Co-designed Quantum Discrete Adiabatic Linear System Solver Via Dynamic Circuits

• URL: http://arxiv.org/abs/2505.24626v1
• Date: Fri, 30 May 2025 14:18:42 GMT
• Title: Co-designed Quantum Discrete Adiabatic Linear System Solver Via Dynamic Circuits
• Abstract summary: Existing quantum discrete adiabatic approaches are hindered by circuit depth that increases linearly with the number of evolution steps.<n>We propose a co-designed framework that integrates dynamic circuit capabilities with real-time classical processing.<n>Our solver successfully overcomes previous depth limitations, maintaining over (80%) solution fidelity even under realistic noise models.
• Score: 13.107851477798627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing quantum discrete adiabatic approaches are hindered by circuit depth that increases linearly with the number of evolution steps, a significant challenge for current quantum hardware with limited coherence times. To address this, we propose a co-designed framework that synergistically integrates dynamic circuit capabilities with real-time classical processing. This framework reformulates the quantum adiabatic evolution into discrete, dynamically adjustable segments. The unitary operator for each segment is optimized on-the-fly using classical computation, and circuit multiplexing techniques are leveraged to reduce the overall circuit depth scaling from \(O(\text{steps}\times\text{depth}(U))\) to \(O(\text{depth}(U))\). We implement and benchmark a quantum discrete adiabatic linear solver based on this framework for linear systems of \(W \in {2,4,8,16}\) dimensions with condition numbers \(\kappa \in {10,20,30,40,50}\). Our solver successfully overcomes previous depth limitations, maintaining over \(80\%\) solution fidelity even under realistic noise models. Key algorithmic optimizations contributing to this performance include a first-order approximation of the discrete evolution operator, a tailored dynamic circuit design exploiting real-imaginary component separation, and noise-resilient post-processing techniques.

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