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arXiv:2609.10932 · Physical Review Applied

Distributed variational quantum computing with deterministic entanglement tuning

Ilhwan Kim, Yong-Su Kim, Kwang Jo Lee, Hyukjoon Kwon, Yosep Kim, Hyang-Tag Lim

quant-phSubmitted Sep 10, 2026• Announced Sep 11, 2026• 0 citations
arXivHTMLPDF

Abstract

Distributed quantum computing offers a scalable route to quantum information processing by entangling spatially separated processors. Although gate teleportation enables universal computation across distributed nodes, it requires repeated consumption of high-fidelity Bell pairs, ancillary qubits, and real-time feedforward, which imposes significant overhead and reduces fidelity. However, many variational quantum algorithms do not demand full universality; rather, they rely on sufficient expressibility to explore solution spaces effectively. Building on this, we propose a distributed variational quantum computing protocol based on deterministic entanglement tuning. In contrast to probabilistic filtering, our approach deterministically modulates pre-shared entanglement using only local operations and classical communication. We validate the protocol through a proof-of-principle experiment by estimating ground-state energies of the He-H$^+$ molecule and the Schwinger model, showing that diverse entanglement levels can be engineered to match problem-specific requirements. Our results suggest a practical alternative for near-term distributed quantum applications.

Journal reference
Physical Review Applied 26, 034017 (2026)
DOI
10.1103/9pj6-prlz
Comments
8 pages, 4 figures, Supplemental Material

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Distributed quantum computing offers a scalable route to quantum information processing by entangling spatially separated processors. Although gate teleportation enables universal computation across distributed nodes, it requires repeated consumption of high-fidelity Bell pairs, ancillary qubits, and real-time feedforward, which imposes significant overhead and reduces fidelity. However, many variational quantum algorithms do not demand full universality; rather, they rely on sufficient expressibility to explore solution spaces effectively. Building on this, we propose a distributed variational quantum computing protocol based on deterministic entanglement tuning. In contrast to probabilistic filtering, our approach deterministically modulates pre-shared entanglement using only local operations and classical communication. We validate the protocol through a proof-of-principle experiment by estimating ground-state energies of the He-H+^++ molecule and the Schwinger model, showing that diverse entanglement levels can be engineered to match problem-specific requirements. Our results suggest a practical alternative for near-term distributed quantum applications.
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.
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Distributed quantum optimisation is challenging because computing the problem cost function across multiple quantum processors requires non-local gates, which can incur overhead in latency and fidelity. Here we introduce QESTO, a distributed variational ansatz for graph-based discrete optimisation that requires only persistent pre-shared Bell pairs for remote operations. Using local operations, it encodes local constraint information in the Bell pairs that is leveraged to produce amplitude transfer towards globally valid distributed solution states. QESTO requires one Bell pair per distributed edge of the problem graph and, after initialisation of the Bell states, uses no non-local gates. On two bounded weighted Wang tile-matching problem ensembles, QESTO achieves stronger convergence to low-cost tilings than equivalently partitioned QAOA with no distributed gates at ansatz depths of two or higher, and exceeds the mean performance of monolithic QAOA at the deepest studied depth in both ensembles. These results suggest that persistent entanglement can support useful variational communication while reducing per-layer non-local gate overhead.