Juris Ulmanis

We demonstrate an end-to-end, multi-stage attack against a quantum neural network (QNN) model that is executed on a trapped-ion quantum computer. Our chain combines side-channel reconnaissance, crosstalk characterization, adversarial example generation, and a physical crosstalk attack that realizes the adversarial perturbation on the device. We cover the full attack chain on ion traps and report the corresponding superconducting-hardware experiments in the appendix. We discuss implications for QaaS providers and hardware mitigations.

We experimentally demonstrate local robust shadows on a trapped-ion quantum computing system, a protocol developed to counteract measurement errors. We alternate between a calibration stage and the shadow estimation stage and also introduce Pauli-X-twirling before measurements in both stages to symmetrize error rates. We then demonstrate the protocol on a trapped-ion quantum computer with artificially shortened measurement pulse duration. This yields faster experiments at the cost of increased error rates which are subsequently mitigated by the robust shadow protocol. We benchmark this approach on three exemplary quantum states: a local Haar random state, as well as standard and Pauli-correlation-encoded QAOA states. In all three cases, the local robust shadow protocol succeeds at mitigating the increased error rates hailing from shorter measurement pulse durations.

Spectral crowding of collective motional modes limits the fidelity of entangling interactions in trapped-ion quantum processors by inducing off-resonant coupling to spectator modes. We introduce a geometric-phase entangling interaction driven by a transverse, time-dependent structured-light force. By applying the force in a plane orthogonal to the optical propagation direction, we reduce the effects of spectral crowding while preserving single-ion addressing. The scheme is compatible with arbitrary qubit encodings, provided that the qubit states experience a differential AC Stark shift. We experimentally realise high-fidelity two-qubit gates with error rates below $5\times10^{-3}$ in ion crystals containing up to 12 ions confined within a single potential well. These results establish gradient-field light-shift gates as a scalable approach to high-fidelity entangling generation in spectrally crowded trapped-ion systems.

Trapped ions are among the most promising platforms for realizing a large-scale quantum information processor. Current progress focuses on integrating optical and electronic components into microfabricated ion traps to allow scaling to large numbers of ion qubits. Most available fabrication strategies for such integrated processors employ monolithic integration of all processor components and rely heavily on CMOS-compatible semiconductor fabrication technologies that are not optimized for the requirements of a trapped-ion quantum processor. In this work, we present a modular approach in which the processor modules, called chiplets, have specific functions and are fabricated separately. The individual chiplets are then combined using heterogeneous integration techniques. This strategy opens up the possibility of choosing the optimal materials and fabrication technology for each of the chiplets, with a minimum amount of fabrication limitations compared to the monolithic approach. Chiplet technology furthermore enables novel processor functionalities to be added in a cost-effective, modular fashion by adding or modifying only a subset of the chiplets. We describe the design concept of a chiplet-based trapped-ion quantum processor and demonstrate the technology with an example of an integrated individual-ion addressing system for a ten-ion crystal. The addressing system emphasizes the modularity of the chiplet approach, combining a surface ion trap manufactured on a glass substrate with a silicon substrate carrying integrated waveguides and a stack of 3D-printed micro-optics, achieving diffraction-limited focal spots at the ion positions.

Resource-efficient, low-depth implementations of quantum circuits remain a promising strategy for achieving reliable and scalable computation on quantum hardware, as they reduce gate resources and limit the accumulation of noisy operations. Here, we propose a low-depth implementation of a class of Hadamard test circuits, complemented by the development of a parameterized quantum ansatz specifically tailored for variational algorithms that exploit the underlying Hadamard test framework. Our findings demonstrate a significant reduction in single- and two-qubit gate counts, suggesting a reliable circuit architecture for noisy intermediate-scale quantum (NISQ) devices. Building on this foundation, we tested our low-depth scheme to investigate the expressive capacity of the proposed parameterized ansatz in simulating nonlinear Burgers' dynamics. The resulting variational quantum states faithfully capture the shockwave feature of the turbulent regime and maintain high overlaps with classical benchmarks, underscoring the practical effectiveness of our framework. Furthermore, we evaluate the effect of hardware noise by modeling the error properties of real quantum processors and by executing the variational algorithm on a trapped-ion-based IBEX Q1 device. The outcomes of our demonstrations highlight the resilience of our low-depth scheme in the turbulent regime, consistently preparing high-fidelity variational states that exhibit strong agreement with classical benchmarks. Our work contributes to the advancement of resource-efficient strategies for quantum computation, offering a robust framework for tackling a range of computationally intensive problems across numerous applications.

Warehouse optimization stands as a critical component for enhancing operational efficiency within the industrial sector. By strategically streamlining warehouse operations, organizations can achieve significant reductions in logistical costs such as the necessary footprint or traveled path, and markedly improve overall workflow efficiency including retrieval times or storage time. Despite the availability of numerous algorithms designed to identify optimal solutions for such optimization challenges, certain scenarios demand computational resources that exceed the capacities of conventional computing systems. In this context, we adapt a formulation of a warehouse optimization problem specifically tailored as a binary optimization problem and implement it in a trapped-ion quantum computer.

We present the first hardware implementation of electrostatic interaction energies using a trapped-ion quantum computer. As test system for our computation, we focus on the reduction of $\mathrm{NO}$ to $\mathrm{N}_2\mathrm{O}$ catalyzed by a nitric oxide reductase (NOR). The quantum computer is used to generate an approximate ground state within the NOR active space. To efficiently measure the necessary one-particle density matrices, we incorporate fermionic basis rotations into the quantum circuit without extending the circuit length, laying the groundwork for further efficient measurement routines using factorizations. Measurements in the computational basis are then used as inputs for computing the electrostatic interaction energies on a classical computer. Our experimental results strongly agree with classical noise-less simulations of the same circuits, finding electrostatic interaction energies within chemical accuracy despite hardware noise. This work shows that algorithms tailored to specific observables of interest, such as interaction energies, may require significantly fewer quantum resources than individual ground state energies would in the straightforward supermolecular approach.