On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks

TL;DR

This paper introduces a novel single-qubit feed-forward neural network architecture leveraging pulsed qubits' cyclical behavior, enabling scalable, efficient quantum-classical hybrid learning without extensive multi-qubit operations.

Contribution

It proposes a single-qubit based feed-forward block that uses classical parameters and pulse modulation, offering a linear scaling approach suitable for pulse-enabled quantum computers.

Findings

Single-qubit feed-forward blocks can mimic neural network behavior.

The approach scales linearly with the number of blocks.

The method is compatible with pulse-enabled quantum hardware.

Abstract

In the last two decades, the combination of machine learning and quantum computing has been an ever-growing topic of interest but, to this date, the limitations of quantum computing hardware have somewhat restricted the use of complex multi-qubit operations for machine learning. In this paper, we capitalize on the cyclical nature of quantum state probabilities observed on pulsed qubits to propose a single-qubit feed forward block whose architecture allows for classical parameters to be used in a way similar to classical neural networks. To do this, we modulate the pulses exciting qubits to induce superimposed rotations around the Bloch Sphere. The approach presented here has the advantage of employing a single qubit per block. Thus, it is linear with respect to the number of blocks, not polynomial with respect to the number of neurons as opposed to the majority of methods elsewhere. Further, since it employs classical parameters, a large number of iterations and updates at training can be effected without dwelling on coherence times and the gradients can be reused and stored if necessary. We also show how an analogy can be drawn to neural networks using sine-squared activation functions and illustrate how the feed-forward block presented here may be used and implemented on pulse-enabled quantum computers.