Geometric characterization of optimal classical RACs with explicit constructions, optimality proofs for several families, and a quantum RAC establishing classical-quantum separation for the (2^k-1, k) family.
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Dubey, Christian Ufrecht, Maniraman Periyasamy, Axel Plinge, Christopher Mutschler & Daniel D
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Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.
Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.
Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.
Hybrid QML models trained with classical DP-SGD retain higher accuracy than classical models under fixed privacy budgets on synthetic and image-classification tasks.
A graph-theoretic nonlinear integer program solved via genetic algorithm reduces qubit transfers in neutral atom quantum circuit compilation compared to prior zoned-architecture compilers.
SCORE is a sequential cyclic optimization method for continuous ground station placement that achieves up to 15% higher total downlink throughput than fixed-site approaches and converges with up to 5x fewer evaluations than differential evolution on tested constellations.
Systematic review of 81 QEM papers finds only 25% use inferential methods and demonstrates via ZNE case studies that parameter sensitivity and temporal drift can create illusory performance gains.
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
LUNA achieves up to 10.95x area reduction and 30% lower latency for qubit readout using integrator-based preprocessing and LogicNet LUT synthesis with minimal fidelity loss.
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
A quantum autoencoder for multivariate time series anomaly detection achieves competitive performance with neural-network autoencoders using fewer trainable parameters.
FPGA emulator tests 10^13 error patterns in 20 days and diversity BP decoder matches BP+OSD logical error rates with 30-80% average speed gains and far less post-processing for QLDPC codes.
PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
Proposes a heterogeneous quantum repeater network architecture using recursive designs and RuleSets with a new bridging building block, but states that full-scale resource trade-off analysis remains future work.
Crosstalk patterns between quantum circuits on IBM processors are predictable by circuit type and hardware architecture, with high intra-revision consistency and topological decoupling between lattice types.
Three scheduling strategies for hybrid quantum-HPC systems cut classical resource use by up to 64% or boost QPU utilization depending on workload balance, validated on real hardware.
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
A distributed switching protocol for unbuffered quantum networks uses cooperative BSA selection and bi-path reservations to achieve high link success rates under load in simulations.
Quantum walks integrated with variational circuits and CUDA-Q acceleration generate high-fidelity adaptive probability distributions for 1D financial modeling and 2D digit patterns.
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Tensor network compression using fluid dynamics as a testbed: Analytical foundations in one dimension
Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.