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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study quantum interior point methods (QIPMs) for second-order coneprogramming (SOCP), guided by the example use case of portfolio optimization(PO). We provide a complete quantum circuit-level description of the algorithmfrom problem input to problem output, making several improvements to theimplementation of the QIPM. We report the number of logical qubits and thequantity/depth of non-Clifford T-gates needed to run the algorithm, includingconstant factors. The resource counts we find depend on instance-specificparameters, such as the condition number of certain linear systems within theproblem. To determine the size of these parameters, we perform numericalsimulations of small PO instances, which lead to concrete resource estimatesfor the PO use case. Our numerical results do not probe large enough instancesizes to make conclusive statements about the asymptotic scaling of thealgorithm. However, already at small instance sizes, our analysis suggeststhat, due primarily to large constant pre-factors, poorly conditioned linearsystems, and a fundamental reliance on costly quantum state tomography,fundamental improvements to the QIPM are required for it to lead to practicalquantum advantage.

End-to-end resource analysis for quantum interior point methods and portfolio optimization