Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors

2025-07-23

AI Summary

Large-scale quantum processors remain scarce and prohibitively expensive to access. Method: This paper introduces a predictive surrogate model with provable computational efficiency guarantees, integrating classical supervised learning with quantum process modeling to accurately emulate the average behavior of a 20-qubit superconducting quantum system using only a small number of real quantum measurement outcomes. Contribution/Results: It presents the first formally defined and theoretically grounded surrogate paradigm for quantum systems, enabling variational quantum algorithm pretraining and nonequilibrium topological phase identification while drastically reducing reliance on physical hardware. Experiments demonstrate a 10²–10³-fold reduction in measurement overhead compared to conventional approaches; notably, the surrogate outperforms native quantum implementations on specific tasks. This work establishes a scalable, low-cost pathway for quantum algorithm development under stringent resource constraints.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

The ongoing development of quantum processors is driving breakthroughs in scientific discovery. Despite this progress, the formidable cost of fabricating large-scale quantum processors means they will remain rare for the foreseeable future, limiting their widespread application. To address this bottleneck, we introduce the concept of predictive surrogates, which are classical learning models designed to emulate the mean-value behavior of a given quantum processor with provably computational efficiency. In particular, we propose two predictive surrogates that can substantially reduce the need for quantum processor access in diverse practical scenarios. To demonstrate their potential in advancing digital quantum simulation, we use these surrogates to emulate a quantum processor with up to 20 programmable superconducting qubits, enabling efficient pre-training of variational quantum eigensolvers for families of transverse-field Ising models and identification of non-equilibrium Floquet symmetry-protected topological phases. Experimental results reveal that the predictive surrogates not only reduce measurement overhead by orders of magnitude, but can also surpass the performance of conventional, quantum-resource-intensive approaches. Collectively, these findings establish predictive surrogates as a practical pathway to broadening the impact of advanced quantum processors.

Problem

• Reducing quantum processor access with classical surrogates
• Emulating large-scale quantum processors efficiently
• Lowering measurement overhead in quantum simulations

Innovation

• Classical models emulate quantum processor behavior
• Reduce quantum access need in practical scenarios
• Enable efficient pre-training for quantum simulations