arXiv research

Khovanov homology helps create quantum error-correcting codes.

Quantum codes on hyperbolic lattices outperform Euclidean ones with higher rates and lower overhead.

Generative AI decodes quantum codes without labeled data.

Using 4-dimensional arithmetic hyperbolic manifolds, we construct some new homological quantum error correcting codes. They are LDPC codes with linear rate and distance $n^ε$. Their rate is evaluated via Euler characteristic arguments and their distance using $\mathbb{Z}_2$-systolic geometry. This construction answers …

We introduce a differential geometric framework for describing families of quantum error-correcting codes and for understanding quantum fault tolerance. This work unifies the notion of topological fault tolerance with fault tolerance in other kinds of quantum error-correcting codes. In particular, we use fibre bundles …

The theory of quantum computation can be constructed from the abstract study of anyonic systems. In mathematical terms, these are unitary topological modular functors. They underlie the Jones polynomial and arise in Witten-Chern-Simons theory. The braiding and fusion of anyonic excitations in quantum Hall electron liqu…

Finding efficient decoders for quantum error correcting codes adapted to realistic experimental noise in fault-tolerant devices represents a significant challenge. In this paper we introduce several decoding algorithms complemented by deep neural decoders and apply them to analyze several fault-tolerant error correctio…

Quantum machine learning generalizes well from limited data.

Quantum codes linked to abelian varieties, providing mathematical rigor.

Lossy compression of statistical data using quantum annealing.

We use Khovanov homology to define families of LDPC quantum error-correcting codes: unknot codes with asymptotical parameters [[3^(2l+1)/sqrt(8πl);1;2^l]]; unlink codes with asymptotical parameters [[sqrt(2/2πl)6^l;2^l;2^l]] and (2,l)-torus link codes with asymptotical parameters [[n;1;d_n]] where d_n>\sqrt(n)/1.62.

New method bounds hardware noise without assumptions.

The combination of machine learning and quantum computing has emerged as a promising approach for addressing previously untenable problems. Reservoir computing is an efficient learning paradigm that utilizes nonlinear dynamical systems for temporal information processing, i.e., processing of input sequences to produce …

Variational hybrid quantum-classical optimization represents one of the most promising avenue to show the advantage of nowadays noisy intermediate-scale quantum computers in solving hard problems, such as finding the minimum-energy state of a Hamiltonian or solving some machine-learning tasks. In these devices noise is…

Study on topological order on fractal geometries, proving no-go theorem and fault-tolerant gates.

The paper develops methods to assess and correct model uncertainties in graphical models.

We define the quantum correction of the Teichmüller space $\mathcal{T}$ of Calabi-Yau manifolds. Under the assumption of no weak quantum correction, we prove that the Teichmüller space $\mathcal{T}$ is a locally symmetric space with the Weil-Petersson metric. For Calabi-Yau threefolds, we show that no strong quantum co…

Post-Quantum Secure Federated DeFi for Inclusive Banking

QTAML models quantum tunneling errors for AI robustness.

Given the Lagrangian fibration $T^4\to T^2$ and a Lagrangian submanifold, exhibiting an elliptic umbilic and supporting a flat line bundle, we study, in the context of mirror symmetry, the ``quantum'' corrections necessary to solve the monodromy of the holomorphic structure of the mirror bundle on the dual fibration.

For the problems of low-rank matrix completion, the efficiency of the widely-used nuclear norm technique may be challenged under many circumstances, especially when certain basis coefficients are fixed, for example, the low-rank correlation matrix completion in various fields such as the financial market and the low-ra…

We introduce the hemicubic codes, a family of quantum codes obtained by associating qubits with the $p$-faces of the $n$-cube (for $n>p$) and stabilizer constraints with faces of dimension $(p\pm1)$. The quantum code obtained by identifying antipodal faces of the resulting complex encodes one logical qubit into $N = 2^…

Quantum field theory connects deep neural networks to criticality.

A model corrects Lithuanian grammatical errors.

The study examines methods to correct measurement error in nutritional epidemiology studies.

Paper presents a new VMBQC model with fewer parameters for better generative modeling.

QGAA learns latent quantum states, reducing errors in quantum data generation.

Study loop corrections in random feature models affecting training and test errors.

Analyzes dynamics of quantum neural networks, predicting exponential decay of training error.

Quantum reservoirs risk bounds are analyzed using Rademacher complexity.

In this paper, we study the quantum $\mathfrak{sl}(n)$ representation category using the web space. Specially, we extend $\mathfrak{sl}(n)$ web space for $n\ge 4$ as generalized Temperley-Lieb algebras. As an application of our study, we find that the HOMFLY polynomial $P_n(q)$ specialized to a one variable polynomial …

We show that using the family of adapted Kähler polarizations of the phase space of a compact, simply connected, Riemannian symmetric space of rank-1, the obtained field $H^{corr}$ of quantum Hilbert spaces produced by geometric quantization including the half-form correction is flat if $M$ is the 3-dimensional sphere …

Correction for Error estimates for binomial approximations of game options [math.PR/0607123]

Fault-tolerant neural networks inspired by biological error correction codes.

Quantum Kerr learning shows enhancements in convergence and generalization for kernel-based methods.

SCaSML improves PDE solvers by correcting errors efficiently.

Given, in the Lagrangian torus fibration $R^4\to R^2$, a Lagrangian submanifold $L$, endowed with a trivial flat connection, the corresponding mirror object is constructed on the dual fibration by means of a family of Morse homologies associated to the generating function of $L$, and it is provided with a holomorphic s…

Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.

Quantum neural tangent kernels help understand variational quantum circuits in machine learning.

The study examines how quantum resources enhance the complexity of quantum circuits.

Quantum algorithms improve perceptron learning efficiency.

Renormalization in neural networks linked to quantum field theory.

Recent developments in the field of deep learning have motivated many researchers to apply these methods to problems in quantum information. Torlai and Melko first proposed a decoder for surface codes based on neural networks. Since then, many other researchers have applied neural networks to study a variety of problem…

New method combines machine learning with data assimilation for model error correction.

Autonomy and adaptation of machines requires that they be able to measure their own errors. We consider the advantages and limitations of such an approach when a machine has to measure the error in a regression task. How can a machine measure the error of regression sub-components when it does not have the ground truth…

String geometry theory uniquely determines classical action with T-symmetry.

Quantum-enhanced method improves stock return prediction accuracy.

We introduce the speculate-correct method to derive error bounds for local classifiers. Using it, we show that k nearest neighbor classifiers, in spite of their famously fractured decision boundaries, have exponential error bounds with O(sqrt((k + ln n) / n)) error bound range for n in-sample examples.