An HPCQC-Tailored Approach for Scalable Measurement of Physical Observables

As we make strides toward the era of quantum utility with improved hardware and defined practical use cases, a primary challenge resides in the definition of robust implementation architectures that enable efficient utilization of quantum accelerators. To truly unlock the capabilities of Quantum Processing Units (QPUs), basic classical support must be upgraded to a high-performance, efficient infrastructure. On the application level, integration translates to defining coherent workflows that enable optimized implementations, while abstracting complexity for larger tasks. Accordingly, a common primitive in hybrid applications is the evaluation of a physical system's observable quantities. Leveraging quantum hardware for this task requires decomposing osbervable-derived operators into vast arrays of Pauli terms, necessitating a high volume of distinct circuit executions that currently suffer from non-optimized management.

This work proposes a novel, scalable procedural architecture that establishes observable measurement as a first-class citizen of the High-Performance Computing - Quantum Computing (HPCQC) stack. We introduce a hierarchical runtime environment featuring a workload manager for resource-aware scheduling and local quantum and classical workers for low-latency, device-specific interactions. Such infrastructure enables a pipeline of advanced optimizations, including, but not restricted to, commutativity-based operator grouping, adaptive shot allocation for variance-aware load balancing, and stateful circuit caching to minimize compilation overhead. By automating the orchestration of measuring non-commuting terms across heterogeneous backends, we demonstrate a pathway to significantly reduce time-to-solution and improve accuracy. This procedure bridges the gap between theoretical quantum algorithms and practical, scalable HPC deployment.

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Contributors:

• Martin Schulz