As quantum computing transitions from theoretical research to practical deployment, a compatibility gap emerges between classical AI software stacks and probabilistic quantum hardware. Existing quantum machine learning frameworks operate at the application level and lack system-level abstractions for hardware adaptation, workload routing, and dynamic code generation. This paper presents UniMind, a middleware framework designed to reduce the abstraction gap between classical AI workloads and heterogeneous quantum-classical computing backends. UniMind introduces two components: (1) an LLM-driven orchestration layer that interprets high-level task specifications and generates topology-aware execution plans through constrained sampling, operating exclusively in user space with sandboxed validation and rule-based fallback; and (2) Unibit, a classical preprocessing representation based on sliding-window frequency estimation and sinc-based smoothing that produces parameters for standard quantum angle encoding. We implement a multi-backend mapping engine and validate the framework with reproducible experiments.
As quantum computing transitions from theoretical research to practical deployment, a compatibility gap emerges between classical AI software stacks and probabilistic quantum hardware. Existing quantum machine learning frameworks operate at the application level and lack system-level abstractions for hardware adaptation, workload routing, and dynamic code generation. This paper presents UniMind, a middleware framework designed to reduce the abstraction gap between classical AI workloads and heterogeneous quantum-classical computing backends. UniMind introduces two components: (1) an LLM-driven orchestration layer that interprets high-level task specifications and generates topology-aware execution plans through constrained sampling, operating exclusively in user space with sandboxed validation and rule-based fallback; and (2) Unibit, a classical preprocessing representation based on sliding-window frequency estimation and sinc-based smoothing that produces parameters for standard quantum angle encoding. We implement a multi-backend mapping engine and validate the framework with reproducible experiments.
UniMind is a user-space middleware for quantum-accelerated AI workloads that scores, places, executes, and refreshes calibration data in a closed loop. This record contains the v7 manuscript in two editions (12-page full and 6-page compact), each as compiled PDF and LaTeX source. The paper is a measurement study of how a quantum runtime should maintain scheduling decisions when hardware calibration changes over time. We instrument a 156-qubit superconducting device (ibm_marrakesh) across three primary daily snapshots (D0-D2, 2026-08-29 to 08-31) plus 34 hourly calibration snapshots (2026-09-01 to 09-02, three backends: marrakesh, kingston, fez). Four findings: (1) calibration validity is temporally bounded and small (day-over-day rank correlation 0.579, top-10 Jaccard 0.25, stale top-3 pins 1.94-2.1x worse across 24-34 h; hourly top-3 margins ~1e-3 vs median |dC| = 0.0127); we formalize the Calibration Validity Horizon T_valid as a lower-bound design primitive. (2) Adaptive refresh (top-10 Jaccard < 0.5 or dC > 0.005, ~0.04 ms per scoring pass) dominates static and periodic policies at near-zero cost; a 144-threshold sweep places the default at the Pareto knee. (3) Refresh, not placement selection, dominates end-to-end reliability at the measured scale (+33/37 pp with fresh pins; pinned vs free is a bounded null, 70.0% vs 76.7%, Fisher p = 0.77, MDE ~ 32 pp). (4) Proxy-trained layout models do not transfer: re-fitting the 7-dim utility J(G) on real device labels collapses its size out-of-sample Spearman from 0.818 to 0.13; feature ablation and a connectivity-vs-quality variance crossover at k ~ 100 isolate the mechanism. All experiments are open-source and reproducible from the companion repository; QPU job IDs and calibration snapshots are recorded in its data directory. Every claim traces to a recorded job or snapshot; a failed exploratory result (rank correlation 1.000 -> 0.771) is reported as unreplicated. The LLM-orchestration layer is a separable controlled-parameter reliability model, not a hardware claim.
The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) is reshaping computational paradigms, creating the need for a new class of system software capable of managing hybrid workloads. This paper presents Q-OS, a Quantum-Aware Operating System designed to orchestrate hybrid quantum-classical AI pipelines across cloud and edge environments. Q-OS introduces three novel system components: a Quantum Scheduling Kernel that intelligently allocates portions of AI workloads (such as optimization or sampling) to quantum co-processors; a Quantum Resource Virtualization Layer that abstracts quantum devices as logical accelerators; and a Quantum-AI Compiler Interface that dynamically maps AI operations from frameworks such as TensorFlow and PyTorch into executable quantum kernels. Experimental evaluations using hybrid workloads-including large language model inference (The Pile dataset), quantum-classical generative adversarial networks (CelebA dataset), and reinforcement learning control (OpenAI Gym)-demonstrate that Q-OS achieves up to 32% reduction in end-to-end latency and 27 % improvement in energy efficiency compared to classical distributed systems. The results indicate that Q-OS establishes a scalable foundation for the next generation of intelligent computing infrastructures, bridging quantum and AI ecosystems for real-world deployment.
The integration of large language models (LLMs) into scientific research is accelerating the realization of autonomous ``AI Scientists.'' While recent advancements have empowered AI to formulate hypotheses and design experiments, a critical gap remains in the execution of these tasks, particularly in the domain of quantum computing (QC). Executing quantum algorithms requires not only generating code but also managing complex computational resources such as QPUs and high-performance computing (HPC) clusters. In this paper, we propose an AI-driven framework specifically designed to bridge this execution gap through the implementation of a Model Context Protocol (MCP) server. Our system enables an LLM agent to process natural language prompts submitted as part of a job, autonomously executing quantum computing workflows by invoking our tools via the MCP. We demonstrate the framework's capability by performing essential quantum algorithmic primitives, including sampling and computation of expectation values. Key technical contributions include the development of an MCP server for quantum execution, a pipeline for interpreting OpenQASM code, an automated workflow with CUDA-Q for the ABCI-Q hybrid platform, and an asynchronous execution pipeline for remote quantum hardware using the Quantinuum emulator via CUDA-Q. This work validates that AI agents can effectively abstract the complexities of hardware interaction through an MCP-based architecture, thereby facilitating the automation of practical quantum research.
As quantum hardware advances, integrating quantum processing units (QPUs) into HPC environments and managing diverse infrastructure and software stacks becomes increasingly essential. Pilot-Quantum addresses these challenges as a middleware designed to provide unified application-level management of resources and workloads across hybrid quantum-classical environments. It is built on a rigorous analysis of existing quantum middleware systems and application execution patterns. It implements the Pilot Abstraction conceptual model, originally developed for HPC, to manage resources, workloads, and tasks. It is designed for quantum applications that rely on task parallelism, including (i) hybrid algorithms, such as variational approaches, and (ii) circuit cutting systems, used to partition and execute large quantum circuits. Pilot-Quantum facilitates seamless integration of QPUs, classical CPUs, and GPUs, while supporting high-level programming frameworks like Qiskit and Pennylane. This enables users to efficiently design and execute hybrid workflows across diverse computing resources. The capabilities of Pilot-Quantum are demonstrated through mini-apps -- simplified yet representative kernels focusing on critical performance bottlenecks. We demonstrate the capabilities of Pilot-Quantum through multiple mini-apps, including different circuit executions (e.g., using IBMś Eagle QPU and simulators), circuit cutting, and quantum machine learning scenarios.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum-classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum–classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum–classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum–classical HPC ecosystems.