Progress towards analytically optimal angles in quantum approximate optimisation

Abstract

The Quantum Approximate Optimisation Algorithm is a $p$ layer, time-variable split operator method executed on a quantum processor and driven to convergence by classical outer loop optimisation. The classical co-processor varies individual application times of a problem/driver propagator sequence to prepare a state which approximately minimizes the problem's generator. Analytical solutions to choose optimal application times (called angles) have proven difficult to find, whereas outer loop optimisation is resource intensive. Here we prove that optimal Quantum Approximate Optimisation Algorithm parameters for $p=1$ layer reduce to one free variable and in the thermodynamic limit, we recover optimal angles. We moreover demonstrate that conditions for vanishing gradients of the overlap function share a similar form which leads to a linear relation between circuit parameters, independent on the number of qubits. Finally, we present a list of numerical effects, observed for particular system size and circuit depth, which are yet to be explained analytically.

arXiv comment10 pages, 1 figure

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Cited by in corpus (6)

• Graph neural network initialisation of quantum approximate optimisation
• Scaling Quantum Approximate Optimization on Near-term Hardware
• Mixer-Phaser Ansätze for Quantum Optimization with Hard Constraints
• Problem-Size Independent Angles for a Grover-Driven Quantum Approximate Optimization Algorithm
• Mitigating Quantum Gate Errors for Variational Eigensolvers Using Hardware-Inspired Zero-Noise Extrapolation
• Role of overparametrization in quantum approximate optimization