9 papers · 1 filter
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…
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…
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…
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…
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…
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…