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State-of-the-art noisy-intermediate-scale quantum (NISQ) processors are currently implemented across a variety of hardware platforms, each with their own distinct gatesets. As such, circuit compilation should not only be aware of, but also deeply connect to, the native gateset and noise properties of each. Trapped-ion processors are one such platform that provides a gateset that can be continuously parameterized across both one- and two-qubit gates. Here we use the Quantum Scientific Computing Open User Testbed (QSCOUT) to study noise-aware compilations focused on continuously parameterized two-qubit $\mathcal{ZZ}$ gates (based on the M{\o}lmer-S{\o}rensen interaction) using $\textbf{Superstaq}$, a quantum software platform for hardware-aware circuit compiler optimizations. We discuss the realization of $\mathcal{ZZ}$ gates with arbitrary angle on the all-to-all connected trapped-ion system. Then we discuss a variety of different compiler optimizations that innately target these $\mathcal{ZZ}$ gates and their noise properties. These optimizations include moving from a restricted maximally entangling gateset to a continuously parameterized one, swap mirroring to further reduce total entangling angle of the operations, focusing the heaviest $\mathcal{ZZ}$ angle participation on the best performing gate pairs, and circuit approximation to remove the least impactful $\mathcal{ZZ}$ gates. We demonstrate these compilation approaches on the hardware with randomized quantum volume circuits, observing the potential to realize a larger quantum volume as a result of these optimizations. Using differing yet complementary analysis techniques, we observe the distinct improvements in system performance provided by these noise-aware compilations and study the role of stochastic and coherent error channels for each compilation choice., Comment: 17 pages, 13 figures

Average circuit eigenvalue sampling (ACES) was introduced by Flammia in arXiv:2108.05803 as a protocol to characterize the Pauli error channels of individual gates across the device simultaneously. The original paper posed using ACES to characterize near-term devices as an open problem. This work advances in this direction by presenting a full implementation of ACES for real devices and deploying it to Superstaq arXiv:2309.05157, along with a device-tailored resource estimation obtained through simulations and experiments. Our simulations show that ACES is able to estimate one- and two-qubit non-uniform Pauli error channels to an average eigenvalue absolute error of under $0.003$ and total variation distance of under 0.001 between simulated and reconstructed probability distributions over Pauli errors with $10^5$ shots per circuit using 5 circuits of depth 14. The question of estimating general error channels through twirling techniques in real devices remains open, as it is dependent on a device's native gates, but simulations with the Clifford set show results in agreement with reported hardware data. Experimental results on IBM's Algiers and Osaka devices are presented, where we characterize their error channels as Pauli channels without twirling., Comment: 7 pages, 6 figures

Combinatorial optimization problems that arise in science and industry typically have constraints. Yet the presence of constraints makes them challenging to tackle using both classical and quantum optimization algorithms. We propose a new quantum algorithm for constrained optimization, which we call quantum constrained Hamiltonian optimization (Q-CHOP). Our algorithm leverages the observation that for many problems, while the best solution is difficult to find, the worst feasible (constraint-satisfying) solution is known. The basic idea is to to enforce a Hamiltonian constraint at all times, thereby restricting evolution to the subspace of feasible states, and slowly "rotate" an objective Hamiltonian to trace an adiabatic path from the worst feasible state to the best feasible state. We additionally propose a version of Q-CHOP that can start in any feasible state. Finally, we benchmark Q-CHOP against the commonly-used adiabatic algorithm with constraints enforced using a penalty term and find that Q-CHOP performs consistently better on a wide range of problems, including textbook problems on graphs, knapsack, combinatorial auction, as well as a real-world financial use case, namely bond exchange-traded fund basket optimization.