Travis Desell

Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manually designed circuit templates, fixed heuristics, or automated rules, face limitations in scalability, flexibility, and adaptability, often producing circuits that are poorly matched to the specific problem or quantum hardware. This work presents the Evolutionary eXploration of Augmenting Quantum Circuits (EXAQC), an evolutionary approach to the automated design and training of parameterized quantum circuits (PQCs) which extends on strategies from neuroevolution and genetic programming. EXAQC jointly searches over gate types, qubit connectivity, parameterization, and circuit depth while respecting hardware and noise constraints. It supports both Qiskit and Pennylane libraries, allowing the user to configure allowed architectural components. Preliminary results demonstrate that circuits evolved on classification tasks are able to achieve over 90% accuracy on most of the benchmark datasets with a limited computational budget, and are able to emulate target circuit quantum states with high fidelity scores.

Asynchronous evolutionary algorithms (AEAs) accelerate neuroevolution but are affected by evaluation-time bias, where networks that train quickly re-enter the population sooner and disproportionately influence evolutionary outcomes. In this work, we investigate memetic scheduling strategies that dynamically determine how much backpropagation should be done by generated genomes over the course of an asynchronous neuroevolution algorithm to see if this can mitigate evaluation-time bias under a strict 1-hour wall-clock budget per run. We compare constant, randomized, and exponentially scaled memetic backpropagation scheduling strategies across multiple time-series forecasting datasets and target variables. Our experiments find an interesting negative result - while it was expected that increasing the amount of backpropagation over time would allow a search to break out of local minima and mitigate time bias, instead we show that exponentially increasing training schedules consistently underperform. We further show that different forecasting targets find better solutions with different memetic schedules, highlighting the no free lunch nature of the problem. We do, however, find a correlation between average improvement per backpropagation epoch (given a varying constant number of backpropagation epochs) and schedules performing well, which can help guide memetic schedule selection.

Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manually designed circuit templates, fixed heuristics, or automated rules, face limitations in scalability, flexibility, and adaptability, often producing circuits that are poorly matched to the specific problem or quantum hardware. In this work, we propose the Evolutionary eXploration of Augmenting Quantum Circuits (EXAQC), an evolutionary approach to the automated design and training of parameterized quantum circuits (PQCs) which leverages and extends on strategies from neuroevolution and genetic programming. The proposed method jointly searches over gate types, qubit connectivity, parameterization, and circuit depth while respecting hardware and noise constraints. The method supports both Qiskit and Pennylane libraries, allowing the user to configure every aspect. This work highlights evolutionary search as a critical tool for advancing quantum machine learning and variational quantum algorithms, providing a principled pathway toward scalable, problem-aware, and hardware-efficient quantum circuit design. Preliminary results demonstrate that circuits evolved on classification tasks are able to achieve over 90% accuracy on most of the benchmark datasets with a limited computational budget, and are able to emulate target circuit quantum states with high fidelity scores.

This work introduces a novel generative continual learning framework based on self-organizing maps (SOMs) and variational autoencoders (VAEs) to enable memory-efficient replay, eliminating the need to store raw data samples or task labels. For high-dimensional input spaces, such as of CIFAR-10 and CIFAR-100, we design a scheme where the SOM operates over the latent space learned by a VAE, whereas, for lower-dimensional inputs, such as those found in MNIST and FashionMNIST, the SOM operates in a standalone fashion. Our method stores a running mean, variance, and covariance for each SOM unit, from which synthetic samples are then generated during future learning iterations. For the VAE-based method, generated samples are then fed through the decoder to then be used in subsequent replay. Experimental results on standard class-incremental benchmarks show that our approach performs competitively with state-of-the-art memory-based methods and outperforms memory-free methods, notably improving over best state-of-the-art single class incremental performance on CIFAR-10 and CIFAR-100 by nearly $10$\% and $7$\%, respectively. Our methodology further facilitates easy visualization of the learning process and can also be utilized as a generative model post-training. Results show our method's capability as a scalable, task-label-free, and memory-efficient solution for continual learning.