Author: Denis Avetisyan
New observability tools are critical for understanding and optimizing the complex workflows driving the next generation of quantum-centric supercomputing applications.
This review details an observability architecture incorporating workflow metrics, telemetry, and algorithm-specific insights for hybrid quantum-classical systems, including Differential Evolution and Sampled Quantum Diagonalization.
Despite the promise of quantum computation, effectively monitoring and interpreting the behavior of hybrid classical-quantum workflows remains a significant challenge due to their probabilistic nature and remote execution. This paper, ‘Observability Architecture for Quantum-Centric Supercomputing Workflows’, proposes a novel system for decoupling telemetry collection from workload execution, enabling persistent monitoring and detailed data retention for reproducible analysis. Our architecture-demonstrated with a sample-based quantum diagonalization workflow-reveals critical solver behavior and supports infrastructure-aware algorithm design. Will this approach unlock systematic experimentation and accelerate the development of robust, high-performance quantum algorithms?
Beyond Brute Force: Hybrid Workflows for Quantum Systems
The simulation of many-body quantum systems – those where multiple particles interact according to the rules of quantum mechanics – presents a formidable challenge to classical computational methods. This difficulty arises because the computational resources required to accurately describe these systems scale exponentially with the number of particles involved. Consequently, even modestly sized systems quickly become intractable, hindering progress in fields like materials discovery, where predicting the properties of novel compounds relies on accurate simulations. Understanding the behavior of correlated electrons in complex materials, designing new high-temperature superconductors, and even accurately modeling chemical reactions are all limited by this fundamental bottleneck. The inability to efficiently simulate these systems restricts the predictive power of computational physics and chemistry, necessitating the development of alternative approaches to unlock deeper insights into the quantum world.
The computational challenge in simulating many-body quantum systems arises from the exponential growth of the Hilbert space – the complete set of all possible states a quantum system can occupy. As the number of particles increases, the dimensionality of this space expands at a rate that quickly overwhelms classical computers. For instance, a system of $n$ qubits requires storing and manipulating $2^n$ complex numbers, a task that becomes intractable even for modest values of $n$. This limitation restricts the size of systems researchers can accurately model, hindering progress in areas like materials science, drug discovery, and fundamental physics. Consequently, even with advancements in classical algorithms and hardware, simulating complex quantum phenomena remains a significant bottleneck, necessitating the exploration of alternative computational strategies.
The limitations of purely classical simulations for complex quantum systems are increasingly apparent, driving exploration of hybrid quantum-classical workflows. These approaches strategically partition computational tasks, assigning those best suited to quantum computers – such as simulating quantum dynamics or exploring vast Hilbert spaces – while relying on classical resources for pre- and post-processing, optimization, and data analysis. This synergy allows researchers to circumvent the exponential scaling challenges that plague traditional methods, effectively expanding the size and complexity of systems that can be investigated. By intelligently distributing the computational load, hybrid algorithms promise to unlock new avenues in materials discovery, drug design, and fundamental investigations of quantum phenomena, offering a pragmatic path toward realizing the full potential of quantum computation in the near term.
The Importance of Seeing the Machine: Observability in Hybrid Workflows
A decoupled Observability Architecture is critical for monitoring hybrid workflows due to the inherent complexity and distributed nature of these systems. This architecture separates the instrumentation of workflows – the generation of telemetry data – from the systems used for data collection, storage, and analysis. This decoupling enables persistent analysis of historical data, independent of active workflow execution, and supports proactive issue detection through anomaly detection and alerting. By avoiding tight coupling between workflows and observability tools, organizations can scale monitoring capabilities, adapt to changing infrastructure, and maintain reliable performance across complex, interconnected systems without impacting workflow execution or introducing single points of failure.
The Workflow Metrics Pyramid categorizes telemetry data based on its level of abstraction and the insights it provides. At its base are resource metrics, detailing hardware utilization like CPU and memory. Above this are task metrics, reflecting the performance of individual workflow tasks, including execution time and status. The next layer comprises workflow metrics, which aggregate task-level data to provide insights into overall workflow performance, such as completion rate and duration. Finally, at the apex of the pyramid are scientific outcomes, representing the high-level results and insights generated by the workflow, directly relating to the objectives of the data science or engineering process. This tiered structure enables a focused approach to monitoring, allowing teams to drill down from high-level outcomes to underlying infrastructure issues as needed.
Prefect serves as a workflow orchestration tool, enabling the definition, scheduling, and monitoring of data pipelines and complex workflows. It focuses on ensuring reliability and observability through features like state management, retries, and alerting. Complementing Prefect, Apache Superset provides a modern, cloud-native data exploration and visualization platform. Superset connects to various data sources, allowing users to create interactive dashboards and perform ad-hoc analysis of workflow metrics. The integration of Prefect and Superset facilitates real-time insights into workflow performance, enabling proactive identification of bottlenecks, failures, and opportunities for optimization. Specifically, metrics generated by Prefect workflows can be ingested into Superset for visualization and monitoring, providing a comprehensive view of data pipeline health and efficiency.
Decoding the Process: Metrics for Algorithm Optimization
Sample-Based Quantum Diagonalization (SQD) is a hybrid quantum-classical algorithm that leverages the strengths of both quantum processing units (QPUs) and high-performance computing (HPC) resources. Specifically, SQD utilizes quantum sampling performed on the IBM Heron R2 Processor to generate data used in a subsequent classical diagonalization process executed on the Miyabi Supercomputer. This approach combines the quantum computer’s ability to efficiently sample from a complex probability distribution with the classical computer’s robust capabilities for performing linear algebra and eigenvalue calculations, enabling the determination of ground state energies and other properties of quantum systems.
Hamming Distance, Sample Preservation Ratio, and Parameter Convergence serve as key performance indicators for assessing the progression of the Sample-Based Quantum Diagonalization (SQD) algorithm. The Hamming Distance, which quantifies the dissimilarity between states, achieved a final value of approximately 4.2 during testing. This metric, alongside the Sample Preservation Ratio and Parameter Convergence, provides granular insight into the efficiency and accuracy of the SQD process as it iterates towards a solution. Monitoring these indicators allows for data-driven optimization and validation of the algorithm’s performance characteristics.
Differential Evolution (DE) was implemented as the optimization algorithm for Sample-Based Quantum Diagonalization (SQD), functioning by iteratively refining parameters based on population differences. To monitor the DE process and assess its exploration of the solution space, the Carryover Acquisition metric was tracked; this value indicates the degree to which beneficial parameter changes from one generation are retained and contribute to improvements in subsequent generations. Higher Carryover Acquisition suggests effective exploration and exploitation of the parameter landscape, while low values may indicate stagnation or insufficient diversity in the population. The DE implementation, coupled with Carryover Acquisition monitoring, aimed to efficiently navigate the parameter space and converge towards optimal SQD configurations.
Efficient data transfer and computation within the Sample-Based Quantum Diagonalization (SQD) workflow necessitate Remote File System access on the Miyabi Supercomputer. Initial execution of the SQD workflow resulted in a calculated ground state energy of -326.71819 Hartree (Ha), followed by a second execution yielding -326.72307 Ha. This indicates computational stability and repeatability facilitated by the remote access infrastructure, allowing for large datasets generated by the IBM Heron R2 Processor to be efficiently processed on the HPC system.
Execution of quantum processing unit (QPU) jobs within the Sample-Based Quantum Diagonalization (SQD) workflow required approximately 5 minutes per job, utilizing 19.6% of the allocated QPU time. Corresponding high-performance computing (HPC) jobs took roughly 10 minutes to complete, consuming 7.7% of allocated tokens. Notably, the Sample Preservation Ratio, a metric indicating the retention of valid samples throughout the process, was measured at approximately 0.01, suggesting a low rate of sample preservation during execution.
Beyond the Simulation: Expanding the Horizon of Quantum Computation
The pursuit of simulating complex systems has been significantly advanced through the synergy of robust observability and hybrid quantum-classical workflows, notably Sequential Quantum Design (SQD). This integration allows researchers to not simply run a quantum simulation, but to deeply interrogate its processes – monitoring key metrics like entanglement fidelity and error rates in real-time. By providing a transparent window into the quantum computation, observability tools pinpoint performance bottlenecks and guide algorithmic optimization. This feedback loop is crucial for tackling systems previously intractable for quantum simulation, such as complex molecules or many-body physics problems, pushing the boundaries of what’s computationally feasible and paving the way for breakthroughs across diverse scientific disciplines. The result is a more efficient and reliable path towards leveraging quantum resources for practical applications.
The progression of quantum simulation relies heavily on meticulous performance analysis, as researchers are now implementing systematic tracking of key metrics to pinpoint computational bottlenecks. This data-driven approach moves beyond simple observation, allowing for iterative optimization of quantum algorithms and the refinement of hybrid quantum-classical workflows. By quantifying aspects such as circuit depth, gate fidelity, and qubit coherence times, scientists can directly correlate algorithmic choices with simulation efficiency. Consequently, this feedback loop not only accelerates the pace of scientific discovery in fields like materials science and drug design, but also facilitates the development of more robust and scalable quantum computing architectures, ultimately enabling the exploration of increasingly complex scientific problems previously considered intractable.
The principles underpinning scalable quantum simulation are proving remarkably versatile, extending far beyond the initial focus on materials science. Researchers are now applying these techniques to accelerate drug discovery by modeling molecular interactions with unprecedented accuracy, potentially identifying promising drug candidates and reducing development timelines. In fundamental physics, quantum simulations offer a pathway to explore phenomena inaccessible through traditional experimentation, such as the behavior of matter under extreme conditions or the properties of exotic particles. Moreover, the computational power unlocked by these advancements is also being leveraged in the realm of artificial intelligence, specifically in the development of novel machine learning algorithms and optimization techniques, promising breakthroughs in areas like pattern recognition and complex data analysis. This cross-disciplinary application demonstrates the broad potential of quantum simulation to revolutionize scientific inquiry across diverse fields.
The pursuit of reliable and scalable quantum simulations hinges on the continuous monitoring and detailed analysis of system performance. This isn’t simply about verifying a final result, but rather establishing a feedback loop throughout the computational process. Researchers are increasingly focused on tracking key metrics – such as qubit coherence, gate fidelity, and entanglement entropy – to proactively identify and mitigate errors. This data-driven approach allows for algorithmic refinement and hardware optimization, ultimately extending the scope of solvable problems. Beyond materials science, the benefits of this continuous improvement cycle are rippling through diverse fields; from accelerating drug discovery by modeling molecular interactions with unprecedented accuracy, to tackling fundamental questions in high-energy physics, and even inspiring novel approaches to machine learning. The ability to consistently validate and enhance quantum simulations is therefore not merely a technical achievement, but a catalyst for broader scientific innovation and technological advancement.
The pursuit of observability in quantum-centric supercomputing, as detailed in the article, isn’t merely a technical exercise; it’s an acknowledgement of inherent unpredictability. One might argue that attempting to fully know a quantum system is akin to chasing a shadow – the act of measurement inevitably alters the observed. As Paul Dirac so aptly put it, “I have not the slightest idea how it works.” This sentiment resonates with the core concept of the Workflow Metrics Pyramid, which attempts to distill complex system behavior into manageable, observable indicators. The architecture presented isn’t about eliminating uncertainty, but about refining the ‘rounding error’ between desired outcomes and the messy reality of quantum computation. It’s a pragmatic approach to understanding systems built on fundamentally probabilistic foundations.
What’s Next?
The pursuit of observability in quantum-centric supercomputing, as outlined in this work, is less a technical challenge than an exercise in applied psychology. The metrics pyramid, the telemetry streams – these are merely tools for translating the anxieties of algorithm designers into quantifiable data. Differential evolution and SQD, however elegant the mathematics, will always be subject to the biases of those who implement them. The true limitations aren’t in the hardware or the software, but in the human tendency to see patterns where none exist, and to cling to favored solutions despite evidence to the contrary.
Future work will inevitably focus on automating the interpretation of this observability data. But the danger lies in believing that an algorithm can objectively assess the ‘health’ of a quantum workflow. The system will reflect the values – and the fears – of its creators. A more fruitful avenue might be to explicitly model the cognitive biases of the developers themselves, creating a ‘bias observatory’ alongside the technical one.
Ultimately, the success of quantum-centric supercomputing won’t be measured in qubits or FLOPS, but in the degree to which the system can accommodate, and perhaps even mitigate, the inherent irrationality of its human operators. All behavior is a negotiation between fear and hope. Psychology explains more than equations ever will.
Original article: https://arxiv.org/pdf/2512.05484.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-08 07:17