In order to realize large scale quantum error correction (QEC), resource states, such as |T〉, must be prepared which is expensive in both space and time. In order to circumvent this problem, alternatives have been proposed, such as the production of continuous angle rotation states [1, 6, 34]. However, the production of these states is non-deterministic and may require multiple repetitions to succeed. The original proposals suggest architectures which do not account for realtime (or dynamic) management of resources to minimize total execution time. Without a realtime scheduler, a statically generated schedule will be unnecessarily expensive. We propose RESCQ (pronounced rescue), a realtime scheduler for programs compiled onto these continuous angle systems. Our scheme actively minimizes total cycle count by on-demand redistribution of resources based on expected production rates. Depending on the underlying hardware, this can cause excessive classical control overhead. We further address this by dynamically selecting the frequency of our recomputation. RESCQ improves over baseline proposals by an average of 2x in cycle count.

3-D DRAM provides a significant performance boost resulting from substantial memory bandwidth. However, the stacked memory architecture exhibits high power density, causing thermal hotspots. Further, systems under power constraints require careful planning for intelligent allocation of the available power to their various components. A straightforward dynamic power management policy of allocating more power to potentially high memory activity 3-D DRAM ranks so as to maximize system performance causes a rise in the temperature of such ranks, making them susceptible to thermal stalls and shutdown by dynamic thermal management (DTM) strategies. A rise in rank temperature, in turn, increases the leakage power of memory ranks, affecting power budgeting decisions. Thus, a coordinated strategy for power budgeting and thermal management is needed. We propose an adjacency-aware dynamic power budgeting technique, 3D-TemPo, which dynamically performs a reward-based power allocation to memory ranks, in order to maximize 3-D DRAM performance under power and thermal constraints, and is sensitive to strong thermal correlations between vertically adjacent ranks. We evaluate 3D-TemPo using SPEC CPU2017 and PARSEC 2.1 benchmark suites and observe speedups of$1\times $to$17.94\times $compared to baseline strategies.