Paper Citation Record · LEDGER

Generative-enhanced optimization for knapsack problems: an industry-relevant study

As of 9 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2502.04928.

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pith.paper-citation-record.v1
2502.04928 v1

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measured 46 of 46 reference resolution

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Reference resolution

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External citation measurements

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Outbound references

Observation c7406da1-4a35-48b0-88e8-ff1c1594e6cc · outbound

This paper cites Martello and P.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Martello and P

Reference 1

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This paper cites A Quantum Approximate Optimization Algorithm.

Generative-enhanced optimization for knapsack problems: an industry-relevant study A Quantum Approximate Optimization Algorithm

Reference 2

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This paper cites Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem,

Reference 3

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Quantum speedups in solving near-symmetric optimization problems by low-depth QAOA

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Generative-enhanced optimization for knapsack problems: an industry-relevant study A fast quantum mechanical algorithm for database search,

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This paper cites Opening the black box inside grover’s algorithm,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Opening the black box inside grover’s algorithm,

Reference 6

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This paper cites Quantum computing in the nisq era and beyond,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Quantum computing in the nisq era and beyond,

Reference 7

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This paper cites Available: https://link.aps.org/doi/10.1103/PhysRevX.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Available: https://link.aps.org/doi/10.1103/PhysRevX

Reference 8

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Quantum fourier transform has small entanglement,

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This paper cites A quantum-inspired approach to exploit turbulence structures,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study A quantum-inspired approach to exploit turbulence structures,

Reference 10

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This paper cites Enhancing combinatorial optimization with classical and quantum generative models,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Enhancing combinatorial optimization with classical and quantum generative models,

Reference 11

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This paper cites CMA-ES/pycma on Github,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study CMA-ES/pycma on Github,

Reference 12

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This paper cites Cinelli, M.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Cinelli, M

Reference 13

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This paper cites PROTES: Probabilistic Optimization with Tensor Sampling.

Generative-enhanced optimization for knapsack problems: an industry-relevant study PROTES: Probabilistic Optimization with Tensor Sampling

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This paper cites Variational quantum algorithms,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Variational quantum algorithms,

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This paper cites Generative Adversarial Networks.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Generative Adversarial Networks

Reference 16

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This paper cites Practical overview of image classification with tensor-network quantum circuits,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Practical overview of image classification with tensor-network quantum circuits,

Reference 17

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This paper cites Tensor networks for complex quantum systems,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Tensor networks for complex quantum systems,

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This paper cites Compactifai: Extreme compression of large language models using quantum-inspired tensor networks,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Compactifai: Extreme compression of large language models using quantum-inspired tensor networks,

Reference 19

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This paper cites Efficient mps representations and quantum circuits from the fourier modes of classical image data,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study Efficient mps representations and quantum circuits from the fourier modes of classical image data,

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This paper cites The density-matrix renormalization group,.

Generative-enhanced optimization for knapsack problems: an industry-relevant study The density-matrix renormalization group,

Reference 21

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Unsupervised generative modeling using matrix product states,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Symmetric tensor networks for generative modeling and constrained combinatorial opti- mization,

Reference 23

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Perfect sampling with unitary tensor networks,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study A practical introduction to tensor networks: Matrix product states and projected entangled pair states,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Quantum Inspired Optimization for Industrial Scale Problems

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Tensor network states and algorithms in the presence of a global u(1) symmetry,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Classical simulation of quantum many-body systems with a tree tensor network,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Deep generative modelling: A com- parative review of vaes, gans, normalizing flows, energy-based and autoregressive models,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Applications of negative dimensional tensors,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study The density-matrix renormalization group in the age of matrix product states,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Tensor-train decomposition,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Supervised learning with tensor networks,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Density matrix renormalization group algorithms with a single center site,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Density-matrix algorithms for quantum renormalization groups,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Facts, conjectures, and improve- ments for simulated annealing,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Tensor Network Estimation of Distribution Algorithms

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Unresolved cited work

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Available: https://www.gurobi.com/documentation/ current/refman/index.html

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Comb tensor networks,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Supervised Learning with Quantum-Inspired Tensor Networks

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Generative learning of continuous data by tensor networks,

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Available: https://link.aps.org/doi/10.1103/PhysRevX.8

Reference 2018

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Generative-enhanced optimization for knapsack problems: an industry-relevant study Available: https://www.science.org/doi/abs/10.1126/ sciadv.adm6761

Reference 2024

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