Quantum machine learning: a classical perspective

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.Comment: v3 33 pages; typos corrected and references adde

Making an Embedded DBMS JIT-friendly

While database management systems (DBMSs) are highly optimized, interactions across the boundary between the programming language (PL) and the DBMS are costly, even for in-process embedded DBMSs. In this paper, we show that programs that interact with the popular embedded DBMS SQLite can be significantly optimized - by a factor of 3.4 in our benchmarks - by inlining across the PL / DBMS boundary. We achieved this speed-up by replacing parts of SQLite's C interpreter with RPython code and composing the resulting meta-tracing virtual machine (VM) - called SQPyte - with the PyPy VM. SQPyte does not compromise stand-alone SQL performance and is 2.2% faster than SQLite on the widely used TPC-H benchmark suite.Comment: 24 pages, 18 figure

On the Efficiency of Optimising Shallow Backtracking in Prolog

The cost of backtracking has been identified as one of the bottlenecks in achieving peak performance in compiled Prolog programs. Much of the backtracking in Prolog programs is shallow, i.e. is caused by unification failures in the head of a clause when there are more alternatives for the same procedure, and so special treatment of this form of backtracking has been proposed as a significant optimisation. This paper describes a modified WAM which optimises shallow backtracking. Four different implementation approaches are compared. A number of benchmark results are presented, measuring the relative tradeoffs between compilation time, code size, and run time. The results show that the speedup gained by this optimisation can be significant