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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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.
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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.
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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.
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.
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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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