Distributing Quantum Computations, Shot-wise

• URL: http://arxiv.org/abs/2411.16530v1
• Date: Mon, 25 Nov 2024 16:16:54 GMT
• Title: Distributing Quantum Computations, Shot-wise
• Authors: Giuseppe Bisicchia, Giuseppe Clemente, Jose Garcia-Alonso, Juan Manuel Murillo Rodríguez, Massimo D'Elia, Antonio Brogi,
• Abstract summary: NISQ era constraints, high sensitivity to noise and limited qubit count, impose significant barriers on the usability of QPUs. We propose a methodological framework, termed shot-wise, which enables the distribution of shots for a single circuit across multiple QPUs.
• Score: 1.2061873132374783
• License:
• Abstract: NISQ (Noisy Intermediate-Scale Quantum) era constraints, high sensitivity to noise and limited qubit count, impose significant barriers on the usability of QPUs (Quantum Process Units) capabilities. To overcome these challenges, researchers are exploring methods to maximize the utility of existing QPUs despite their limitations. Building upon the idea that the execution of a quantum circuit's shots needs not to be treated as a singular monolithic unit, we propose a methodological framework, termed shot-wise, which enables the distribution of shots for a single circuit across multiple QPUs. Our framework features customizable policies to adapt to various scenarios. Additionally, it introduces a calibration method to pre-evaluate the accuracy and reliability of each QPU's output before the actual distribution process and an incremental execution mechanism for dynamically managing the shot allocation and policy updates. Such an approach enables flexible and fine-grained management of the distribution process, taking into account various user-defined constraints and (contrasting) objectives. Experimental findings show that while these strategies generally do not exceed the best individual QPU results, they maintain robustness and align closely with average outcomes. Overall, the shot-wise methodology improves result stability and often outperforms single QPU runs, offering a flexible approach to managing variability in quantum computing.

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