Short-depth QAOA circuits and quantum annealing on higher-order ising models

Pelofske, Elijah; Bärtschi, Andreas; Eidenbenz, Stephan

doi:10.1038/s41534-024-00825-w

Title: Short-depth QAOA circuits and quantum annealing on higher-order ising models

Journal Article · 11 March 2024 · npj Quantum Information

DOI: https://doi.org/10.1038/s41534-024-00825-w · OSTI ID:2323389

; Bärtschi, Andreas; Eidenbenz, Stephan

Abstract We present a direct comparison between QAOA (Quantum Alternating Operator Ansatz), and QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p = 1, 2 rounds is executed on the 127 qubit heavy-hex graph gate-model quantum computer ibm_washington, using on-device grid-searches for angle finding, and QA is executed on two Pegasus-chip D-Wave quantum annealers. The problems are random Ising models whose connectivity matches heavy-hex graphs and the Pegasus graph connectivity, and optionally include hardware-compatible cubic terms ( Z Z Z terms). The QAOA circuits are heavily optimized and of extremely short depth, with a CNOT depth of 6 per round, which allows whole chip usage of the heavy-hex lattice. QAOA and QA are both compared against simulated annealing and the optimal solutions are computed exactly using CPLEX. The noiseless mean QAOA expectation values for p = 1, 2 are computed using classical light-cone based simulations. We find QA outperforms QAOA on the evaluated devices.

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Sponsoring Organization:: USDOE

OSTI ID:: 2323389

Alternate ID(s):: OSTI ID: 2332785

Journal Information:: npj Quantum Information, Journal Name: npj Quantum Information Journal Issue: 1 Vol. 10; ISSN 2056-6387

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Error mitigation extends the computational reach of a noisy quantum processor Kandala, Abhinav; Temme, Kristan; Córcoles, Antonio D. Nature, Vol. 567, Issue 7749 https://doi.org/10.1038/s41586-019-1040-7	journal	March 2019
Quantum Annealing vs. QAOA: 127 Qubit Higher-Order Ising Problems on NISQ Computers Pelofske, Elijah; Bärtschi, Andreas; Eidenbenz, Stephan Lecture Notes in Computer Science https://doi.org/10.1007/978-3-031-32041-5_13	book	May 2023
Noise-induced barren plateaus in variational quantum algorithms Wang, Samson; Fontana, Enrico; Cerezo, M. Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-27045-6	journal	November 2021
Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial Optimization Sanders, Yuval R.; Berry, Dominic W.; Costa, Pedro C. S. PRX Quantum, Vol. 1, Issue 2 https://doi.org/10.1103/PRXQuantum.1.020312	journal	November 2020
Performance of the Quantum Approximate Optimization Algorithm on the Maximum Cut Problem Crooks, Gavin E. arXiv https://doi.org/10.48550/arXiv.1811.08419	preprint	January 2018
Twisted hybrid algorithms for combinatorial optimization Caha, Libor; Kliesch, Alexander; Koenig, Robert Quantum Science and Technology, Vol. 7, Issue 4 https://doi.org/10.1088/2058-9565/ac7f4f	journal	August 2022
Demonstration of Fidelity Improvement Using Dynamical Decoupling with Superconducting Qubits Pokharel, Bibek; Anand, Namit; Fortman, Benjamin Physical Review Letters, Vol. 121, Issue 22 https://doi.org/10.1103/PhysRevLett.121.220502	journal	November 2018
The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: A Typical Case Farhi, Edward; Gamarnik, David; Gutmann, Sam arXiv https://doi.org/10.48550/arXiv.2004.09002	preprint	January 2020
Large-Scale Quantum Approximate Optimization via Divide-and-Conquer Li, Junde; Alam, Mahabubul; Ghosh, Swaroop IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 42, Issue 6 https://doi.org/10.1109/TCAD.2022.3212196	journal	June 2023
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Max-cut Clustering Utilizing Warm-Start QAOA and IBM Runtime Beaulieu, Daniel; Pham, Anh arXiv https://doi.org/10.48550/arXiv.2108.13464	preprint	January 2021
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