Quantum Error Correction (QEC) codes form the foundation of Fault-Tolerant Quantum Computing (FTQC) and predominantly use the Clifford+T gate set. Recently, Clifford operations have become the key performance bottleneck in implementing QEC. While state-of-the-art approaches like Pauli-Based Compilation (PBC) reduce Clifford overhead by transforming Clifford gates into Pauli measurements, they do so at the cost of gate-level parallelism, inflating circuit depth and execution times. To overcome these limitations, we introduce TACO, a Transpiler-Architecture Co-design framework that tackles the Clifford bottleneck through circuit and architectural optimization. TACO uses FTQC insights to guide hardware-aware Clifford gate elimination and circuit restructuring, and leverages the resulting optimized circuits to refine architectural design. TACO applies FTQC-specific transformations to aggressively reduce Clifford overhead from rotation synthesis and Toffoli decompositions, while preserving gate-level parallelism. The resulting architecture is optimized for the locality and data-movement patterns of these circuits, enabling high-throughput, resource-efficient execution. Our evaluation across diverse benchmarks shows that TACO achieves up to 21.9x (mean 4.4x) reduction in execution time compared to the state-of-the-art baseline.

To realize the full potential of quantum computers, we must mitigate qubit errors by developing noise-aware algorithms, compilers, and architectures.Thus, simulating quantum programs on highperformance computing (HPC) systems with different noise models is a de facto tool researchers use.Unfortunately, noisy simulators iteratively execute a similar circuit for thousands of trials, thereby incurring significant performance overheads.To address this, we propose a noisy simulation technique called Tree-Based Quantum Circuit Simulation (TQSim) 1 .TQSim exploits the reusability of intermediate results during the noisy simulation, reducing computation.TQSim dynamically partitions a circuit into several subcircuits.It then reuses the intermediate results from these subcircuits during computation.Compared to a noisy Qulacsbased baseline simulator, TQSim achieves a speedup of up to 3.89× for noisy simulations.TQSim is designed to be efficient with multinode setups while also maintaining tight fidelity bounds.

The Quantum Approximate Optimization Algorithm (QAOA) addresses combinatorial optimization challenges by converting inputs to graphs. However, the optimal parameter searching process of QAOA is greatly affected by noise. Larger problems yield bigger graphs, requiring more qubits and making their outcomes highly noise-sensitive. This paper introduces Red-QAOA, leveraging energy landscape concentration via a simulated annealing-based graph reduction.

Quantum computers face challenges due to limited resources, particularly in cloud environments. Despite these ob-stacles, Variational Quantum Algorithms (VQAs) are considered promising applications for present-day Noisy Intermediate-Scale Quantum (NISQ) systems. VQAs require multiple optimization iterations to converge on a globally optimal solution. Moreover, these optimizations, known as restarts, need to be repeated from different points to mitigate the impact of noise. Unfortunately, the job scheduling policies for each VQA task in the cloud are heavily unoptimized. Notably, each VQA execution instance is typically scheduled on a single NISQ device. Given the variety of devices in the cloud, users often prefer higher-fidelity devices to ensure higher-quality solutions. However, this preference leads to increased queueing delays and unbalanced resource utilization. We propose Qoncord, an automated job scheduling framework to address these cloud-centric challenges for VQAs. Qoncord leverages the insight that not all training iterations and restarts are equal, Qoncord strategically divides the training process into exploratory and fine-tuning phases. Early exploratory iterations, more resilient to noise, are executed on less busy machines, while fine-tuning occurs on high-fidelity machines. This adaptive approach mitigates the impact of noise, optimizes resource usage, and reduces queuing delays in cloud environments. Qoncord also significantly reduces execution time and minimizes restart overheads by eliminating low-performance iterations. Thus, Qoncord offers similar solutions 17.4 × faster. It also provides 13.3% better solutions for the same time budget as the baseline.