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Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization
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Combinatorial optimization plays a crucial role in many industrial applications. While classical computing often struggles with complex instances, quantum optimization emerges as a promising alternative. Here, we present an enhanced bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm to address higher-order unconstrained binary optimization (HUBO). We apply BF-DCQO to a HUBO problem featuring three-local terms in the Ising spin-glass model, validated experimentally using 156 qubits on an IBM quantum processor. In the studied instances, our results outperform standard methods such as the quantum approximate optimization algorithm, quantum annealing, simulated annealing, and Tabu search. Furthermore, we provide numerical evidence of the feasibility of a similar HUBO problem on a 433-qubit Osprey-like quantum processor. Finally, we solve denser instances of the MAX 3-SAT problem in an IonQ emulator. Our results show that BF-DCQO offers an effective path for solving large-scale HUBO problems on current and near-term quantum processors.
Forward citations
Cited by 6 Pith papers
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BF-DCQO on IonQ's trapped-ion processors solves dense HUBO instances (protein folding up to 33 qubits, MAX 4-SAT and spin-glasses at 36 qubits) when followed by classical post-processing.
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Runtime Quantum Advantage with Digital Quantum Optimization
BF-DCQO on IBM's 156-qubit Heron hardware reaches approximate solutions to crafted HUBO instances faster than simulated annealing and CPLEX, but the comparisons rest on selectively chosen instances and assumed runtimes.
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Hybrid CVaR and IQP quantum workflows find CPLEX-verified optimal solutions for mRNA-folding QUBO instances up to 156 qubits, but simulated scaling shows steeply declining success rates.
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Replacing the full QAOA Hamiltonian with an approximate quadratic or SWAP-truncated version yields shallower circuits that can outperform standard QAOA under realistic noise.
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Branch-and-bound digitized counterdiabatic quantum optimization
A branch-and-bound wrapper around digitized counterdiabatic quantum optimization finds better or equal solutions to higher-order binary problems than simulated annealing, using fewer measured energy evaluations.
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Sequential Quantum Computing
Feeding a quantum annealer's approximate solutions into a digital quantum computer's counterdiabatic optimization finds the exact ground state of a 156-qubit problem that neither standalone machine found.
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