A weak-coupling approximation reduces classical overhead in quantum circuit knitting to polynomial cost when one qubit couples weakly to others, shown on QAOA-style layered circuits.
Solving large-scale vehicle routing problems with hybrid quantum-classical decomposition,
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Hierarchical clustered decomposition plus local repair lets standard QAOA solve 13-node two-vehicle VRP instances using 12 qubits per subproblem with approximation ratios 1.2-1.5 versus Gurobi.
ITAS, a multi-agent tutoring system with quantum-specialized LLM agents, cloud infrastructure, and analytics, was deployed in a real quantum computing course and provided evidence that agent specialization improves reliability while surfacing curriculum gaps.
Introduces versioned late materialization to eliminate data redundancy in ultra-long sequence training for DLRMs by storing histories once and reconstructing via pointers at training time.
D-QEO framework uses quantum topographical preconditioning on separable functions via small parallel subcircuits to generate seeds that accelerate classical global optimization and avoid exponential failure rates.
citing papers explorer
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Scalable quantum circuit knitting using a weak-coupling approximation
A weak-coupling approximation reduces classical overhead in quantum circuit knitting to polynomial cost when one qubit couples weakly to others, shown on QAOA-style layered circuits.
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Hierarchical QAOA for the Vehicle Routing Problem via Clustered Decomposition and Local Feasibility Repair
Hierarchical clustered decomposition plus local repair lets standard QAOA solve 13-node two-vehicle VRP instances using 12 qubits per subproblem with approximation ratios 1.2-1.5 versus Gurobi.
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From Prototype to Classroom: An Intelligent Tutoring System for Quantum Education
ITAS, a multi-agent tutoring system with quantum-specialized LLM agents, cloud infrastructure, and analytics, was deployed in a real quantum computing course and provided evidence that agent specialization improves reliability while surfacing curriculum gaps.
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Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
Introduces versioned late materialization to eliminate data redundancy in ultra-long sequence training for DLRMs by storing histories once and reconstructing via pointers at training time.
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Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search
D-QEO framework uses quantum topographical preconditioning on separable functions via small parallel subcircuits to generate seeds that accelerate classical global optimization and avoid exponential failure rates.