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.
Near-term distributed quantum computation using mean-field corrections and auxiliary qubits
Abstract
Copyright and License
© 2024 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
This work was done during A M G's internship at NVIDIA. A M G acknowledges support from the NSF through the Graduate Research Fellowships Program (DGE 2140743) and from the Theodore H Ashford Fellowships in the Sciences. At CalTech, A A is supported in part by the Bren-endowed chair. S F Y thanks the AFOSR (FA9550-19-1-0233) and the NSF through the Qu-IDEAS HDR Institute (OAC-2118310), the CUA PFC (PHY-2317134), and Q-SEnSE QLCI (OMA-2016244) for funding.
Data Availability
The data that support the findings of this study will be openly available following an embargo at the following URL/DOI:https://github.com/amcclaingomez3/MF-DQC.
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Additional details
- Nvidia (United States)
- National Science Foundation
- NSF Graduate Research Fellowship DGE-2140743
- Harvard University
- Theodore H. Ashford Fellowship
- California Institute of Technology
- Bren Professor of Computing and Mathematical Sciences
- United States Air Force Office of Scientific Research
- FA9550-19-1-0233
- National Science Foundation
- OAC-2118310
- National Science Foundation
- PHY-2317134
- National Science Foundation
- OSI-2016244