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Chin-Yi Lin

Publications and source records attributed to Chin-Yi Lin.

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Quantum Simulation of Dissipative Energy Transfer via Noisy Quantum Computer

We study whether dissipative energy-transfer dynamics can be simulated on noisy near-term quantum hardware by treating device noise as a calibrated resource rather than purely as an error source. Focusing on a biased exciton dimer, we encode the single-excitation manifold into a two-qubit subspace and implement the coherent dynamics through a shallow Trotterized propagator, while repeated noisy identity operations provide an effective dissipative channel. We benchmark the resulting short-time population dynamics against the hierarchical equations of motion (HEOM), which serves as a numerically accurate reference for the corresponding open-system model. On IBM quantum hardware, the calibrated noisy circuit reproduces a broad range of dissipative trajectories in the tested regime, and the fitted HEOM parameters exhibit an approximately linear dependence on the noisy-gate frequency. This empirical relation enables a practically useful interpolation strategy: once calibrated by a finite set of HEOM calculations, the noisy circuit can replace repeated HEOM fitting for intermediate parameter points within the same biased-dimer family. To extend the dynamics beyond the circuit-depth limit, we combine the short-time quantum data with the transfer tensor method (TTM). In simulator studies, TTM accurately extends the dynamics well beyond the directly simulated window, whereas on real hardware its performance is limited by the instability of coherence-sensitive initial states. Our results show that noisy few-qubit devices can act as calibrated phenomenological simulators of open-system dynamics and, within a restricted but experimentally relevant regime, can provide a practical surrogate for repeated HEOM-based modeling.

quant-ph

Quantum Speedups for Multiproposal MCMC

Multiproposal Markov chain Monte Carlo (MCMC) algorithms choose from multiple proposals to generate their next chain step in order to sample from challenging target distributions more efficiently. However, on classical machines, these algorithms require $\mathcal{O}(P)$ target evaluations for each Markov chain step when choosing from $P$ proposals. Recent work demonstrates the possibility of quadratic quantum speedups for one such multiproposal MCMC algorithm. After generating $P$ proposals, this quantum parallel MCMC (QPMCMC) algorithm requires only $\mathcal{O}(\sqrt{P})$ target evaluations at each step, outperforming its classical counterpart. However, generating $P$ proposals using classical computers still requires $\mathcal{O}(P)$ time complexity, resulting in the overall complexity of QPMCMC remaining $\mathcal{O}(P)$. Here, we present a new, faster quantum multiproposal MCMC strategy, QPMCMC2. With a specially designed Tjelmeland distribution that generates proposals close to the input state, QPMCMC2 requires only $\mathcal{O}(1)$ target evaluations and $\mathcal{O}(\log P)$ qubits when computing over a large number of proposals $P$. Unlike its slower predecessor, the QPMCMC2 Markov kernel (1) maintains detailed balance exactly and (2) is fully explicit for a large class of graphical models. We demonstrate this flexibility by applying QPMCMC2 to novel Ising-type models built on bacterial evolutionary networks and obtain significant speedups for Bayesian ancestral trait reconstruction for 248 observed salmonella bacteria.

quant-ph