A recent advancement in quantum computing shows a quantum advantage of certified randomness on the racetrack processor. This work investigates the execution efficiency of this architecture for general-purpose programs. We first explore the impact of increasing zones on runtime efficiency. Counterintuitively, our evaluations using variational programs reveal that expanding zones may degrade runtime performance under the existing scheduling policy. This degradation may be attributed to the increase in track length, which increases ion circulation overhead, offsetting the benefits of enhanced parallelism. To mitigate this, the proposed Plutarch exploits 3 strategies: (i) unitary decomposition and translation to maximize zone utilization, (ii) prioritizing the execution of nearby gates over ion circulation, and (iii) implementing shortcuts to provide the alternative path.

Quantum Optimal Control (QOC) frameworks are powerful tools for compiling quantum circuits into low-latency hardware control pulses, but recent studies suffer from two critical limitations: lengthy compilation times and potential logical inconsistencies from flawed gate grouping strategies. In this work, we introduce d'ArQ, a novel QOC framework that solves these challenges. (i) We identify and resolve the causality problem, a flaw in greedy partitioning that can produce invalid schedules, by introducing a DAG-based grouping algorithm with assigning mergeability to each group so that it guarantees logical correctness. (ii) To mitigate compilation times, we use a pre-computed library of pulses derived from random unitary matrices to provide a high-quality random initialization for pulse optimization. (iii) Diverging from prior work based on GRAPE, d'ArQ is built on the GOAT algorithm. We demonstrate that the choice of analytic basis is a critical hyperparameter and introduce a heuristic cost model to dynamically select the optimal basis for each synthesis task, improving pulse performance. When evaluated against the state-of-the-art baseline PAQOC on a realistic, inhomogeneous hardware model, d'ArQ demonstrates superior performance. Notably, d'ArQ reduces circuit latency up to 22.8% and compilation time up to 56.8%, establishing a more robust and physically realistic path for circuit compilation.

A zoned neutral atom architecture achieves exceptional fidelity by segregating the execution spaces of 1- and 2-qubit gates, being a promising candidate for high-accuracy quantum systems. Unfortunately, na'ively applying programs designed for static qubit topologies to zoned architectures may result in most execution time being consumed by intra-zone travels of atoms. To address this, we introduce Mantra (Minimizing trAp movemeNts for aTom aRray Architectures), which rewrites quantum programs to reduce the interleaving of single- and two-qubit gates. Mantra incorporates three strategies: (i) a fountain-shaped controlled-Z (CZ) chain, (ii) ZZ-interaction protocol without a 1-qubit gate, and (iii) preemptive gate scheduling. Mantra reduces inter-zone movements by 68%, physical gate counts by 35%, and improves circuit fidelities by 17% compared to the standard executions.

Recent advances in quantum computing introduce the ability to reuse qubits through mid-circuit measurements, thereby enhancing the efficiency of quantum devices with limited computational resources.However, identifying optimal reuse opportunities in quantum circuits remains challenging due to the intricate dependencies between quantum gates.Existing frameworks address this by either directly searching for reuse opportunities or converting circuits into directed acyclic graphs (DAGs).Unfortunately, these frameworks may require exponential search complexity or may not always ensure optimal results due to their non-deterministic property.To overcome these challenges, we propose QR-Map (Qubit Reuse Map), a map-based framework that abstracts computational dependencies for efficient qubit reuse.By extracting and aligning two-qubit gates, QR-Map facilitates dependency detection and ensures qubit savings without incurring excessive idle time.This approach achieves an optimal balance between gate serialization depth and crosstalk reduction.Evaluations with various quantum circuit benchmarks demonstrate that quantum circuits optimized with QR-Map achieve average reductions of 20% in qubit usage, 25% in circuit depth, and 22% in SWAP insertions compared to those optimized with the state-of-the-art framework.

The quantum approximate optimization algorithm (QAOA) is introduced to efficiently solve combinatorial optimization problems. Despite the promise of QAOA, the cost of executing QAOA circuits at scale for quantum advantage may still be excessive for the near-future quantum device. We observe the increasing overhead of QAOA circuit execution in the native gate translation. To execute QAOA circuits on a real quantum computing device, Hamiltonians composed of predefined specific rotations (e.g., ZZ and X) should be decomposed into finite native gates. By adopting rotational combinations that utilize native gates more directly than the standard QAOA circuit model, the execution cost on real quantum devices can be reduced. In this study, we propose Racoon (Rotational Space Virtualization for QAOA Ansatz), an algorithm-hardware co-design approach that revisits the synthesis conditions of QAOA circuits and selects alternative candidates with different rotational combinations. Our analysis of six commercial quantum processors demonstrates that applying Racoon to QAOA circuits for the 4-node Sherrington-Kirkpatrick model reduces the number of native gates by an average of 23% and up to 79%. Consequently, using Racoon results in 43% fewer training epochs, 41% lower training energy consumption, and a 6% improvement in inference on average compared to standard QAOA. Racoon consistently reduces circuit depth as the number of qubits and layers increases, achieving 123 × more circuit depth reduction compared to the recently proposed Depth First Search (DFS)-based method. Furthermore, we confirm that Racoon’s method can be extended to State-of-The-Art QAOAs with modified ansätze and to the variational quantum eigensolver (VQE).