Generating three-qubit quantum circuits with neural networks

Michael Swaddle, Lyle Noakes, Harry Smallbone, Liam Salter, Jingbo Wang

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)

    Abstract

    A new method for compiling quantum algorithms is proposed and tested for a three qubit system. The proposed method is to decompose a unitary matrix U, into a product of simpler Uj via a neural network. These Uj can then be decomposed into product of known quantum gates. Key to the effectiveness of this approach is the restriction of the set of training data generated to paths which approximate minimal normal subRiemannian geodesics, as this removes unnecessary redundancy and ensures the products are unique. The two neural networks are shown to work effectively, each individually returning low loss values on validation data after relatively short training periods. The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits. There is scope for more work in scaling this approach for larger quantum systems.

    Original languageEnglish
    Pages (from-to)3391-3395
    Number of pages5
    JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
    Volume381
    Issue number39
    DOIs
    Publication statusPublished - 17 Oct 2017

    Fingerprint

    Dive into the research topics of 'Generating three-qubit quantum circuits with neural networks'. Together they form a unique fingerprint.

    Cite this