Energetic Signatures and Quantum States: Toward a Consciousness-Driven Architecture for Neuromorphic Computing

We introduce a neuromorphic approach to representing the energetic signature of a quantum state. In this scheme, a quantum Hamiltonian is decomposed into a linear combination of energies that we term eigenergy components. A more widely understood concept of eigenspectra emerges when the quantum state itself is an eigenstate of the Hamiltonian. Eigenenergy components of a general state are then obtained by factoring the corresponding energy eigenvalue with the associated eigenstate probability. The complex behavior of the components collectively defines the energetic signature of the state. From such a signature, the original quantum state can be recovered when energetic constraints are lifted. A solid-state mixed-signal implementation is described that leverages properties of the co-integrated analog neuromorphic–digital platform BrainScaleS-2, regarded as world-leading in neuromorphic hardware. Here, spiking neurons realize the linear coupling between components and quantum states and convert a quantum Hamiltonian to a temporal eigenergy distribution through an address-event representation. From this standard communication protocol, the eigenergy components—encoded temporally in the resulting spike pattern—are extracted again by downstream cores. In the neuromorphic output, an energetic signature is mapped to a population of leaky integrate-and-fire neurons that asynchronously evokes a corresponding spiking probability. The functionality of the architecture is demonstrated for Hamiltonians—sparse, dense, and low rank—that arise from models of bilayer graphene relationships [1]. Quantum states remain at the core of various means of information encoding and processing in domains such as communication, sensing, and computing. Algorithms in these domains are usually expressed in mathematical frameworks based on the axioms of quantum theory. Encoded data and subsequent manipulations are regarded as determined by a wholly different probabilistic rule than those found in classical digital computers [2]. Using standard digital hardware, access to states and associated operations therefore poses disproportionate challenges. Co-integrated digital-analog neuromorphic computing architectures present an alternative, reminiscent of single photons transmitting information through an array of gates on a linear-optical circuit. They qualify— as physically motivated data structures—for engineerable representations of quantum states. Mapping quantum systems directly to spiking activity and its propagation through dedicated emulation circuits offers a distinctively novel platform for processing quantum information that operates in a natural synchronous-to-asynchronous mode.