07:43:20 GMT
- Title: Federated learning with distributed fixed design quantum chips and
quantum channels
- Authors: Ammar Daskin
- Abstract summary: The privacy in classical federated learning can be breached through the use of local gradient results along with engineered queries to the clients.
We propose a quantum federated learning model where fixed design quantum chips are operated based on the quantum states sent by a centralized server.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The privacy in classical federated learning can be breached through the use
of local gradient results along with engineered queries to the clients.
However, quantum communication channels are considered more secure because a
measurement on the channel causes a loss of information, which can be detected
by the sender. Therefore, the quantum version of federated learning can be used
to provide more privacy. Additionally, sending an $N$ dimensional data vector
through a quantum channel requires sending $\log N$ entangled qubits, which can
potentially provide exponential efficiency if the data vector is utilized as
quantum states.
In this paper, we propose a quantum federated learning model where fixed
design quantum chips are operated based on the quantum states sent by a
centralized server. Based on the coming superposition states, the clients
compute and then send their local gradients as quantum states to the server,
where they are aggregated to update parameters. Since the server does not send
model parameters, but instead sends the operator as a quantum state, the
clients are not required to share the model. This allows for the creation of
asynchronous learning models. In addition, the model as a quantum state is fed
into client-side chips directly; therefore, it does not require measurements on
the upcoming quantum state to obtain model parameters in order to compute
gradients. This can provide efficiency over the models where the parameter
vector is sent via classical or quantum channels and local gradients are
obtained through the obtained values of these parameters.
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