EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.
A practical introduction to tensor net- works: Matrix product states and projected entangled pair states,
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MPS energy landscapes lack poor local minima because gauge freedom induces overparametrization that concentrates local minima near the global minimum, with the local minimum distribution proven invariant under orthogonality center moves.
EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.
A family of SDP-derived certified upper bounds converges to the bulk spectral gap, proving it semi-decidable for quantum lattice systems.
The paper introduces compositional interpretability as a category-theoretic framework that casts mechanistic explanations as commuting syntactic-semantic mappings optimized under faithfulness and complexity constraints derived from minimum description length.
AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
Computational constraints exponentially suppress accessible entanglement for some highly entangled quantum states and can make mixed-state min-entropy appear maximal when the information-theoretic version is negative.
Alternating cross interpolation performs elementwise operations on tensor trains in O(χ³) time with error control, improving on the standard O(χ⁴) scaling when output ranks are controlled.
Derives combinatorial expression for degrees of tensor train varieties via integral geometry and releases Julia package TTVarietyDegree.jl.
Canonical mapping of quantum-dot-superconductor clusters enables neural quantum-state calculations that reveal trivial singlet, Heisenberg-like, and critical regimes with 1D gaplessness and 2D triplet states.
A communication-aware multi-GPU distribution approach for tensor network contraction reports 7-173x extra speedup over slicing on 8 H100 GPUs and 42x to 67,869x on 1024 GPUs.
A matrix product state tensor network method enables numerically exact simulation of coherence and population dynamics in spin networks, including under repeated light pulses.
A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.
Case study applies verifier-guided LLM evolutionary agents to contraction-order optimization in tensor networks and concludes that human validation remains essential.
A quantum-inspired framework using effective Hamiltonians, Metropolis annealing and stochastic tensor-network compression is proposed for adaptive multi-demand routing in large-scale QKD networks.
Perspective review comparing variational and feedback quantum algorithms for simulating phase transitions in quantum many-body systems, highlighting barren plateaus and advocating physics-informed hybridization.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
Introductory lecture notes on tensor networks with emphasis on matrix-product states, their algorithms, higher-dimensional generalizations, and applications to mixed states and open quantum systems, accompanied by Julia code.
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