Hybrid Path-Sums offer a new symbolic framework with rewriting rules and assertions to represent, simplify, and verify properties of hybrid quantum-classical programs.
Measurement-basedquantum computation on cluster states,
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
A VMBQC model restricted to one extra trainable parameter generates distributions that the corresponding unitary model cannot learn.
Experimental realization of 2D CV cluster states with 191 modes and -1.2 dB nullifier squeezing in the microwave domain using multi-tone parametric amplification.
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.
Photonic QNNs with two trainable parameters solve nonlinear tasks like XOR at 100% accuracy where parameter-matched ANNs fail, with hardware deployment confirming the result.
The paper proposes connecting path (momentum) entanglement to concurrence in phase-modified Mach-Zehnder interferometers for two-quanton systems as an analog to spin/polarization joint measurements.
citing papers explorer
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Hybrid Path-Sums for Hybrid Quantum Programs
Hybrid Path-Sums offer a new symbolic framework with rewriting rules and assertions to represent, simplify, and verify properties of hybrid quantum-classical programs.
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Minimizing classical resources in variational measurement-based quantum computation for generative modeling
A VMBQC model restricted to one extra trainable parameter generates distributions that the corresponding unitary model cannot learn.
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Continuous-variable two-dimensional cluster states in the microwave domain
Experimental realization of 2D CV cluster states with 191 modes and -1.2 dB nullifier squeezing in the microwave domain using multi-tone parametric amplification.
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Resource Management in Heterogeneous Quantum Repeater Networks
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.
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Algorithmic Advantage on a Gate-Based Photonic Quantum Neural Network
Photonic QNNs with two trainable parameters solve nonlinear tasks like XOR at 100% accuracy where parameter-matched ANNs fail, with hardware deployment confirming the result.