Generative modeling of quantum simulators

Kusal M. Abeywickrama, Srinjoy Ganguly, Luis Gerardo Ayala Bertel +1 more

Saverio Monaco, Florian Rehm, M. Scham +3 more

Quantum-based generative models offer a promising approach for simulating complex phenomena in high-energy physics, such as calorimeter showers used for particle identification and energy reconstruction at the LHC. We propose the Quantum Angle Generator (QAG), a variational quantum model trained with a Maximum Mean Discrepancy loss to generate images from the probabilistic outputs of quantum circuits. We study model and training hyperparameters, assess the impact of quantum noise during training and inference, and evaluate the QAG in both noiseless simulations and simulated Noisy Intermediate-Scale Quantum hardware. Results show that the QAG can adapt through learning to hardware-induced noise, yielding stable, high-quality outputs even under significant noise and calibration variability.

Yuchen Zhu, Molei Tao, Jin, Yuebo +1 more

In quantum many-body systems, measurements can induce qualitative new features, but their simulation is hindered by the exponential complexity involved in sampling the measurement results. We propose to use machine learning to assist the simulation of measurement-induced quantum phenomena. In particular, we focus on the measurement-altered quantum criticality protocol and generate local reduced density matrices of the critical chain given random measurement results. Such generation is enabled by a physics-preserving conditional diffusion generative model, which learns an observation-indexed probability distribution of an ensemble of quantum states, and then samples from that distribution given an observation.

Christian W. Bauer, Anthony N. Ciavarella, Jad C. Halimeh +19 more

High-energy collisions produce large numbers of quarks and gluons that are bound into hadrons by the confining interactions of quantum chromodynamics (QCD). This hadronization process is intrinsically real-time and non-perturbative. Event generators describe hadronization using phenomenological models with parameters fitted to data rather than calculated from QCD. Uncertainties in these models limit the precision of certain measurements at the Large Hadron Collider. Quantum computers could enable first-principles simulations of these dynamics. Existing quantum simulations of gauge theories have demonstrated string-breaking dynamics. This white paper summarizes the conclusions of the InQubator for Quantum Simulation workshop \textit{From String Dynamics to Event Generation with Quantum Simulation}. We develop a roadmap for extending these simulations to inform phenomenological models of hadronization. A sequence of toy models is proposed for connecting current quantum simulations to QCD. The observables that should be extracted are listed, and the challenges that separate present simulations from quantitative predictions are identified. We propose a staged program in which quantum simulations first provide qualitative modifications to event generators and later provide quantitative input.

Tzvetan S. Metodi, Frederic T. Chong

Hans A. De Raedt, Kristel F. L. Michielsen, Anthony Hams +2 more