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

Most research in quantum computing today is performed against simulations ofquantum computers rather than true quantum computers. Simulating a quantumcomputer entails implementing all of the unitary operators corresponding to thequantum gates as tensors. For high numbers of qubits, performing tensormultiplications for these simulations becomes quite expensive, since $N$-qubitgates correspond to $2^{N}$-dimensional tensors. One way to accelerate such asimulation is to use field programmable gate array (FPGA) hardware toefficiently compute the matrix multiplications. Though FPGAs can efficientlyperform tensor multiplications, they are memory bound, having relatively smallblock random access memory. One way to potentially reduce the memory footprintof a quantum computing system is to represent it as a tensor network; tensornetworks are a formalism for representing compositions of tensors whereineconomical tensor contractions are readily identified. Thus we explore tensornetworks as a means to reducing the memory footprint of quantum computingsystems and broadly accelerating simulations of such systems.

Tensor Networks for Simulating Quantum Circuits on FPGAs