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Simulations of lattice gauge theories on noisy quantum hardware inherently suffer from violations of the gauge symmetry due to coherent and incoherent errors of the underlying physical system that implements the simulation. These gauge violations cause the simulations to become unphysical requiring the result of the simulation to be discarded. We investigate an active correction scheme that relies on detecting gauge violations locally and subsequently correcting them by dissipatively driving the system back into the physical gauge sector. We show that the correction scheme not only ensures the protection of the gauge symmetry, but it also leads to a longer validity of the simulation results even within the gauge-invariant sector. Finally, we discuss further applications of the scheme such as preparation of the many-body ground state of the simulated system., Comment: 9 pages, 8 figures

In recent years, the number of hybrid algorithms that combine quantum and classical computations has been continuously increasing. These two approaches to computing can mutually enhance each others' performances thus bringing the promise of more advanced algorithms that can outmatch their pure counterparts. In order to accommodate this new class of codes, a proper environment has to be created, which enables the interplay between the quantum and classical hardware. For many of these hybrid processes the coherence time of the quantum computer arises as a natural time constraint, making it crucial to minimize the classical overhead. For ion-trap quantum computers however, this is a much less limiting factor than with superconducting technologies, since the relevant timescale is on the order of seconds instead of microseconds. In fact, this long coherence time enables us to develop a scheme for real-time control of quantum computations in an interpreted programming language like Python. In particular, compilation of all instructions in advance is not necessary, unlike with superconducting qubits. This keeps the implementation of hybrid algorithms simple and also lets users benefit from the rich environment of existing Python libraries. In order to show that this approach of interpreted quantum-classsical computations (IQCC) is feasible, we bring real-world examples and evaluate them in realistic benchmarks., Comment: 8 pages, 6 figures, 1 table

A promising architecture for scaling up quantum computers based on trapped ions are so called Quantum Charged-Coupled Devices (QCCD). These consist of multiple ion traps, each designed for solving specific tasks, that are connected by transport links. In this paper we present the backend compiler phases needed for running quantum circuits on a QCCD architecture, while providing strategies to solve the optimization problems that occur when generating assembly instructions. We implement and test these strategies for the QVLS-Q1 chip architecture., Comment: 6 pages, 4 figures

Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical ground- and steady states with this ansatz using a number of variational parameters that scales polynomially in system size. This compressed representation allows us to reconstruct states with high classical fidelities outperforming standard methods such as maximum likelihood estimation. Furthermore, it achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data., Comment: 11 pages, 6 figures

We present a novel approach for the integration of scattering cross sections and the generation of partonic event samples in high-energy physics. We propose an importance sampling technique capable of overcoming typical deficiencies of existing approaches by incorporating neural networks. The method guarantees full phase space coverage and the exact reproduction of the desired target distribution, in our case given by the squared transition matrix element. We study the performance of the algorithm for a few representative examples, including top-quark pair production and gluon scattering into three- and four-gluon final states., Comment: 19 pages, 6 figures, updated references, extended discussion