Outputs (6)

Simulating quantum field theories on continuous-variable quantum computers (2024)
Journal Article
Abel, S., Spannowsky, M., & Williams, S. (2024). Simulating quantum field theories on continuous-variable quantum computers. Physical Review A, 110(1), Article 012607. https://doi.org/10.1103/physreva.110.012607

We delve into the use of photonic quantum computing to simulate quantum mechanics and extend its application towards quantum field theory. We develop and prove a method that leverages this form of continuous-variable quantum computing (CVQC) to repro... Read More about Simulating quantum field theories on continuous-variable quantum computers.

Training neural networks with universal adiabatic quantum computing (2024)
Journal Article
Abel, S., Criado, J. C., & Spannowsky, M. (2024). Training neural networks with universal adiabatic quantum computing. Frontiers in Artificial Intelligence, 7, Article 1368569. https://doi.org/10.3389/frai.2024.1368569

The training of neural networks (NNs) is a computationally intensive task requiring significant time and resources. This article presents a novel approach to NN training using adiabatic quantum computing (AQC), a paradigm that leverages the principle... Read More about Training neural networks with universal adiabatic quantum computing.

Quantum optimization of complex systems with a quantum annealer (2022)
Journal Article
Abel, S., Blance, A., & Spannowsky, M. (2022). Quantum optimization of complex systems with a quantum annealer. Physical Review A, 106(4), https://doi.org/10.1103/physreva.106.042607

We perform an in-depth comparison of quantum annealing with several classical optimization techniques, namely, thermal annealing, Nelder-Mead, and gradient descent. The focus of our study is large quasicontinuous potentials that must be encoded using... Read More about Quantum optimization of complex systems with a quantum annealer.

Completely quantum neural networks (2022)
Journal Article
Abel, S., Criado, J. C., & Spannowsky, M. (2022). Completely quantum neural networks. Physical Review A, 106(2), Article 022601. https://doi.org/10.1103/physreva.106.022601

Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a sta... Read More about Completely quantum neural networks.

Quantum-Field-Theoretic Simulation Platform for Observing the Fate of the False Vacuum (2021)
Journal Article
Abel, S., & Spannowsky, M. (2021). Quantum-Field-Theoretic Simulation Platform for Observing the Fate of the False Vacuum. PRX Quantum, 2(1), Article 010349. https://doi.org/10.1103/prxquantum.2.010349

We design and implement a quantum annealing simulation platform to observe and study dynamical processes in quantum field theory (QFT). Our approach encodes the field theory as an Ising model, which is then solved by a quantum annealer. As a proof of... Read More about Quantum-Field-Theoretic Simulation Platform for Observing the Fate of the False Vacuum.

Quantum computing for quantum tunneling (2021)
Journal Article
Abel, S., Chancellor, N., & Spannowsky, M. (2021). Quantum computing for quantum tunneling. Physical Review D, 103(1), Article 016008. https://doi.org/10.1103/physrevd.103.016008

We demonstrate how quantum field theory problems can be practically encoded by using a discretization of the field theory problem into a general Ising model, with the continuous field values being encoded into Ising spin chains. To illustrate the met... Read More about Quantum computing for quantum tunneling.