Constructs and proves correct a QUBO Hamiltonian H_mod,k whose zero-energy ground states exist exactly when a graph admits a nowhere-zero Z_k-flow, with degeneracy matching the flow polynomial.
A quantum approximate optimization algorithm
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ATHENA compiler uses UMS lookahead and EES early scheduling to reduce teleportations by 34% and latency by 2x on average versus prior block-based approaches in distributed quantum computers.
A gradient estimator for Lie-symmetric PQCs expresses the gradient as a linear combination of Hadamard-test expectations whose coefficients are estimated via shadow tomography, yielding logarithmic shot scaling.
An LLM-guided scout–promote–confirm loop discovers adaptive variational quantum policies that beat static baselines on MIS and CVRP while exposing proxy-ranking failures.
A framework consisting of 13 benchmark families is introduced to enable application-level benchmarking of quantum systems across metrics including solution quality, total execution time, energy consumption, and time-to-solution.
Parallel HADOF execution on up to four IBM QPUs achieves 3-4x wall-clock speedup for combinatorial QUBO problems versus sequential runs, with comparable quality and validation on genome assembly instances.
A review synthesizing foundations, constructions, advantage conditions, and challenges for non-variational quantum kernel methods in supervised learning.
citing papers explorer
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A QUBO Formulation for Nowhere-Zero $k$-Flows
Constructs and proves correct a QUBO Hamiltonian H_mod,k whose zero-energy ground states exist exactly when a graph admits a nowhere-zero Z_k-flow, with degeneracy matching the flow polynomial.
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ATHENA: A Compiler For Optimized Scheduling In Distributed Quantum Computers
ATHENA compiler uses UMS lookahead and EES early scheduling to reduce teleportations by 34% and latency by 2x on average versus prior block-based approaches in distributed quantum computers.
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Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries
A gradient estimator for Lie-symmetric PQCs expresses the gradient as a linear combination of Hadamard-test expectations whose coefficients are estimated via shadow tomography, yielding logarithmic shot scaling.
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AutoQResearch: LLM-Guided Closed-Loop Policy Search for Adaptive Variational Quantum Optimization
An LLM-guided scout–promote–confirm loop discovers adaptive variational quantum policies that beat static baselines on MIS and CVRP while exposing proxy-ranking failures.
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Measuring what matters: A scalable framework for application-level quantum benchmarking
A framework consisting of 13 benchmark families is introduced to enable application-level benchmarking of quantum systems across metrics including solution quality, total execution time, energy consumption, and time-to-solution.
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Towards High Performance Quantum Computing (HPQ): Parallelisation of the Hamiltonian Auto Decomposition Optimisation Framework (HADOF)
Parallel HADOF execution on up to four IBM QPUs achieves 3-4x wall-clock speedup for combinatorial QUBO problems versus sequential runs, with comparable quality and validation on genome assembly instances.
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Non-variational supervised quantum kernel methods: a review
A review synthesizing foundations, constructions, advantage conditions, and challenges for non-variational quantum kernel methods in supervised learning.