We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canonical quantization techniques to derive a quantum model grounded in circuit quantum electrodynamics. Numerical simulations demonstrate key dynamical features of the quantized memristor and LIF neuron in the weak-coupling and adiabatic regime, including memory effects and spiking behaviour. This work establishes a foundational model for quantum neuromorphic computing, offering a pathway towards biologically inspired quantum spiking neural networks and new paradigms in quantum machine learning.
We present a computational method to automatically design then-qubit realisations of quantum algorithms. Our approach leverages a domain-specific language (DSL) that enables the construction of quantum circuits via modular building blocks, making it well-suited for evolutionary search. In this DSL quantum circuits are abstracted beyond the usual gate-sequence description and scale automatically to any problem size. This enables us to learn the algorithm structure rather than a specific unitary implementation. We demonstrate our method by automatically designing three known quantum algorithms—the Quantum Fourier Transform, the Deutsch-Jozsa algorithm, and Grover's search. Remarkably, we were able to learn the general implementation of each algorithm by considering examples of circuits containing at most 5-qubits. Our method proves robust, as it maintains performance across increasingly large ...
We construct a hybrid quantum-classical approach for the K-Nearest Neighbour algorithm, where the information is embedded in a phase-distributed multimode coherent state with the assistance of a single photon. The task of finding the closeness between the data points is governed by a coherent state-based distance metric. Sorting and class assignment are performed by a classical processor. We provide the quantum optical architecture corresponding to our algorithm. The subordinate optical network is validated by numerical simulation. We also optimize the computational resources of the algorithm in the context of space, energy requirements and gate complexity. The underlying distance metric is also found to be comparable to the Euclidean distance and manifests high classification performances for diverse and well-known public benchmark and synthesized data sets.
We introduce a method that generates ground-state Ansätze for quantum many-body systems that are both analytically tractable and accurate over wide parameter regimes. Our approach leverages a custom symbolic language to construct tensor network states via an evolutionary algorithm. This language provides operations that allow the generated tensor network states to automatically scale with system size. Consequently, we can evaluate Ansatz fitness for small systems, which is computationally efficient, while favoring structures that continue to perform well with increasing system size. This ensures that the Ansatz captures robust features of the ground-state structure. Remarkably, we find analytically tractable Ansätze with a degree of universality, which encode correlations, capture finite-size effects, accurately predict ground-state energies, and offer a good description of critical phenomena. We demonstrate ...
Collision models (CMs) describe an open system interacting in sequence with elements of an environment, termed ancillas. They have been established as a useful tool for analyzing non-Markovian open quantum dynamics based on the ability to control the environmental memory through simple feedback mechanisms. In this work, we investigate how ancilla-ancilla (AA) entanglement can serve as a mechanism for controlling the non-Markovianity of an open system, focusing on an operational approach to generating correlations within the environment. To this end, we first demonstrate that the open dynamics of CMs with sequentially generated correlations between groups of ancillas can be mapped onto a composite CM, where the memory part of the environment is incorporated into an enlarged Markovian system. We then apply this framework to an all-qubit CM, and show that non-Markovian behavior emerges ...
In order to leverage quantum computers for machine learning tasks such as image classification, consideration is required. Noisy Intermediate-Scale Quantum (NISQ) computers have limitations that include noise, scalability, read-in and read-out times, and gate operation times. Therefore, strategies should be devised to mitigate the impact complex datasets that can have on the overall efficiency of a quantum machine learning pipeline. This may otherwise lead to excessive resource demands or noise. We apply a classical feature extraction using a ResNet10-inspired convolutional autoencoder to reduce dataset dimensionality and extract abstract, meaningful features before feeding them into a quantum layer. The chosen quantum layer is a quantum-enhanced support vector machine (QSVM), as SVMs typically do not require large sample sizes to identify patterns in data and have short-depth quantum circuits ...
South Africa and China have shattered new frontiers in secure communication with the launch of the world's longest intercontinental quantum satellite link-stretching an astonishing 12,900 kilometres. This groundbreaking collaboration marks the first-ever quantum satellite connection in the Southern Hemisphere, using principles of quantum mechanics to create virtually unbreakable encryption. Beyond the high-tech achievement, it places South Africa at the forefront of global quantum innovation, opening doors to future advances in data security and technology.
Metamaterials are a class of artificially engineered materials with periodic structures possessing exceptional properties not found in conventional materials. This definition can be extended when we introduce a degree of freedom by adding quantum elements such as quantum dots, cold atoms, Josephson junctions, and molecules, making metamaterials highly valuable for various quantum applications. Metamaterials have been used to achieve invisibility cloaking, super-resolution, energy harvesting, and sensing, among other applications. Most of these applications are performed in the classical regime. Metamaterials have gradually made their way into the quantum regime since the advent of quantum computing and quantum sensing and imaging. Quantum metamaterials are a relatively new technology, and their use in quantum information processing has proliferated. We restrict this study to quantum state ...