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Kuo-Chin Chen

Publications and source records attributed to Kuo-Chin Chen.

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Quantum Walks on Simplicial Complexes and Harmonic Homology: Application to Topological Data Analysis with Superpolynomial Speedups

This work investigates whether quantum walks on simplicial complexes exhibit quantum advantages. We introduce a novel quantum walk that encodes the combinatorial Laplacian, a key object reflecting the topology of the simplicial complex. We construct a unitary encoding projecting onto the kernel of the Laplacian, representing the harmonic cycles in the complex's homology. Our efficient construction of quantum walk unitaries for clique complexes paves the way for exploring higher-order interactions within topological structures. Our construction requires $O(n^3\log(1/ε)/λ_k)$ gates, where $n$ is the number of vertices, $λ_k$ is the smallest non-zero eigenvalue of the Laplacian, and $ε$ is the projection error. Our results indicate apparent superpolynomial quantum speedup with quantum walks, without quantum oracles, provided the spectral gap of the Laplacian is inverse-polynomially bounded and efficient simplex sampling is available. Crucially, the walk operates on a state space encompassing both positively and negatively oriented simplices, effectively doubling its size compared to unoriented approaches. Through coherent interference of these paired simplices, we are able to successfully encode the combinatorial Laplacian, which would otherwise be impossible. This is our major technical contribution. We also extend the framework by constructing variant quantum walks that enable us to: (1) estimate normalized persistent Betti numbers throughout a deformation process, (2) verify a specific QMA$_1$-hard problem related to clique complex homology, showcasing potential applications in computational complexity theory, and (3) solve the high-dimensional discrete Dirichlet problem (HDDP), generalizing the classical discrete Dirichlet problem on graphs to simplicial complexes, with an apparent superpolynomial speedup over the best known classical algorithm.

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

(Quantum) complexity of testing signed graph clusterability

This study examines clusterability testing for a signed graph in the bounded-degree model. Our contributions are two-fold. First, we provide a quantum algorithm with query complexity $\tilde{O}(N^{1/3})$ for testing clusterability, which yields a polynomial speedup over the best classical clusterability tester known [arXiv:2102.07587]. Second, we prove an $\tildeΩ(\sqrt{N})$ classical query lower bound for testing clusterability, which nearly matches the upper bound from [arXiv:2102.07587]. This settles the classical query complexity of clusterability testing, and it shows that our quantum algorithm has an advantage over any classical algorithm.

quant-ph