Quantum eigenstate broadcasting assisted by a coherent link
Benjamin F. Schiffer, Jordi Tura

TL;DR
This paper introduces a quantum eigenstate broadcasting method that leverages limited entanglement between distributed quantum devices to efficiently prepare Hamiltonian eigenstates with reduced circuit depth and improved suppression of unwanted amplitudes.
Contribution
The authors propose a novel distributed quantum eigenstate preparation protocol that uses minimal entanglement to enhance efficiency and suppress errors more effectively than single-device methods.
Findings
Reduced circuit depth in distributed eigenstate preparation.
Improved suppression of unwanted amplitudes to approximately 0.30.
Requires only a single auxiliary qubit per device for entanglement.
Abstract
Preparing the ground state of a local Hamiltonian is a crucial problem in understanding quantum many-body systems, with applications in a variety of physics fields and connections to combinatorial optimization. While various quantum algorithms exist which can prepare the ground state with high precision and provable guarantees from an initial approximation, current devices are limited to shallow circuits. Here we consider the setting where Alice and Bob, in a distributed quantum computing architecture, want to prepare the same Hamiltonian eigenstate. We demonstrate that the circuit depth of the eigenstate preparation algorithm can be reduced when the devices can share limited entanglement. Especially so in the case where one of them has a near-perfect eigenstate, which is more efficiently broadcast to the other device. Our approach requires only a single auxiliary qubit per device to be…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Neural Networks and Reservoir Computing
