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20242026
most citedAssessing and Advancing the Potential of Quantum Computing: A NASA Case Study

17 citations · 32 across the 11 of their papers we have counts for

collaborators

13 papers

quant-ph2026

Temporal information processing on a 4,500-qubit quantum annealer

Antonio Sannia, Roberto Menta, Pratik Sathe +7

Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and che…

quant-ph2026

Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting

Filip B. Maciejewski, Stuart Hadfield, Oscar Wallis +5

Progress towards a quantum advantage using known heuristic methods for combinatorial optimization is impeded by hardware noise and limited qubit count. Here, we propose a noise-awa…

quant-ph2026

Noise-Directed Adaptive Remapping for Integer Optimization: from qubits to (encoded) qudits

Stuart Hadfield, Filip B. Maciejewski, Davide Venturelli

We extend Noise-Directed Adaptive Remapping (NDAR), a recently proposed heuristic meta-algorithm that leverages device noise as a computational resource, to optimization problems o…

cs.LG2026

Sequential Reservoir Computing for Efficient High-Dimensional Spatiotemporal Forecasting

Ata Akbari Asanjan, Filip Wudarski, Daniel O'Connor +4

Forecasting high-dimensional spatiotemporal systems remains computationally challenging for recurrent neural networks (RNNs) and long short-term memory (LSTM) models due to gradien…

quant-ph2025

Quantum Sensing using Geometrical Phase in Qubit-Oscillator Systems

Nishchay Suri, Zhihui Wang, Tanay Roy +2

We present a quantum sensing protocol for coupled qubit-oscillator systems that surpasses the standard quantum limit (SQL) by exploiting a geometrical phase. The signal is encoded…

quant-ph2025

From quantum feature maps to quantum reservoir computing: perspectives and applications

Casper Gyurik, Filip Wudarski, Evan Philip +5

We explore the interplay between two emerging paradigms: reservoir computing and quantum computing. We observe how quantum systems featuring beyond-classical correlations and vast…

physics.ed-ph2025

The USRA Feynman Quantum Academy: If You Give a Student a Quantum Internship

Zoe Gonzalez Izquierdo, Sophie Block, Besart Mujeci +3

In the rapidly expanding field of quantum computing, one key aspect to maintain ongoing progress is ensuring that early career scientists interested in the field get appropriate gu…

quant-ph2024★ 7 cited

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

Masoud Mohseni, Artur Scherer, K. Grace Johnson +48

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become po…

quant-ph2024

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…

quant-ph2024★ 17 cited

Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study

Eleanor G. Rieffel, Ata Akbari Asanjan, M. Sohaib Alam +20

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum co…

cs.AI2024★ 2 cited

Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization

Mert Esencan, Tarun Advaith Kumar, Ata Akbari Asanjan +5

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial inte…

quant-ph2024★ 6 cited

Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping

Filip B. Maciejewski, Jacob Biamonte, Stuart Hadfield +1

We present Noise-Directed Adaptive Remapping (NDAR), a heuristic algorithm for approximately solving binary optimization problems by leveraging certain types of noise. We consider…

quant-ph2024

Uniformly Decaying Subspaces for Error Mitigated Quantum Computation

Nishchay Suri, Jason Saied, Davide Venturelli

We present a general condition to obtain subspaces that decay uniformly in a system governed by the Lindblad master equation and use them to perform error mitigated quantum computa…