A quantum computer that can learn like a brain, using the timing of signals rather than brute calculation, is the goal of this project. Today’s quantum devices are noisy, error-prone, and difficult to program. Neuromorphic engineering—which mimics the brain’s energy-efficient, time-based signalling—offers a way around these limits. The project will test whether the randomness inherent in quantum hardware can be harnessed as a feature, not a bug, to build models that can quantify their own uncertainty. This would address a critical gap: current quantum computers cannot reliably tell users how confident they should be in a result. If successful, the work could produce quantum neural networks that run on existing noisy devices, making them useful for tasks like graph analysis or attention-based processing. This is fundamental science—there is no immediate consumer application. But similar work on neuromorphic chips has already slashed energy use in edge computing. A deeper understanding of how quantum noise mimics neural timing could eventually lead to more reliable decision-making machines in logistics, diagnostics, or infrastructure monitoring, where knowing the confidence of a prediction matters as much as the prediction itself.
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Mimicking the operation of biological brains, neuromorphic engineering (NE) aims at improving the energy efficiency of computational tasks that can be carried out by encoding information with time. Recently, time-encoded spiking models have found widespread applications for the implementation of attention mechanisms in transformers, as the detection of timing coincidences can efficiently replace attention weights. Neuromorphic architectures also offer a natural platform to study probabilistic computing applications (PC), e.g. Monte Carlo simulations and Bayesian Neural Networks, whose inherent stochasticity, in turn, makes them ideal choices to interface with nascent quantum computing (QC) devices. Quantum models can effectively propagate timing information by leveraging noisy gates and state relaxations, making spiking neurons promising candidate computational models for noisy intermediate scale quantum (NISQ) computers. This project will explore to what extent the principles of neuromorphic modelling can be embedded or applied to quantum devices, in two-fold directions. - One focus will be uncertainty, expressivity or trainability quantification of NISQ applications: by leveraging the inherent randomness of the computational substrate, a central working principle in neuromorphic engineering, we will explore further the implications of parallelisms across NE and digital/analog parameterised Quantum Circuits. A grand goal in this direction would be producing reliable decision-making machines that can quantify their own uncertainty. - In parallel, novel quantum neuromorphic models could be found, ranging from attention-based transformers, to more general graph neural networks, or hybrid intermediate representations for NISQ circuits, with the target of increasing the usability and applicability of (particularly analog) NISQ devices.
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