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In the evolving field of quantum computing, optimizing Quantum Error Correction (QEC) parameters is crucial due to the varying types and amounts of physical noise across quantum computers. Traditional simulators use a forward paradigm to derive logical error rates from inputs like code distance and rounds, but this can lead to resource wastage. Adjusting QEC parameters manually with tools like STIM is often inefficient, especially given the daily fluctuations in quantum error rates. To address this, we introduce MITS, a reverse engineering tool for STIM that automatically determines optimal QEC settings based on a given quantum computer’s noise model and a target logical error rate. This approach minimizes qubit and gate usage by precisely matching the necessary logical error rate with the constraints of qubit numbers and gate fidelity. Our investigations into various heuristics and machine learning models for MITS show that XGBoost and Random Forest regressions, with Pearson correlation coefficients of 0.98 and 0.96, respectively, are highly effective in this context. 

Quantum error correction codes (QECCs) are essential for reliable quantum computing as they protect quantum states against noise and errors. Limited research has explored the resilience of QECCs to biased noise, critical for selecting optimal codes. We examine how different noise types impact QECCs, considering the varying susceptibility of quantum systems to specific errors. Our goal is to identify opportunities to minimize the resources—or overhead—needed for effective error correction. We conduct a detailed study on two QECCs—rotated and unrotated surface codes—under various noise models using simulations. Rotated surface codes generally perform better due to their simplicity and lower qubit overhead. They exceed the noise threshold of current quantum processors, making them more effective at lower error rates. This study highlights a hierarchy in surface code implementation based on resource demand, consistently observed across both code types. Our analysis ranks the code-capacity model as the most pessimistic and the circuit-level model as the most realistic, mapping error thresholds that show surface code advantages. Additionally, higher code distances improve performance without excessively increasing qubit overhead. Tailoring surface codes to align with the target logical error rate and the biased physical error profile is crucial for optimizing reliability and resource use. 

Quantum error correction (QEC) plays a crucial role in correcting noise and paving the way for fault-tolerant quantum computing. This field has seen significant advancements, with new quantum error correction codes emerging regularly to address errors effectively. Among these, topological codes, particularly surface codes, stand out for their low error thresholds and feasibility for implementation in large-scale quantum computers. However, these codes are restricted to encoding a single qubit. Lattice surgery is crucial for enabling interactions among multiple encoded qubits or between the lattices of a surface code, ensuring that its sophisticated error-correcting features are maintained without significantly increasing the operational overhead. Lattice surgery is pivotal for scaling QECCs across more extensive quantum systems. Despite its critical importance, comprehending lattice surgery is challenging due to its inherent complexity, demanding a deep understanding of intricate quantum physics and mathematical concepts. This paper endeavors to demystify lattice surgery, making it accessible to those without a profound background in quantum physics or mathematics. This work explores surface codes, introduces the basics of lattice surgery, and demonstrates its application in building quantum gates and emulating multi-qubit circuits. 

Security and reliability are primary concerns in any computing paradigm, including quantum computing. Currently, users can access quantum computers through a cloud-based platform where they can run their programs on a suite of quantum computers. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect that more companies including untrusted/less-trusted/unreliable vendors will begin offering quantum computers as hardware-as-a-service at varied price/performance points. Since computing time on quantum hardware is expensive and the access queue could be long, the users will be motivated to use the cheaper and readily available but unreliable/less-trusted hardware. The less-trusted vendors can tamper with the results, providing a sub-optimal solution to the user. For applications such as, critical infrastructure optimization, the inferior solution may have significant socio-political implications. Since quantum computers cannot be simulated in classical computers, users have no way of verifying the computation outcome. In this paper, we address this challenge by modeling adversarial tampering and simulating it's impact on both pure quantum and hybrid quantum-classical workloads. To achieve trustworthy computing in a mixed environment of trusted and untrusted hardware, we propose an equitable distribution of total shots (i.e., repeated executions of quantum programs) across hardware options. On average, we note ≈ 30X and ≈ 1.5X improvement across the pure quantum workloads and a maximum improvement of ≈ 5X for hybrid-classical algorithm in the chosen quality metrics. We also propose an intelligent run adaptive shot distribution heuristic leveraging temporal variation in hardware quality to user's advantage, allowing them to identify tampered/untrustworthy hardware at runtime and allocate more number of shots to the reliable hardware, which results in a maximum improvement of ≈ 190X and ≈ 9X across the pure quantum workloads and an improvement of up to ≈ 2.5X for hybrid-classical algorithm.