1Xanadu, Toronto, ON, M5G 2C8, Canada
2Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
3Center for Computational Quantum Physics, Flatiron Institute, New York, NY, 10010, USA
4Department of Physics, Columbia University, New York, 10027, USA
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Abstract
We propose a new method to extend the size of a quantum computation beyond the number of physical qubits available on a single device. This is accomplished by randomly inserting measure-and-prepare channels to express the output state of a large circuit as a separable state across distinct devices. Our method employs randomized measurements, resulting in a sample overhead that is $widetilde{O}(4^k / varepsilon ^2)$, where $varepsilon $ is the accuracy of the computation and $k$ the number of parallel wires that are “cut” to obtain smaller sub-circuits. We also show an information-theoretic lower bound of $Omega(2^k / varepsilon ^2)$ for any comparable procedure. We use our techniques to show that circuits in the Quantum Approximate Optimization Algorithm (QAOA) with $p$ entangling layers can be simulated by circuits on a fraction of the original number of qubits with an overhead that is roughly $2^{O(pkappa)}$, where $kappa$ is the size of a known balanced vertex separator of the graph which encodes the optimization problem. We obtain numerical evidence of practical speedups using our method applied to the QAOA, compared to prior work. Finally, we investigate the practical feasibility of applying the circuit cutting procedure to large-scale QAOA problems on clustered graphs by using a $30$-qubit simulator to evaluate the variational energy of a $129$-qubit problem as well as carry out a $62$-qubit optimization.
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► References
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Cited by
[1] Lirandë Pira and Chris Ferrie, “An Invitation to Distributed Quantum Neural Networks”, arXiv:2211.07056, (2022).
[2] Lukas Brenner, Christophe Piveteau, and David Sutter, “Optimal wire cutting with classical communication”, arXiv:2302.03366, (2023).
[3] Matthew DeCross, Eli Chertkov, Megan Kohagen, and Michael Foss-Feig, “Qubit-reuse compilation with mid-circuit measurement and reset”, arXiv:2210.08039, (2022).
[4] Christian Ufrecht, Maniraman Periyasamy, Sebastian Rietsch, Daniel D. Scherer, Axel Plinge, and Christopher Mutschler, “Cutting multi-control quantum gates with ZX calculus”, arXiv:2302.00387, (2023).
[5] Marvin Bechtold, Johanna Barzen, Frank Leymann, Alexander Mandl, Julian Obst, Felix Truger, and Benjamin Weder, “Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices”, arXiv:2302.01792, (2023).
[6] Ritajit Majumdar and Christopher J. Wood, “Error mitigated quantum circuit cutting”, arXiv:2211.13431, (2022).
[7] Daniel T. Chen, Zain H. Saleem, and Michael A. Perlin, “Quantum Divide and Conquer for Classical Shadows”, arXiv:2212.00761, (2022).
[8] Gideon Uchehara, Tor M. Aamodt, and Olivia Di Matteo, “Rotation-inspired circuit cut optimization”, arXiv:2211.07358, (2022).
[9] Carlos A. Riofrío, Oliver Mitevski, Caitlin Jones, Florian Krellner, Aleksandar Vučković, Joseph Doetsch, Johannes Klepsch, Thomas Ehmer, and Andre Luckow, “A performance characterization of quantum generative models”, arXiv:2301.09363, (2023).
[10] Diego Guala, Shaoming Zhang, Esther Cruz, Carlos A. Riofrío, Johannes Klepsch, and Juan Miguel Arrazola, “A practical overview of image classification with variational tensor-network quantum circuits”, arXiv:2209.11058, (2022).
The above citations are from SAO/NASA ADS (last updated successfully 2023-03-03 16:49:02). The list may be incomplete as not all publishers provide suitable and complete citation data.
On Crossref’s cited-by service no data on citing works was found (last attempt 2023-03-03 16:49:00).
This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.
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