9 papers
Robust Quantum Machine Learning for Collider Event Selection under Detector Variability
Christopher Brown, Michael Spannowsky, Simon Williams
Robust machine-learning methods are becoming increasingly important for high-energy physics data analysis as experiments enter the era of higher luminosity and future higher-energy…
Qumode Tensor Networks for False Vacuum Decay in Quantum Field Theory
Steven Abel, Michael Spannowsky, Simon Williams
False vacuum decay in scalar quantum field theory (QFT) is a cornerstone of early Universe cosmology and high energy physics, yet its real-time dynamics is essentially inaccessible…
Berry's phase on photonic quantum computers
Steven Abel, Iwo Wasek, Simon Williams
We formulate a continuous-variable quantum computing (CVQC) algorithm to study Berry's phase on photonic quantum computers. We demonstrate that CVQC allows the simulation of charge…
Continuous-variable photonic quantum extreme learning machines for fast collider-data selection
Benedikt Maier, Michael Spannowsky, Simon Williams
We study continuous-variable photonic quantum extreme learning machines as fast, low-overhead front-ends for collider data processing. Data is encoded in photonic modes through qua…
Real-Time Scattering Processes with Continuous-Variable Quantum Computers
Steven Abel, Michael Spannowsky, Simon Williams
We propose a framework for simulating the real-time dynamics of quantum field theories (QFTs) using continuous-variable quantum computing (CVQC). Focusing on ()-dimensional $Ï…
Enhancing Quantum Field Theory Simulations on NISQ Devices with Hamiltonian Truncation
James Ingoldby, Michael Spannowsky, Timur Sypchenko +1
Quantum computers can efficiently simulate highly entangled quantum systems, offering a solution to challenges facing classical simulation of Quantum Field Theories (QFTs). This pa…