QubitONeuron Private Limited
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QubitONeuron Private Limited
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We work across molecular bioinformatics, quantum-inspired learning and specialised compute — developing new ways to search biological sequence space while keeping the implementation itself behind the laboratory door.
We map iterative scientific and optimisation workloads onto FPGAs using parallel datapaths, pipelining, custom numerical precision and on-chip memory — targeting predictable low-latency and energy-efficient execution.
We study antimicrobial-peptide sequence space through charge per residue (net charge/length), hydrophobicity, hydrophobic moment and residue composition. Our current focus is charge-aware screening: identifying activity-relevant sequence patterns and prioritising candidates for deeper computational or experimental evaluation.
Current frontier — antimicrobial peptide screening & candidate prioritisation
Research screening only — not a clinical or therapeutic decision system
We investigate hybrid classical–quantum-inspired models that combine variational state representations with simulated quantum observables to learn nonlinear relationships in peptide-derived representations. The quantum operations are simulated on classical hardware and integrated with practical machine learning.