arXiv subjects

Alonso Viladomat Jasso

Publications and source records attributed to Alonso Viladomat Jasso.

4 recordsLinked to original sources

arXiv2026

Opportunistic QKD: Exploiting Idle Capacity of Classical WDM Systems

While QKD has been proven in lab environments, large-scale implementation requires integration with existing infrastructure. This paper proposes an opportunistic QKD framework that takes advantage of idle spectral capacity, that is, unused channels in classical fibers, to perform QKD while prioritizing classical traffic. To mitigate crosstalk during the co-propagation of classical and quantum signals, we require a guardband of unused channels between classical and quantum signals. We propose a stochastic traffic model, with a deterministic day-night cycle and fractional Gaussian noise. Monte-Carlo simulations of an 80-channel WDM system with our stochastic traffic model demonstrate that 45-65\% of unused spectrum can be repurposed for QKD, depending on the traffic conditions. We also model a key reservoir, with available and recovery states. We define the Reliability Horizon as the 3σ depletion threshold. We find a trade-off between buffer reset levels: increasing the buffer reset level extends the reliability horizon but linearly increases recovery time, resulting in longer service "dark windows". Furthermore, simulations indicate that the first-passage time follows a heavy-tailed distribution, which is accurately characterized by a composite model combining a diurnal trend and a Bihill transition function. This framework enables network operators to optimize buffer parameters for specific Service Level Agreements (SLAs) in real-world environments.

quant-ph↗

arXiv2023

Testing of Hybrid Quantum-Classical K-Means for Nonlinear Noise Mitigation

Nearest-neighbour clustering is a powerful set of heuristic algorithms that find natural application in the decoding of signals transmitted using the M-Quadrature Amplitude Modulation (M-QAM) protocol. Lloyd et al. proposed a quantum version of the algorithm that promised an exponential speed-up. We analyse the performance of this algorithm by simulating the use of a hybrid quantum-classical implementation of it upon 16-QAM and experimental 64-QAM data. We then benchmark the implementation against the classical k-means clustering algorithm. The choice of quantum encoding of the classical data plays a significant role in the performance, as it would for the hybrid quantum-classical implementation of any quantum machine learning algorithm. In this work, we use the popular angle embedding method for data embedding and the swap test for overlap estimation. The algorithm is emulated in software using Qiskit and tested on simulated and real-world experimental data. The discrepancy in accuracy from the perspective of the induced metric of the angle embedding method is discussed, and a thorough analysis regarding the angle embedding method in the context of distance estimation is provided. We detail an experimental optic fibre setup as well, from which we collect 64-QAM data. This is the dataset upon which the algorithms are benchmarked. Finally, some promising current and future directions for further research are discussed.

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arXiv2023

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.

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arXiv2023

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 k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to not currently 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.

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