Optimization Framework for Reducing Mid-circuit Measurements and Resets

• URL: http://arxiv.org/abs/2504.16579v1
• Date: Wed, 23 Apr 2025 10:01:00 GMT
• Title: Optimization Framework for Reducing Mid-circuit Measurements and Resets
• Authors: Yanbin Chen, Innocenzo Fulginiti, Christian B. Mendl,
• Abstract summary: We implement an optimization framework that targets both mid-circuit measurements and resets.<n>We evaluate our framework using a large dataset of randomly generated dynamic circuits.
• Score: 0.13108652488669736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper addresses the optimization of dynamic circuits in quantum computing, with a focus on reducing the cost of mid-circuit measurements and resets. We extend the probabilistic circuit model (PCM) and implement an optimization framework that targets both mid-circuit measurements and resets. To overcome the limitation of the prior PCM-based pass, where optimizations are only possible on pure single-qubit states, we incorporate circuit synthesis to enable optimizations on multi-qubit states. With a parameter $n_{pcm}$, our framework balances optimization level against resource usage.We evaluate our framework using a large dataset of randomly generated dynamic circuits. Experimental results demonstrate that our method is highly effective in reducing mid-circuit measurements and resets. In our demonstrative example, when applying our optimization framework to the Bernstein-Vazirani algorithm after employing qubit reuse, we significantly reduce its runtime overhead by removing all of the resets.

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