Real-time healthcare workforce scheduling represents a critical optimization challenge with direct implications for patient care quality and operational efficiency. This article introduces a quantum computing approach to dynamic hospital staff rescheduling that addresses the complex constraints of medical workforce allocation when facing unexpected absences. By formulating the problem as a quadratic unconstrained binary optimization (QUBO) model suitable for quantum processing, demonstrate how quantum annealing and gate-based quantum algorithms can rapidly identify optimal staff substitutions while balancing factors such as specialist qualifications, shift duration, fatigue management, and geographical proximity. The simulation results indicate that quantum-accelerated scheduling can significantly reduce the average time to fill critical absences compared to classical heuristic methods while maintaining high adherence to staffing quality metrics. This has substantial potential to mitigate the downstream clinical impacts of staffing disruptions, particularly in high-acuity hospital departments where timely specialist presence is essential for patient outcomes.
Real-time healthcare workforce scheduling represents a critical optimization challenge with direct implications for patient care quality and operational efficiency. This article introduces a quantum computing approach to dynamic hospital staff rescheduling that addresses the complex constraints of medical workforce allocation when facing unexpected absences. By formulating the problem as a quadratic unconstrained binary optimization (QUBO) model suitable for quantum processing, demonstrate how quantum annealing and gate-based quantum algorithms can rapidly identify optimal staff substitutions while balancing factors such as specialist qualifications, shift duration, fatigue management, and geographical proximity. The simulation results indicate that quantum-accelerated scheduling can significantly reduce the average time to fill critical absences compared to classical heuristic methods while maintaining high adherence to staffing quality metrics. This has substantial potential to mitigate the downstream clinical impacts of staffing disruptions, particularly in high-acuity hospital departments where timely specialist presence is essential for patient outcomes.
Scheduling medical staff in healthcare institutions is a complex and critical task that requires balancing operational constraints, personnel availability, quality of care, and staff well-being. These interdependent factors make the creation of optimal schedules particularly challenging necessitating sophisticated approaches that effectively reconcile institutional needs and medical staff expectations. This project explores an innovative alternative approach: quantum optimization. By formulating the scheduling problem as a QUBO (Quadratic Unconstrained Binary Optimization) model, we aim to leverage the capabilities of quantum algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA) This framework allows constraints and objectives to be reformulated as quadratic functions, facilitating their integration into Ising models and their resolution on quantum gate_based circuit systems.To this end, we implement a hybrid quantum-classical solution using QAOA on IBM’s Qiskit platform. We analyze QAOA’s performance on problem instances of up to 27 qubits, and compare it against classical solvers such as CPLEX. Through this comparative approach, we assess the practical viability of quantum optimization in medical staff scheduling and contribute to the broader investigation of quantum computing in operational planning within healthcare systems.
This paper presents a quantum-inspired optimization scheme for patient allocation, aimed at the expedient and efficient distribution of medical resources. The objective is to minimize patients' waiting times and optimize the usage of available medical capacity by allocating patients to the most appropriate hospitals based on the severity of their injuries. This allocation problem is situated within the urban context, seeking to minimize the distance between patients and hospitals. Traditionally, solving such large-scale allocation problems in expansive urban areas poses significant computational challenges. However, by incorporating quantum-inspired methods, we model this problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, which is solved using a digital annealer. We conducted an experimental study on the digital annealing device and compared its performance with that of a conventional simulated annealing algorithm. The results demonstrate that our proposed digital annealing approach to patient allocation offers superior performance and effectiveness.
This paper presents a hybrid quantum-classical framework for real-time hospital operations that addresses three routine sources of delay and risk: operating room (OR) rescheduling under disruptions, dynamic ICU/ED staffing under shocks, and MRI throughput with quality control. The approach integrates shallow, latency-bounded quantum optimization within established scheduling pipelines to accelerate diversified search over combinatorial decision spaces under strict deadlines, while classical solvers enforce feasibility, safety, and deterministic fallbacks. In parallel, sensing-guided MRI control provides realtime field/SNR characterization that drives adaptive acquisition and stabilizes AI reconstruction, reducing rescans without compromising diagnostic integrity. The system comprises: (1) a realtime OR rescheduling module that preserves credentialing, safety, and resource constraints; (2) a dynamic ICU/ED assignment module that maintains safe coverage and fairness with acuityand skill-mix awareness; and (3) an MRI module that couples on-scanner quality control with physics-consistent reconstruction to shorten scan times. Evaluation emphasizes wall-clock latency, constraint satisfaction, utilization, and image-quality indices-metrics aligned to patient access, safety, and operational resilience. Results indicate that shallow hybrid quantum methods, when fused with robust classical controllers and sensing feedback, can deliver reliable, incremental improvements under real-time hospital conditions, while providing auditable guardrails and failsafe rollbacks suitable for near-term deployment.
Recent advancements in quantum computing technology have heralded a new era of computational capabilities, outpacing classical simulations in solving complex problems. This remarkable progress has sparked a burgeoning interest in quantum algorithms across various sectors, with optimization emerging as a particularly vibrant field of study. A focal point of this field is the development and refinement of optimization algorithms that are suitable for current and emerging capability of quantum computers. These advancements are crucial as they offer practical ways to apply quantum computing to real-world challenges. Among these applications, personnel and task execution scheduling is a compelling use case for quantum computing as applied to solve complex optimization problems critical to business and industrial operations. In this paper, we explore the utilization of quantum computers to address this class of optimization problems. Employing a range of advanced techniques, including a novel method that improves efficiency, our method demonstrates the capability of quantum computing to provide effective solutions for intricate optimization problems. We particularly focus on the application of IBM's 127 -qubit quantum devices to manage scheduling tasks in workforce management, involving 500 to 874 binary decision variables and over 1000 constraints. This exploration serves not only as a proof of concept for the practical applications of quantum computing but also as a testament to its evolving role in solving business-critical optimization challenges.
As systems become more complex and large-scale, there is a need for more effective scheduling algorithms to maximize resource usage and minimize job completion times. The older methods of scheduling like First-Come-First-Serve (FCFS) and Round Robin (RR), although easier to compute, tend to perform poorly in dynamic scenarios because they cannot manage intricate constraints and multi-objective optimization efficiently. As traditional methods approach their limit of performance, quantum computing presents a potentially fruitful avenue for conquering combinatorial challenges based on its ability to examine many states at once. This work proposes a quantum-aided scheduling framework that employs Quadratic Unconstrained Binary Optimization (QUBO) formulations and quantum solvers for the purpose of improving job scheduling in distributed environments. The original contribution is the marrying of quantum optimization (with IBM Qiskit's QAOA and D-Wave's hybrid solvers) with traditional distributed computing frameworks such as Apache Spark. Google Cluster Workload Traces are employed to mimic real-world situations, and jobs are described by CPU, memory requirements, and arrival times. Once the scheduling problem has been formulated into a QUBO model, quantum algorithms produce the optimized job-to-machine assignments. The outcomes are compared against baseline classical schedulers on performance metrics including make span, throughput, and resource utilization. The experiment demonstrates that quantum-assisted scheduling significantly improves makespan and enhances resource utilization, particularly under high workloads. It also provides evidence of the feasibility of integrating quantum solvers with existing big data infrastructures.
Quantum annealing (QA) is a metaheuristic methodologythat has recently been used to analyze the performance of first generation adiabatic quantum processors, like the oneavailable from D-wave Systems, in solving combinatorial optimization problems. In this paper, we formulate QA representation of problems essential to mitigating the effects of the COVID-19 pandemic, namely, the Nurse Scheduling Problem (NSP), the Physician Scheduling Problem (PSP), and the Nurse-Physician Scheduling Problem (NPSP). The proposed objective functions for each problem are scripted to the Ising model and then transformed into thequadratic unconstrained binary optimization (QUBO)format. An optimal solutionis soughtfor a set of constraints, including thenumber ofnurses or physicians per shift andthe maximum number of shifts for each nurse or physician.After reducingtheproposed NSP, PSP, and NPSP QUBOs to a novel Ising type Hamiltonian, we obtain solutions from a classical (simulated) annealer,and then from the D-wave 2000Q forward and reverse annealer. Results from the reverse quantum annealer show a dramatic improvement in solution quality as compared to those from the classical annealer. We observe that the reverse annealing method gives us the most satisfactory output from among three heuristic processes used, for each scheduling problem.