Predictive Quantum Circuit Preparation: Forecasting Future Quantum Workloads and Proactively Preparing Quantum Computational Resources

The increasing development of quantum computing has introduced new computational challenges involving quantum circuit compilation, hardware-specific qubit mapping, gate decomposition, routing optimization, and the adaptation of quantum programs to changing quantum processor conditions. Although modern quantum computing platforms provide increasingly sophisticated compilation and execution services, the preparation of quantum circuits remains a potentially significant source of computational overhead. Conventional quantum computing workflows generally initiate circuit analysis, compilation, hardware mapping, and execution preparation after a user submits a computational task. Consequently, when multiple users submit related or structurally similar quantum workloads, certain preparation operations may be repeatedly performed even when their future requirements could potentially have been anticipated. This paper introduces Predictive Quantum Circuit Preparation (PQCP), a conceptual framework that investigates whether future quantum computational workloads can be forecast and their associated circuit preparation operations selectively completed before actual execution requests arrive. The proposed framework integrates quantum workload forecasting, future circuit structure prediction, circuit-family identification, proactive circuit analysis, hardware-aware gate decomposition, predictive qubit mapping, routing preparation, calibration-aware compatibility verification, and adaptive preparation scheduling. Rather than predicting quantum measurement outcomes or assuming that future quantum states can be generated in advance, PQCP focuses on preparing reusable classical representations and execution configurations associated with anticipated quantum circuits. Its architecture distinguishes between reusable circuit-independent preparation, circuit-structure-dependent compilation, hardware-specific execution preparation, and time-sensitive calibration-dependent optimization. A unified optimization formulation is developed to estimate the expected benefits of advance preparation while accounting for prediction uncertainty, compilation expenditure, hardware compatibility, calibration changes, memory requirements, preparation deadlines, and the probability of future workload reuse. The framework further investigates whether preparation during periods of relatively low computational demand can reduce post-submission preparation latency and improve quantum computing service throughput without interfering with currently executing tasks. An experimental methodology is established to evaluate circuit compilation time, qubit mapping time, routing overhead, circuit depth, gate count, preparation reuse rate, hardware compatibility, execution fidelity, and total computational expenditure. Particular attention is given to the distinction between reducing classical circuit preparation time and improving the physical execution quality of quantum circuits. The proposed framework does not assume that advance preparation is universally beneficial, since quantum hardware conditions may change and speculative preparation may produce unused or outdated results. Instead, it establishes a research foundation for determining when predicted quantum workloads provide sufficient opportunities for proactive preparation and when prepared computational resources remain valid for subsequent execution. By extending predictive computing into quantum circuit compilation and hardware adaptation, this research investigates a transition from exclusively reactive quantum task preparation toward predictive quantum computational preparation. Its practical effectiveness remains subject to empirical validation.