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Found 12 papers, 2 papers with code
Improved Financial Forecasting via Quantum Machine Learning
Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications.
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
Quantum Vision Transformers
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis.
Quantum Reinforcement Learning via Policy Iteration
Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised learning.
Prospects and challenges of quantum finance
Quantum computers are expected to have substantial impact on the finance industry, as they will be able to solve certain problems considerably faster than the best known classical algorithms.
Quantum algorithms for Second-Order Cone Programming and Support Vector Machines
We present a quantum interior-point method (IPM) for second-order cone programming (SOCP) that runs in time $\widetilde{O} \left( n\sqrt{r} \frac{\zeta \kappa}{\delta^2} \log \left(1/\epsilon\right) \right)$ where $r$ is the rank and $n$ the dimension of the SOCP, $\delta$ bounds the distance of intermediate solutions from the cone boundary, $\zeta$ is a parameter upper bounded by $\sqrt{n}$, and $\kappa$ is an upper bound on the condition number of matrices arising in the classical IPM for SOCP.
Quantum Expectation-Maximization for Gaussian Mixture Models
In this work we define and use a quantum version of EM to fit a Gaussian Mixture Model.
q-means: A quantum algorithm for unsupervised machine learning
For a natural notion of well-clusterable datasets, the running time becomes $\widetilde{O}\left( k^2 d \frac{\eta^{2. 5}}{\delta^3} + k^{2. 5} \frac{\eta^2}{\delta^3} \right)$ per iteration, which is linear in the number of features $d$, and polynomial in the rank $k$, the maximum square norm $\eta$ and the error parameter $\delta$.
Quantum algorithms for feedforward neural networks
The running times of our algorithms can be quadratically faster in the size of the network than their standard classical counterparts since they depend linearly on the number of neurons in the network, as opposed to the number of connections between neurons as in the classical case.
Anonymity for practical quantum networks
Quantum communication networks have the potential to revolutionise information and communication technologies.
Quantum Physics
Quantum classification of the MNIST dataset with Slow Feature Analysis
We simulate the quantum classifier (including errors) and show that it can provide classification of the MNIST handwritten digit dataset, a widely used dataset for benchmarking classification algorithms, with $98. 5\%$ accuracy, similar to the classical case.
Learning with Errors is easy with quantum samples
Learning with Errors is one of the fundamental problems in computational learning theory and has in the last years become the cornerstone of post-quantum cryptography.
Quantum Physics Computational Complexity
