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20072025
most citedMilestones toward Majorana-based quantum computing

498 citations · 1.4k across the 30 of their papers we have counts for

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7 papers · 1 filter

quant-ph2025

Excising dead components in the surface code using minimally invasive alterations: A performance study

Ryan V. Mishmash, Vadym Kliuchnikov, Juan Bello-Rivas +7

The physical implementation of a large-scale error-corrected quantum processor will necessarily need to mitigate the presence of defective (thereby "dead") physical components in i…

quant-ph2025★ 5 cited

Roadmap to fault tolerant quantum computation using topological qubit arrays

David Aasen, Morteza Aghaee, Zulfi Alam +179

We describe a concrete device roadmap towards a fault-tolerant quantum computing architecture based on noise-resilient, topologically protected Majorana-based qubits. Our roadmap e…

quant-ph2023★ 10 cited

Improved Pairwise Measurement-Based Surface Code

Linnea Grans-Samuelsson, Ryan V. Mishmash, David Aasen +5

We devise a new realization of the surface code on a rectangular lattice of qubits utilizing single-qubit and nearest-neighbor two-qubit Pauli measurements and three auxiliary qubi…

quant-ph2023★ 19 cited

Hierarchical Clifford transformations to reduce entanglement in quantum chemistry wavefunctions

Ryan V. Mishmash, Tanvi P. Gujarati, Mario Motta +3

The performance of computational methods for many-body physics and chemistry is strongly dependent on the choice of basis used to cast the problem; hence, the search for better bas…

quant-ph2022★ 22 cited

Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection

Christa Zoufal, Ryan V. Mishmash, Nitin Sharma +6

We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as black box. Th…

quant-ph2022★ 83 cited

Quantum-enhanced Markov chain Monte Carlo

David Layden, Guglielmo Mazzola, Ryan V. Mishmash +4

Sampling from complicated probability distributions is a hard computational problem arising in many fields, including statistical physics, optimization, and machine learning. Quant…