Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor

Abstract

Using a quantum processor to embed and process classical data enables the generation of correlations between variables that are inefficient to represent through classical computation. A fundamental question is whether these correlations could be harnessed to enhance learning performances on real datasets. Here, we report the use of a neutral atom quantum processor comprising up to $32$ qubits to implement machine learning tasks on graph-structured data. To that end, we introduce a quantum feature map to encode the information about graphs in the parameters of a tunable Hamiltonian acting on an array of qubits. Using this tool, we first show that interactions in the quantum system can be used to distinguish non-isomorphic graphs that are locally equivalent. We then realize a toxicity screening experiment, consisting of a binary classification protocol on a biochemistry dataset comprising $286$ molecules of sizes ranging from $2$ to $32$ nodes, and obtain results which are comparable to those using the best classical kernels. Using techniques to compare the geometry of the feature spaces associated with kernel methods, we then show evidence that the quantum feature map perceives data in an original way, which is hard to replicate using classical kernels.

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Cited by in corpus (9)

• The state of quantum computing applications in health and medicine
• Quantum utility -- definition and assessment of a practical quantum advantage
• Qudit Machine Learning
• Supervised Learning Guarantee for Quantum AdaBoost
• Ground state-based quantum feature maps
• Spin-glass quantum phase transition in amorphous arrays of Rydberg atoms
• Quantum fidelity kernel with a trapped-ion simulation platform
• Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification
• Attributed-graphs kernel implementation using local detuning of neutral-atoms Rydberg Hamiltonian