The Department of the Air Force (DAF) and the U.S. Space Force (USSF) are seeking innovative solutions through the Small Business Innovation Research (SBIR) program to enhance logistics planning and optimization in space environments. The primary objective is to explore the feasibility of applying Quantum Machine Learning (QML) and Quantum Optimization (QO) techniques to improve demand forecasting, routing, and scheduling for logistics operations across terrestrial and space domains. This initiative is crucial for addressing the complexities of modern logistics, particularly as missions expand to include rapid orbital resupply and interplanetary operations. Interested parties should note that the solicitation is set to open on July 23, 2025, with applications due by August 20, 2025, and further details can be found at the provided source link.

## Description

The Department of the Air Force (DAF) and USSF face escalating complexity in sustaining agile, resilient logistics across terrestrial, orbital, and emerging space environments. Existing logistics planning systems are overly manual, computationally limited, and poorly suited for dynamic, contested scenarios. As logistics demands expand to include rapid orbital resupply, lunar outposts, and interplanetary assets, there is an urgent need for advanced decision-support tools. Hybrid quantum-classical computing approaches&mdash;including QML and QO&mdash;have demonstrated early promise in tackling combinatorially complex logistics challenges, such as:- Reducing compute time by orders of magnitude for vehicle routing problems (VRP), job scheduling, and inventory optimization.- Enabling real-time planning adaptations under operational stressors and adversarial disruptions. - Scaling across terrestrial, orbital, lunar, and interplanetary logistics architectures. Core capability areas to be explored in this effort include:<ol><li> Quantum-Enhanced Demand Forecasting: Leveraging QML to model operational and environmental data, enabling superior responsiveness in uncertain and fast-changing mission environments.</li><li> Hybrid Quantum Optimization for Routing &amp; Scheduling: Applying quantum algorithms&mdash;such as quantum approximate optimization algorithms (QAOA) and quantum annealing&mdash;to solve NP-hard logistics problems and benchmarking them against classical methods.</li><li> Mission-Aware, Real-Time Logistics Planning: Integrating real-time data (e.g., weather, adversary actions, orbital mechanics) into optimization engines for continuous re-planning and mission adaptation.</li><li> Resilient, Contested Logistics Management: Modeling logistics risk under degraded conditions and exploring blockchain-enabled logistics chains for verifiability and control in adversarial domains.</li><li> Dual-Use and Digital Integration: Aligning with Logistics 4.0 transformation goals by enabling interoperability with DoD digital twins, Artificial Intelligence (AI)-enabled mobility planning tools, and autonomous logistics systems.</li></ol>Phase I will explore technical feasibility, prototype hybrid algorithmic models, and establish performance baselines. This effort directly supports the USSF Mission Sustainment Strategy by advancing decision advantage, predictive planning, and mission survivability through next-generation logistics computing.

## Objective

The goal of this effort is to establish the technical feasibility of applying Quantum Machine Learning (QML) and Quantum Optimization (QO) to space logistics planning, demand forecasting, and mobility optimization. This effort will develop and benchmark a hybrid quantum-classical algorithm for a representative logistics scenario, assessing performance improvements over classical models. The Phase I goal is to validate the potential of QML/QO to enhance decision speed, adaptability, and mission resilience in dynamic, contested environments&mdash;laying the foundation for a scalable platform to be developed in Phase II. This aligns with the U.S. Space Force (USSF) Mission Sustainment Strategy and U.S. Department of Defense (DoD) objectives for digital logistics modernization and survivable supply chain operations.

## Phase I

The objective for Phase I is to establish the technical feasibility and operational relevance of QML and QO for enhancing space logistics planning and demand forecasting. Phase I will focus on developing and evaluating a hybrid quantum-classical approach to solve logistics challenges such as dynamic routing, scheduling, or inventory management in space and contested terrestrial environments. Key tasks include:- Develop a prototype algorithm or simulation that applies QML or QO techniques to a relevant logistics use case (e.g., vehicle routing, resupply scheduling, or demand forecasting).- Construct a hybrid quantum-classical model, utilizing Quadratic Unconstrained Binary Optimization (QUBO) formulations, classical decomposition strategies, and variational quantum algorithms where applicable.- Benchmark the quantum-enabled solution against traditional classical optimization models in terms of accuracy, computational efficiency, scalability, and adaptability under uncertainty.- Engage logistics stakeholders (USSF, Space Systems Command (SSC), U.S. Transportation Command (USTRANSCOM), Air Mobility Command (AMC), Defense Logistics Agency (DLA) to validate use case relevance and gather mission-specific planning requirements.Deliverables may include:- A working prototype simulation or algorithm test case demonstrating the selected quantum technique. - A comparative analysis report detailing performance trade-offs between quantum and classical approaches. - A stakeholder engagement report summarizing feedback, mission alignment, and integration opportunities. - A Phase II roadmap outlining pathways for algorithm maturation, scalability improvements, and field validation in real-world operational environments. 

## Phase II

The Phase II objective is to design, develop, and deploy a fully functional, scalable quantum-enhanced logistics optimization platform using QML and QO. This system will integrate with operational logistics architectures from USSF, U.S. Air Force (USAF), and USTRANSCOM to enable real-time, resilient, and predictive decision-making for space and terrestrial sustainment operations. Key focus areas include:- Platform Development: Build a full-stack hybrid quantum-classical optimization engine, leveraging QUBO formulations, quantum annealing, and/or variational quantum algorithms to solve NP-hard logistics problems at scale.- System Integration: Develop secure and interoperable APIs to connect the platform with existing DoD logistics systems, digital twin architectures, and planning tools (e.g., Spaceport Common Operating Picture, AI-based cargo management platforms).- Operational Validation: Apply the platform to mission-relevant use cases such as dynamic routing and scheduling under degraded or adversarial conditions, real-time resupply logistics and campaign planning, and mobility as well as sustainment optimization across orbital, lunar, and terrestrial nodes. - Human-Machine Interface: Design an intuitive user interface for planners, analysts, and logisticians, enabling transparent interaction with quantum-driven outputs.- Performance Benchmarking: Conduct comparative analysis of platform performance versus classical optimization solutions in representative mission environments.Deliverables may include:- A scalable QML/QO optimization prototype with web or edge deployment capability.- Integration interfaces (APIs) for real-time data exchange with DoD systems and digital twins. - Operational test results and benchmarking report, demonstrating improvements in speed, accuracy, and mission adaptability. - A detailed Phase III transition plan, outlining technical, programmatic, and commercial pathways for full deployment. 

## Phase III: Dual Use Applications

For Phase III, the quantum-enabled logistics optimization platform will support high-priority defense missions requiring resilient, adaptive, and real-time logistics decision-making in contested and dynamic environments. Key applications include:- Dynamic and Survivable Space Logistics: For USSF, AMC, Space Operations Command (SpOC), and SSC, enabling real-time resupply and routing under adversarial conditions.- Mission Planning in Great Power Competition: Support logistics strategy and sustainment under threat conditions, denial-of-service environments, and complex global mobility scenarios.- Agile Combat Employment (ACE) and Rapid Global Mobility: Enhance deployment timelines, asset utilization, and supply chain resilience across air and space domains.The platform will also serve multiple high-demand commercial sectors facing complex, dynamic logistics challenges, including:- Space Cargo and Orbital Routing: Enable predictive cargo planning for commercial spaceports, launch providers, and satellite resupply networks.- Global Supply Chain Optimization: Serve aerospace, maritime, and automotive firms by improving cost-efficiency, resilience, and routing under uncertainty.- AI-Quantum Logistics Tools: Provide a new class of logistics planning products that combine AI prediction with quantum optimization&mdash;ideal for logistics automation, risk forecasting, and multi-modal scheduling in large-scale networks.The target Technology Readiness Level (TRL) is TRL 7, 8, or 9 achieved through successful operational prototype demonstration, integration into live mission environments, and commercial product adaptation. For the transition strategy, consider a DoD transition path by working directly with SSC, USTRANSCOM, and logistics operators to embed the platform into operational workflows and digital twin environments and a commercialization path partnering with space logistics firms, global supply chain integrators, and quantum computing vendors to deploy the platform as a commercial software as a service (SaaS) or edge-integrated tool.

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