results for au:Wossnig_L in:quant-ph

- Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.
- Apr 21 2017 quant-ph arXiv:1704.06174v2Solving linear systems of equations is a frequently encountered problem in machine learning and optimisation. Given a matrix $A$ and a vector $\mathbf b$ the task is to find the vector $\mathbf x$ such that $A \mathbf x = \mathbf b$. We describe a quantum algorithm that achieves a sparsity-independent runtime scaling of $\mathcal{O}(\kappa^2 \|A\|_F \text{polylog}(n)/\epsilon)$, where $n\times n$ is the dimensionality of $A$ with Frobenius norm $\|A\|_F$, $\kappa$ denotes the condition number of $A$, and $\epsilon$ is the desired precision parameter. When applied to a dense matrix with spectral norm bounded by a constant, the runtime of the proposed algorithm is bounded by $\mathcal{O}(\kappa^2\sqrt{n} \text{polylog}(n)/\epsilon)$, which is a quadratic improvement over known quantum linear system algorithms. Our algorithm is built upon a singular value estimation subroutine, which makes use of a memory architecture that allows for efficient preparation of quantum states that correspond to the rows and row Frobenius norms of $A$.
- Dec 07 2016 quant-ph arXiv:1612.01789v2Solving optimization problems in disciplines such as machine learning is commonly done by iterative methods. Gradient descent algorithms find local minima by moving along the direction of steepest descent while Newton's method takes into account curvature information and thereby often improves convergence. Here, we develop quantum versions of these iterative optimization algorithms and apply them to homogeneous polynomial optimization with a unit norm constraint. In each step, multiple copies of the current candidate are used to improve the candidate using quantum phase estimation, an adapted quantum principal component analysis scheme, as well as quantum matrix multiplications and inversions. The required operations perform polylogarithmically in the dimension of the solution vector, an exponential speed-up over classical algorithms, which scale polynomially. The quantum algorithm can therefore be beneficial for high dimensional problems where a relatively small number of iterations is sufficient.