Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits

TL;DR

This paper introduces a near-term distributed quantum computing approach that uses mean-field corrections and auxiliary qubits to improve scalability and performance with limited quantum information transfer.

Contribution

It proposes an approximate distributed quantum computing scheme with mean-field corrections, auxiliary qubits, and circuit-cutting for variational algorithms, enhancing scalability and accuracy.

Findings

Reduces algorithmic error by orders of magnitude.

Requires fewer iterations for variational pre-training.

Extends performance with limited quantum information transfer.

Abstract

Distributed quantum computation is often proposed to increase the scalability of quantum hardware, as it reduces cooperative noise and requisite connectivity by sharing quantum information between distant quantum devices. However, such exchange of quantum information itself poses unique engineering challenges, requiring high gate fidelity and costly non-local operations. To mitigate this, we propose near-term distributed quantum computing, focusing on approximate approaches that involve limited information transfer and conservative entanglement production. We first devise an approximate distributed computing scheme for the time evolution of quantum systems split across any combination of classical and quantum devices. Our procedure harnesses mean-field corrections and auxiliary qubits to link two or more devices classically, optimally encoding the auxiliary qubits to both minimize short-time evolution error and extend the approximate scheme's performance to longer evolution times. We then expand the scheme to include limited quantum information transfer through selective qubit shuffling or teleportation, broadening our method's applicability and boosting its performance. Finally, we build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms. To characterize our technique, we introduce a non-linear perturbation theory that discerns the critical role of our mean-field corrections in optimization and may be suitable for analyzing other non-linear quantum techniques. This fragmented pre-training is remarkably successful, reducing algorithmic error by orders of magnitude while requiring fewer iterations.