5 research outputs found
Deep Neural Network Equalization for Optical Short Reach Communication
Nonlinear distortion has always been a challenge for optical communication due to the
nonlinear transfer characteristics of the fiber itself. The next frontier for optical communication is a
second type of nonlinearities, which results from optical and electrical components. They become the
dominant nonlinearity for shorter reaches. The highest data rates cannot be achieved without effective
compensation. A classical countermeasure is receiver-side equalization of nonlinear impairments
and memory effects using Volterra series. However, such Volterra equalizers are architecturally
complex and their parametrization can be numerical unstable. This contribution proposes an
alternative nonlinear equalizer architecture based on machine learning. Its performance is evaluated
experimentally on coherent 88 Gbaud dual polarization 16QAM 600 Gb/s back-to-back measurements.
The proposed equalizers outperform Volterra and memory polynomial Volterra equalizers up to 6th
orders at a target bit-error rate (BER) of 10
−2
by 0.5 dB and 0.8 dB in optical signal-to-noise ratio
(OSNR), respectively
A Review of the Applications of Quantum Machine Learning in Optical Communication Systems
In the context of optical signal processing, quantum and quantum-inspired
machine learning algorithms have massive potential for deployment. One of the
applications is in error correction protocols for the received noisy signals.
In some scenarios, non-linear and unknown errors can lead to noise that
bypasses linear error correction protocols that optical receivers generally
implement. In those cases, machine learning techniques are used to recover the
transmitted signal from the received signal through various estimation
procedures. Since quantum machine learning algorithms promise advantage over
classical algorithms, we expect that optical signal processing can benefit from
these advantages. In this review, we survey several proposed quantum and
quantum-inspired machine learning algorithms and their applicability with
current technology to optical signal processing.Comment: European Wireless Conference (EW) 2023 - 6G Driving a Sustainable
Growt
Quantum and Quantum-Inspired Stereographic K Nearest-Neighbour Clustering
Nearest-neighbour clustering is a simple yet powerful machine learning
algorithm that finds natural application in the decoding of signals in
classical optical fibre communication systems. Quantum nearest-neighbour
clustering promises a speed-up over the classical algorithms, but the current
embedding of classical data introduces inaccuracies, insurmountable slowdowns,
or undesired effects. This work proposes the generalised inverse stereographic
projection into the Bloch sphere as an encoding for quantum distance estimation
in k nearest-neighbour clustering, develops an analogous classical counterpart,
and benchmarks its accuracy, runtime and convergence. Our proposed algorithm
provides an improvement in both the accuracy and the convergence rate of the
algorithm. We detail an experimental optic fibre setup as well, from which we
collect 64-Quadrature Amplitude Modulation data. This is the dataset upon which
the algorithms are benchmarked. Through experiments, we demonstrate the
numerous benefits and practicality of using the `quantum-inspired'
stereographic k nearest-neighbour for clustering real-world optical-fibre data.
This work also proves that one can achieve a greater advantage by optimising
the radius of the inverse stereographic projection.Comment: Submitted to Entrop
Testing of Hybrid Quantum-Classical K-Means for Nonlinear Noise Mitigation
Nearest-neighbour clustering is a simple yet powerful machine learning
algorithm that finds natural application in the decoding of signals in
classical optical-fibre communication systems. Quantum k-means clustering
promises a speed-up over the classical k-means algorithm; however, it has been
shown to currently not provide this speed-up for decoding optical-fibre signals
due to the embedding of classical data, which introduces inaccuracies and
slowdowns. Although still not achieving an exponential speed-up for NISQ
implementations, this work proposes the generalised inverse stereographic
projection as an improved embedding into the Bloch sphere for quantum distance
estimation in k-nearest-neighbour clustering, which allows us to get closer to
the classical performance. We also use the generalised inverse stereographic
projection to develop an analogous classical clustering algorithm and benchmark
its accuracy, runtime and convergence for decoding real-world experimental
optical-fibre communication data. This proposed `quantum-inspired' algorithm
provides an improvement in both the accuracy and convergence rate with respect
to the k-means algorithm. Hence, this work presents two main contributions.
Firstly, we propose the general inverse stereographic projection into the Bloch
sphere as a better embedding for quantum machine learning algorithms; here, we
use the problem of clustering quadrature amplitude modulated optical-fibre
signals as an example. Secondly, as a purely classical contribution inspired by
the first contribution, we propose and benchmark the use of the general inverse
stereographic projection and spherical centroid for clustering optical-fibre
signals, showing that optimizing the radius yields a consistent improvement in
accuracy and convergence rate.Comment: 2023 IEEE Global Communications Conference: Selected Areas in
Communications: Quantum Communications and Computin