Abstract
Present-day, noisy, small or intermediate-scale quantum processors---although far from fault-tolerant---support the execution of heuristic quantum algorithms, which might enable a quantum advantage, for example, when applied to combinatorial optimization problems. On small-scale quantum processors, validations of such algorithms serve as important technology demonstrators. We implement the quantum approximate optimization algorithm (QAOA) on our hardware platform, consisting of two superconducting transmon qubits and one parametrically modulated coupler. We solve small instances of the NP-complete exact-cover problem, with 96.6% success probability, by iterating the algorithm up to level two.
Highlights
Quantum computing promises exponential computational speedup in a number of fields, such as cryptography, quantum simulation, and linear algebra [1]
Fault-tolerant quantum computer is still many years away, impressive progress has been made over the last decade using superconducting circuits [2,3,4], leading to the noisy intermediate-scale quantum (NISQ) era [5]
It was predicted that NISQ devices should allow for “quantum supremacy” [6], that is, solving a problem that is intractable on a classical computer in a reasonable time
Summary
Quantum computing promises exponential computational speedup in a number of fields, such as cryptography, quantum simulation, and linear algebra [1]. It was predicted that NISQ devices should allow for “quantum supremacy” [6], that is, solving a problem that is intractable on a classical computer in a reasonable time This was recently demonstrated on a 53-qubit processor by sampling the output distributions of random circuits [7]. Applying the QAOA to instances of the exact-cover problem extracted from real-world data in the context of tail assignment has been numerically studied with 25 qubits, corresponding to 25 routes and 278 flights [18]. In order to find the solution to the optimization problem, it is not necessary for |γ ∗, β∗ to be equal to the ground state: as long as the ground-state probability is high enough, the quantum processor can be used to generate a shortlist of potential solutions that can be checked efficiently (in polynomial time) on a classical computer. The ground states are degenerate for problems B and D
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