TQml Simulator: optimized simulation of quantum machine learning

TL;DR

The TQml Simulator is a high-performance quantum circuit simulator optimized for quantum machine learning, using tailored simulation techniques for different layers to significantly outperform existing simulators.

Contribution

It introduces a novel simulation approach that dynamically selects the most efficient method per layer, boosting simulation speed for quantum machine learning circuits.

Findings

Outperforms Pennylane's default simulator by up to 10x.

Performance gains depend on circuit complexity and hardware.

Tailored simulation methods improve forward and backward pass efficiency.

Abstract

Hardware-efficient circuits employed in Quantum Machine Learning are typically composed of alternating layers of uniformly applied gates. High-speed numerical simulators for such circuits are crucial for advancing research in this field. In this work, we numerically benchmark universal and gate-specific techniques for simulating the action of layers of gates on quantum state vectors, aiming to accelerate the overall simulation of Quantum Machine Learning algorithms. Our analysis shows that the optimal simulation method for a given layer of gates depends on the number of qubits involved, and that a tailored combination of techniques can yield substantial performance gains in the forward and backward passes for a given circuit. Building on these insights, we developed a numerical simulator, named TQml Simulator, that employs the most efficient simulation method for each layer in a given circuit. We evaluated TQml Simulator on circuits constructed from standard gate sets, such as rotations and CNOTs, as well as on native gates from IonQ and IBM quantum processing units. In most cases, our simulator outperforms equivalent Pennylane's default.qubit simulator by up to a factor of 10, depending on the circuit, the number of qubits, the batch size of the input data, and the hardware used.