**4. Challenges of neuromorphic computing**

Conventional CMOS-based neuromorphic systems have shown promise in delivering brain-like features including pattern recognition, adaptive learning, and sophisticated sensing. However, because of insurmountable problems inherent in the material qualities of conventional semiconductors, their future potential is constrained. For creating a system that imitates a biological brain, quantum materials and technologies are anticipated to function as a ground-breaking, next-generation computational platform. This field is at a point where further fundamental investigation into every facet of the issue would be extremely beneficial. This issue is devoted to evaluating the state of the art in the field and exhibiting fresh, possibly ground-breaking concepts.

Ab initio theoretical calculations combined with state-of-the-art synthesis and nanoscale structural, electrical, magnetic, and optical characterisation are some of the techniques that will be used to better understand the properties of quantum materials, the impact of defects, and their ultimate effect on devices and systems. It is crucial to conduct a thorough, quantitative, interdisciplinary analysis of the relevant materials under intense (electrical, thermal, magnetic, and physical) stresses. Modern technologies for synthesis and characterisation are now at a point where they can give precise control and information about a material's structure and how it affects its physical properties.

With a full understanding of the properties of the materials, new design ideas that go beyond typical semiconductor devices based on the charge or even the spin of the electron are being developed. For instance, Mott physics offers a way to simulate technology that is inspired by the brain. Synaptic devices encode a memory state by shifting and modifying the concentration of defects, whereas "neuronal" devices accumulate "metal phase" and modulate the conduction by metallic filamentary development (e.g., oxygen vacancies). In general, the combination of memristive phenomena with light-sensitive oxides is leading to exciting design ideas for memory, networks, and neuro-sensors. Polymer-gated transistors with numerous functionalities can be used to achieve synaptic behaviour, which calls for plasticity in a material's response function, as well as in photonic and magnetoelectric systems. Spin-based devices, on the other hand, benefit from pronounced nonlinearities in the magnetic responses of quantum materials. Optomagnetic neural networks can be adjusted to construct low-dissipation networks thanks to the low-energy plasticity and non-volatility of magnetic properties, while magnetic anisotropies are proposed to train networks. Nanoscale magnetic phenomena can also be used to create nanowires. Time-dependent responses that resemble synaptic and dendritic trees as well as neural spikes can be produced by superconducting Josephson devices. Many physical phenomena, such reservoir computing and in-memory computing, are being studied as avenues to duplicate the magnificent processes carried out by the brain. It is crucial to comprehend the ultimate physical limits of these phenomena, including the smallest sizes, shortest times, or closest physical proximity permitted by the physics of the materials and devices; all of these are directly related to scaling issues. Only then can these phenomena be incorporated into functional devices.

Emergent behaviour is at the core of biological neurons' and synapses' complexity and remarkable effectiveness. Numerous self-organising principles may be seen in the emergent properties of quantum materials and their nonlinearities, which offer a variety of static and dynamic states helpful for simulating sophisticated brain devices and networks. [27] A diversity of different memory states, fine-tuning of critical behaviour, and the formation of unique collective phenomena are possible thanks to the metastability provided by the complex energy landscape of the quantum system [26, 28]. It is crucial to take into account the network in which the devices are located in order to approach the problem of building a brain-like machine. The network is the system, to adapt Herbert Kroemer's aphorism to bio-inspired neuromorphic computing.
