Julien Baglio

With the latest advances in quantum computing technology, we are gradually moving from the noisy intermediate-scale quantum (NISQ) era characterized by hardware limited in the number of qubits and plagued with quantum noise, to the age of quantum utility where both the newest hardware and software methods allow for tackling problems which have been deemed difficult or intractable with conventional classical methods. One of these difficult problems is the simulation of one-dimensional (1D) nuclear magnetic resonance (NMR) spectra, a major tool to learn about the structure of molecules, helping the design of new materials or drugs. Using advanced error mitigation and error suppression techniques from Q-CTRL together with the latest commercially available superconducting-qubit quantum computer from IBM and trapped-ion quantum computer from IonQ, we present the quantum Hamiltonian simulation of liquid-state 1D NMR spectra in the high-field regime for spin systems up to 34 spins. Our pipeline has a major impact on the ability to execute deep quantum circuits with the reduction of quantum noise, improving mean square error by a factor of 22. It allows for the execution of deep quantum circuits and obtaining salient features of the 1D NMR spectra for both 16-spin and 22-spin systems, as well as a 34-spin system, which lies beyond the regime where unrestricted full Liouvillespace simulations are practical (32 spins, the Liouville limit). Our work is a step toward near-term quantum utility in NMR spectroscopy.

In the current noisy intermediate scale quantum computing era, and after the significant progress of the quantum hardware we have seen in the past few years, it is of high importance to understand how different quantum algorithms behave on different types of hardware. This includes whether or not they can be implemented at all and, if so, what the quality of the results is. This work quantitatively demonstrates, for the first time, how the quantum generator architecture for the style-based quantum generative adversarial network (qGAN) can not only be implemented but also yield good results on two very different types of hardware for data augmentation: the IBM bm_torino quantum computer based on the Heron chip using superconducting transmon qubits and the aria-1 IonQ quantum computer based on trapped-ion qubits. The style-based qGAN, proposed in 2022, generalizes the state of the art for qGANs and allows for shallow-depth networks. The results obtained on both devices are of comparable quality, with the aria-1 device delivering somewhat more accurate results than the ibm_torino device, while the runtime on ibm_torino is significantly shorter than on aria-1. Parallelization of the circuits, using up to 48 qubits on IBM quantum systems and up to 24 qubits on the IonQ system, is also presented, reducing the number of submitted jobs and allowing for a substantial reduction of the runtime on the quantum processor to generate the total number of samples.

We propose and assess an alternative quantum generator architecture in the context of generative adversarial learning for Monte Carlo event generation, used to simulate particle physics processes at the Large Hadron Collider (LHC). We validate this methodology by implementing the quantum network on artificial data generated from known underlying distributions. The network is then applied to Monte Carlo-generated datasets of specific LHC scattering processes. The new quantum generator architecture leads to a generalization of the state-of-the-art implementations, achieving smaller Kullback-Leibler divergences even with shallow-depth networks. Moreover, the quantum generator successfully learns the underlying distribution functions even if trained with small training sample sets; this is particularly interesting for data augmentation applications. We deploy this novel methodology on two different quantum hardware architectures, trapped-ion and superconducting technologies, to test its hardware-independent viability.