A linear photonic swap test circuit for quantum kernel estimation
- URL: http://arxiv.org/abs/2402.17923v1
- Date: Tue, 27 Feb 2024 22:34:14 GMT
- Title: A linear photonic swap test circuit for quantum kernel estimation
- Authors: Alessio Baldazzi, Nicol\`o Leone, Matteo Sanna, Stefano Azzini and
Lorenzo Pavesi
- Abstract summary: photonic swap test circuit successfully encodes two qubits and estimates their scalar product with a measured root mean square error smaller than 0.05.
This result paves the way for the development of integrated photonic architectures capable of performing Quantum Machine Learning tasks with robust devices operating at room temperature.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Among supervised learning models, Support Vector Machine stands out as one of
the most robust and efficient models for classifying data clusters. At the core
of this method, a kernel function is employed to calculate the distance between
different elements of the dataset, allowing for their classification. Since
every kernel function can be expressed as a scalar product, we can estimate it
using Quantum Mechanics, where probability amplitudes and scalar products are
fundamental objects. The swap test, indeed, is a quantum algorithm capable of
computing the scalar product of two arbitrary wavefunctions, potentially
enabling a quantum speed-up. Here, we present an integrated photonic circuit
designed to implement the swap test algorithm. Our approach relies solely on
linear optical integrated components and qudits, represented by single photons
from an attenuated laser beam propagating through a set of waveguides. By
utilizing 2$^3$ spatial degrees of freedom for the qudits, we can configure all
the necessary arrangements to set any two-qubits state and perform the swap
test. This simplifies the requirements on the circuitry elements and eliminates
the need for non-linearity, heralding, or post-selection to achieve
multi-qubits gates. Our photonic swap test circuit successfully encodes two
qubits and estimates their scalar product with a measured root mean square
error smaller than 0.05. This result paves the way for the development of
integrated photonic architectures capable of performing Quantum Machine
Learning tasks with robust devices operating at room temperature.
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