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Quantum Machine Learning Quantum Compilation Routing Architecture

Scaling Quantum Computations via Gate Virtualization

arXiv
Authors: Nathaniel Tornow, Emmanouil Giortamis, Pramod Bhatotia

Year

2024

Paper ID

66070

Status

Preprint

Abstract Read

~2 min

Abstract Words

179

Citations

N/A

Abstract

We present the Quantum Virtual Machine (QVM), an end-to-end generic system for scalable execution of large quantum circuits with high fidelity on noisy and small quantum processors (QPUs) by leveraging gate virtualization. QVM exposes a virtual circuit intermediate representation (IR) that extends the notion of quantum circuits to incorporate gate virtualization. Based on the virtual circuit as our IR, we propose the QVM compiler - an extensible compiler infrastructure to transpile a virtual circuit through a series of modular optimization passes to produce a set of optimized circuit fragments. Lastly, these transpiled circuit fragments are executed on QPUs using our QVM runtime - a scalable and distributed infrastructure to virtualize and execute circuit fragments on a set of distributed QPUs. We evaluate QVM on IBM's 7- and 27-qubit QPUs. Our evaluation shows that using our system, we can scale the circuit sizes executable on QPUs up to double the size of the QPU while improving fidelity by 4.7times on average compared to larger QPUs and that we can effectively reduce circuit depths to only 40% of the original circuit depths.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • We present the Quantum Virtual Machine (QVM), an end-to-end generic system for scalable execution of large quantum circuits with high fidelity on noisy and small quantum...

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