Variational quantum thermalization of environment-coupled DNA sequence biases
Understanding whether environmental conditions bias the statistical weight of specific genomic configurations requires a framework connecting molecular interactions, thermal fluctuations, and sequence-level observables. Here, we model DNA as an open quantum system coupled to an environmental thermal bath (represented as an external bosonic field) and parameterize the underlying molecular…
The study investigates whether environmental factors influence the statistical significance of certain genetic arrangements. To analyze DNA as an open quantum system interacting with a thermal environment, represented as a bosonic external field, the researchers parameterize molecular interaction energies based on particular nucleotide sequences.
The researchers employed two temperature-based methods: classical Bayesian optimization aiming for the precise thermodynamic equilibrium distribution (Gibbs state), and quantum simulation using a Variational Quantum Thermalizer (VQT) through a nine-layer parameterized quantum circuit. Both techniques optimized the same model of interaction energies and targeted a sequence objective across short DNA fragments (2 to 5 base pairs), utilizing 200 outer evaluations.
When compared to a distribution of equal likelihood, the classical optimization strategy increased target sequence probabilities by factors of 9.11, 25.64, 54.51, and 159.87. The VQT method also showed significant target sequence enrichment across all sequence lengths, enhancing probabilities between 9.85 and 62.77 times. By contrasting VQT-selected Hamiltonians with exact Gibbs calculations, the study separated errors in parameter learning from approximation errors in the variational quantum circuit.
While the conformance with the exact-Gibbs optimum was high for shorter sequences, longer sequences showed a greater reliance on variational thermal approximations. These findings indicate that environmental-induced sequence biases can be systematically analyzed using a variational quantum simulation framework, linking biophysical models of DNA thermodynamics with quantum algorithms for finite-temperature state preparation.
Importantly, the equilibrium probabilities reflect intrinsic thermodynamic biases, distinct from actual evolutionary substitution rates, which are still influenced by kinetic activation barriers, replication accuracy, and DNA repair mechanisms.
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