English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine learning has recently emerged as a fruitful area for findingpotential quantum computational advantage. Many of the quantum enhanced machinelearning algorithms critically hinge upon the ability to efficiently producestates proportional to high-dimensional data points stored in a quantumaccessible memory. Even given query access to exponentially many entries storedin a database, the construction of which is considered a one-off overhead, ithas been argued that the cost of preparing such amplitude-encoded states mayoffset any exponential quantum advantage. Here we prove using smoothedanalysis, that if the data-analysis algorithm is robust against smallentry-wise input perturbation, state preparation can always be achieved withconstant queries. This criterion is typically satisfied in realistic machinelearning applications, where input data is subjective to moderate noise. Ourresults are equally applicable to the recent seminal progress inquantum-inspired algorithms, where specially constructed databases suffice forpolylogarithmic classical algorithm in low-rank cases. The consequence of ourfinding is that for the purpose of practical machine learning, polylogarithmicprocessing time is possible under a general and flexible input model withquantum algorithms or quantum-inspired classical algorithms in the low-rankcases.

Smooth input preparation for quantum and quantum-inspired machine learning