15 citations · 23 across the 16 of their papers we have counts for
17 papers
The Expressive Power of Constrained QAOA: What You Might Have MISsed
Boris Tsvelikhovskiy, Bao Bach, Ilya Safro
Feasibility-preserving mixer Hamiltonians offer an attractive alternative to penalty encodings for constrained quantum optimization, yet how enforcing constraints alters the state…
Efficient Estimation of Reduced QAOA Expressibility on Acyclic Graphs
Bao Bach, Boris Tsvelikhovskiy, Jose Falla +1
Classically equivalent formulations of an optimization problem need not lead to equivalent quantum algorithms. For MaxCut, fixing the value of a single vertex removes a simple glob…
Warm-Starting MaxCut Relaxation via Low-Depth Quantum Approximate Optimization Algorithm
Bao G. Bach, Ilya Safro, Filip B. Maciejewski
Quantum optimization has attracted growing interest as quantum hardware continues to improve, yet state-of-the-art classical solvers remain a formidable benchmark for practical uti…
Quantum Hypergraph Partitioning
Cameron Ibrahim, Bao G. Bach, Jad Salem +4
Quantum optimization algorithms are inherently probabilistic, yet they are most often used to search for a single high-quality solution. In this paper, we instead study hypergraph…
Scaling Qubit Mapping and Routing With Position Graph Abstraction and Memoization
Brent Russon, Bao Bach, Ed Younis +1
Scalable qubit mapping and routing remain major bottlenecks in quantum compilation, especially for Trapped-Ion Quantum Charge-Coupled device (TI-QCCD) architectures, where qubit in…
Learning Cut Distributions with Quantum Optimization
Bao Bach, Cameron Ibrahim, Reuben Tate +3
Many combinatorial optimization problems admit a maximin fairness variant, where the aim is to find a distribution over possible solutions which maximizes an expected worst-case ou…
Reductions of QAOA Induced by Classical Symmetries: Theoretical Insights and Practical Implications
Boris Tsvelikhovskiy, Bao Bach, Jose Falla +1
The performance of the Quantum Approximate Optimization Algorithm (QAOA) is closely tied to the structure of the dynamical Lie algebra (DLA) generated by its Hamiltonians, which de…
Solving Large-Scale QUBO with Transferred Parameters from Multilevel QAOA of low depth
Bao G Bach, Filip B. Maciejewski, Ilya Safro
The Quantum Approximate Optimization Algorithm (QAOA) is a promising quantum approach for tackling combinatorial optimization problems. However, hardware constraints such as limite…
Provably faster randomized and quantum algorithms for -means clustering via uniform sampling
Tyler Chen, Archan Ray, Akshay Seshadri +6
The -means algorithm (Lloyd's algorithm) is a widely used method for clustering unlabeled data. A key bottleneck of the -means algorithm is that each iteration requires time…
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach
Kien X. Nguyen, Bao Bach, Ilya Safro
Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates to achieve the quantum advantage in solving combinatorial optimization problems. The proce…
Efficient Compilation for Shuttling Trapped-Ion Machines via the Position Graph Architectural Abstraction
Bao Bach, Ilya Safro, Ed Younis
With the growth of quantum platforms for gate-based quantum computation, compilation holds a crucial role in deciding the success of the implementation. While there has been rich r…
QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa +2
In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuit…
A Multilevel Approach For Solving Large-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization
Filip B. Maciejewski, Bao Gia Bach, Maxime Dupont +5
Quantum approximate optimization is one of the promising candidates for useful quantum computation, particularly in the context of finding approximate solutions to Quadratic Uncons…
MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA
Bao Bach, Jose Falla, Ilya Safro
Learning the problem structure at multiple levels of coarseness to inform the decomposition-based hybrid quantum-classical combinatorial optimization solvers is a promising approac…
Simulating neutrino oscillations on a superconducting qutrit
Ha C. Nguyen, Bao G. Bach, Tien D. Nguyen +3
Precise measurements of parameters in the PMNS framework might lead to new physics beyond the Standard Model. However, they are incredibly challenging to determine in neutrino osci…
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization
Trong Duong, Sang T. Truong, Minh Tam +3
Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neu…
An FPGA-based Solution for Convolution Operation Acceleration
Trung Dinh Pham, Bao Gia Bach, Lam Trinh Luu +5
Hardware-based acceleration is an extensive attempt to facilitate many computationally-intensive mathematics operations. This paper proposes an FPGA-based architecture to accelerat…