Reducing the Cost of Qudit State Preparation by Learning a Sparse Structure for SNAP Gates
Preparing quantum states in bosonic qudits with SNAP--Displacement sequences requires both optimizing the gate parameters and deciding which Fock levels should be controlled by each SNAP gate.
ProofPaper ↗
Key points
- A fully parameterized ansatz assigns an independent phase to every Fock level in every gate, leading to a number of phases that grows steeply.
- Previous work reduced this cost by restricting the trainable phases to predefined sparse patterns, such as a diagonal or a grid, which however may not be optimal for a specific target state.
- In this work, we address this limitation by introducing a method that learns which phases are needed together with their values, using a differentiable mask.
- We compare the proposed method with fully parameterized and predefined sparse ansatzes on Fourier and Haar-random target states.
Sources (1)
- [1]Reducing the Cost of Qudit State Preparation by Learning a Sparse Structure for SNAP GatesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 09:16 AM
Preparing quantum states in bosonic qudits with SNAP--Displacement sequences requires both optimizing the gate parameters and deciding which Fock levels should be controlled by each SNAP gate.
A fully parameterized ansatz assigns an independent phase to every Fock level in every gate, leading to a number of phases that grows steeply.
Extractive summary: sentences quoted from the sources.
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