Hidden Hypergraphs,Error-Correcting Codes,and Critical Learning in Hopfield Networks

In 1943, McCulloch and Pitts introduced a discrete recurrent neural network as a model for computation in brains. The work inspired breakthroughs such as the first computer design and the theory of finite automata. We focus on learning in Hopfield networks, a special case with symmetric weights and fixed-point attractor dynamics. Specifically, we explore minimum energy flow (MEF) as a scalable convex objective for determining network parameters. We catalog various properties of MEF, such as biological plausibility, and then compare to classical approaches in the theory of learning. Trained Hopfield networks can perform unsupervised clustering and define novel error-correcting coding schemes. They also efficiently find hidden structures (cliques) in graph theory. We extend this known connection from graphs to hypergraphs and discover n-node networks with robust storage of 2Ω(n1−ϵ) memories for any ϵ>0. In the case of graphs, we also determine a critical ratio of training samples at which networks generalize completely.     

On non-homogeneous Cauchy–Fueter equations and Hartogs’ phenomenon in several quaternionic variables

The Cauchy–Fueter complex is the counterpart of the Dolbeault complex in the theory of several quaternionic variables. By using the fundamental solution to the Laplacian operators of fourth order associated to this differential complex on Hⁿ${\mathbb{H}}^n$, we can solve the system of non-homogeneous Cauchy–Fueter equations and prove the Hartogs’ extension phenomenon for quaternionic regular functions on any domain. The quaternionic version of Bochner–Martinelli integral representation formula for H$\mathbb{H}$-valued functions is also given.     

Neural-Network Quantum States for Spin-1 Systems: Spin-Basis and Parameterization Effects on Compactness of Representations

Neural network quantum states (NQS) have been widely applied to spin-1/2 systems, where they have proven to be highly effective. The application to systems with larger on-site dimension, such as spin-1 or bosonic systems, has been explored less and predominantly using spin-1/2 Restricted Boltzmann Machines (RBMs) with a one-hot/unary encoding. Here, we propose a more direct generalization of RBMs for spin-1 that retains the key properties of the standard spin-1/2 RBM, specifically trivial product states representations, labeling freedom for the visible variables and gauge equivalence to the tensor network formulation. To test this new approach, we present variational Monte Carlo (VMC) calculations for the spin-1 anti-ferromagnetic Heisenberg (AFH) model and benchmark it against the one-hot/unary encoded RBM demonstrating that it achieves the same accuracy with substantially fewer variational parameters. Furthermore, we investigate how the hidden unit complexity of NQS depend on the local single-spin basis used. Exploiting the tensor network version of our RBM we construct an analytic NQS representation of the Affleck-Kennedy-Lieb-Tasaki (AKLT) state in the xyz spin-1 basis using only M=2N hidden units, compared to M∼O(N2) required in the Sz basis. Additional VMC calculations provide strong evidence that the AKLT state in fact possesses an exact compact NQS representation in the xyz basis with only M=N hidden units. These insights help to further unravel how to most effectively adapt the NQS framework for more complex quantum systems.     

A Damping-Tunable Snap System: From Dissipative Hyperchaos to Conservative Chaos

A hyperjerk system described by a single fourth-order ordinary differential equation of the form x⃜=f(x⃛,x¨,x˙,x) has been referred to as a snap system. A damping-tunable snap system, capable of an adjustable attractor dimension (DL) ranging from dissipative hyperchaos (DL<4) to conservative chaos (DL=4), is presented for the first time, in particular not only in a snap system, but also in a four-dimensional (4D) system. Such an attractor dimension is adjustable by nonlinear damping of a relatively simple quadratic function of the form Ax2, easily tunable by a single parameter A. The proposed snap system is practically implemented and verified by the reconfigurable circuits of field programmable analog arrays (FPAAs).     

Shannon Entropy Loss in Mixed-Radix Conversions

This paper models a translation for base-2 pseudorandom number generators (PRNGs) to mixed-radix uses such as card shuffling. In particular, we explore a shuffler algorithm that relies on a sequence of uniformly distributed random inputs from a mixed-radix domain to implement a Fisher–Yates shuffle that calls for inputs from a base-2 PRNG. Entropy is lost through this mixed-radix conversion, which is assumed to be surjective mapping from a relatively large domain of size 2J to a set of arbitrary size n. Previous research evaluated the Shannon entropy loss of a similar mapping process, but this previous bound ignored the mixed-radix component of the original formulation, focusing only on a fixed n value. In this paper, we calculate a more precise formula that takes into account a variable target domain radix, n, and further derives a tighter bound on the Shannon entropy loss of the surjective map, while demonstrating monotonicity in a decrease in entropy loss based on increased size J of the source domain 2J. Lastly, this formulation is used to specify the optimal parameters to simulate a card-shuffling algorithm with different test PRNGs, validating a concrete use case with quantifiable deviations from maximal entropy, making it suitable to low-power implementation in a casino.     

Construction of a Family of Maximally Entangled Bases in ℂd ⊗ ℂd′

In this paper, we present a new method for the construction of maximally entangled states in Cd⊗Cd′ when d′≥2d. A systematic way of constructing a set of maximally entangled bases (MEBs) in Cd⊗Cd′ was established. Both cases when d′ is divisible by d and not divisible by d are discussed. We give two examples of maximally entangled bases in C2⊗C4, which are mutually unbiased bases. Finally, we found a new example of an unextendible maximally entangled basis (UMEB) in C2⊗C5.     

Constraints on sub-GeV hidden sector gauge bosons from a search for heavy neutrino decays

Several models of dark matter motivate the concept of hidden sectors consisting of SU_(3)C×SU_(2)L×U_(1)Y$\mathit{SU}{(3)}_C\times \mathit{SU}{(2)}_L\times U{(1)}_Y$ singlet fields. The interaction between our and hidden matter could be transmitted by new abelian U^′(1)$U^{\prime }(1)$ gauge bosons A^′$A^{\prime }$ mixing with ordinary photons. If such A^′$A^{\prime }$?s with the mass in the sub-GeV range exist, they would be produced through mixing with photons emitted in decays of η   and η^′${\eta }^{\prime }$ neutral mesons generated by the high energy proton beam in a neutrino target. The A^′$A^{\prime }$?s would then penetrate the downstream shielding and be observed in a neutrino detector via their A^′→e⁺e⁻$A^{\prime }\to e^{+}{e}^{-}$ decays. Using bounds from the CHARM neutrino experiment at CERN that searched for an excess of e⁺e⁻$e^{+}{e}^{-}$ pairs from heavy neutrino decays, the area excluding the γ−A^′$\gamma -A^{\prime }$ mixing range 10⁻⁷???10⁻⁴${10}^{-7}???{10}^{-4}$ for the A^′$A^{\prime }$ mass region 1?_MA′?500 MeV$1?M_{A^{\prime }}?500\text{MeV}$ is derived. The obtained results are also used to constrain models, where a new gauge boson X   interacts with quarks and leptons. New upper limits on the branching ratio as small as Br(η→γX)?10⁻¹⁴$\mathrm{Br}(\eta \to \gamma X)?{10}^{-14}$ and Br(η^′→γX)?10⁻¹²$\mathrm{Br}({\eta }^{\prime }\to \gamma X)?{10}^{-12}$ are obtained, which are several orders of magnitude more restrictive than the previous bounds from the Crystal Barrel experiment.     

An energy-conserving Galerkin scheme for a class of nonlinear dispersive equations

A Galerkin scheme is presented for a class of conservative nonlinear dispersive equations, such as the Camassa–Holm equation and the regularized long wave equation. The scheme has two advantageous features: first, it is conservative in that it keeps the discrete analogue of the continuous energy conservation property in the original equations; second, it can be formulated only with cheap H¹$H^1$-elements even if the original equations include third derivative u_xxx$u_{\mathit{xxx}}$. Numerical experiments confirm the stability and effectiveness of the proposed scheme.     

Matrix regularization of embedded 4-manifolds

We consider products of two 2-manifolds such as S²×S²$S^2\times S^2$, embedded in Euclidean space and show that the corresponding 4-volume preserving diffeomorphism algebra can be approximated by a tensor product SU(N)⊗SU(N)$\mathit{SU}(N)\otimes \mathit{SU}(N)$ i.e. functions on a manifold are approximated by the Kronecker product of two SU(N)$\mathit{SU}(N)$ matrices.     

Quantum Bitcoin Mining

This paper studies the effect of quantum computers on Bitcoin mining. The shift in computational paradigm towards quantum computation allows the entire search space of the golden nonce to be queried at once by exploiting quantum superpositions and entanglement. Using Grover’s algorithm, a solution can be extracted in time O(2256/t), where t is the target value for the nonce. This is better using a square root over the classical search algorithm that requires O(2256/t) tries. If sufficiently large quantum computers are available for the public, mining activity in the classical sense becomes obsolete, as quantum computers always win. Without considering quantum noise, the size of the quantum computer needs to be ≈104 qubits.