Authors :
Aruna Thethali; Kranthi Kiran Mandava
Volume/Issue :
Volume 10 - 2025, Issue 8 - August
Google Scholar :
https://tinyurl.com/2s3e6vxp
Scribd :
https://tinyurl.com/4wkzdf68
DOI :
https://doi.org/10.38124/ijisrt/25aug550
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Abstract :
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.
Keywords :
Neuromorphic Computing,Quantum State Representation,Energetic Signature,BrainScaleS-2,Spiking Neural Networks,Mixed-Signal Architecture,Eigenergy Components.
References :
- S. Czischek, A. Baumbach, S. Billaudelle, B. Cramer et al., "Spiking neuromorphic chip learns entangled quantum states," 2020. [PDF]
- D. S. Assi, H. Huang, V. Karthikeyan, V. C. S. Theja et al., "Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems," 2023. ncbi.nlm.nih.gov
- R. Klassert, A. Baumbach, M. A. Petrovici, and M. Gärttner, "Variational learning of quantum ground states on spiking neuromorphic hardware," 2022. ncbi.nlm.nih.gov
- S. M. Bezrukov and L. B. Kish, "Deterministic multivalued logic scheme for information processing and routing in the brain," 2009. [PDF]
- G. Castagnoli, "Quantum computation and the physical computation level of biological information processing," 2009. [PDF]
- P. A. van der Helm, "Transparallel mind: Classical computing with quantum power," 2014. [PDF]
- G. Wendin, "Can biological quantum networks solve NP-hard problems?," 2019. [PDF]
- K. Schmidt, J. Culbertson, C. Cox, H. S. Clouse et al., "What is it Like to Be a Bot: Simulated, Situated, Structurally Coherent Qualia (S3Q) Theory of Consciousness," 2021. [PDF]
- J. Keppler, "The Role of the Brain in Conscious Processes: A New Way of Looking at the Neural Correlates of Consciousness," 2018. ncbi.nlm.nih.gov
- A. Ulhaq, "Neuromorphic Correlates of Artificial Consciousness," 2024. [PDF]
- D. Aur, "Can we build a conscious machine?," 2014. [PDF]
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.
Keywords :
Neuromorphic Computing,Quantum State Representation,Energetic Signature,BrainScaleS-2,Spiking Neural Networks,Mixed-Signal Architecture,Eigenergy Components.
