17 citations · 17 across the 3 of their papers we have counts for
10 papers
Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
Joao F. Doriguello
Reinforcement learning is a subfield of machine learning that studies how an agent interacts with an environment in order to extract as large a reward as possible. A standard appro…
Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
Jonathan Allcock, Joao F. Doriguello, Gábor Ivanyos +1
Bell sampling is a simple yet powerful tool based on measuring two copies of a quantum state in the Bell basis, and has found applications in a plethora of problems related to stab…
A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model
Andris Ambainis, Joao F. Doriguello, Debbie Lim
We propose novel classical and quantum online algorithms for learning finite- and infinite-horizon Markov Decision Processes (MDPs). Our algorithms are based on a hybrid online-off…
On the practicality of quantum sieving algorithms for the shortest vector problem
Joao F. Doriguello, George Giapitzakis, Alessandro Luongo +1
One of the main candidates of post-quantum cryptography is lattice-based cryptography. Its cryptographic security against quantum attackers is based on the worst-case hardness of l…
Beyond Bell sampling: stabilizer state learning and quantum pseudorandomness lower bounds on qudits
Jonathan Allcock, Joao F. Doriguello, Gábor Ivanyos +1
Bell sampling is a simple yet powerful measurement primitive that has recently attracted a lot of attention, and has proven to be a valuable tool in studying stabiliser states. Unf…
Quantum generalizations of Glauber and Metropolis dynamics
András Gilyén, Chi-Fang Chen, Joao F. Doriguello +1
Classical Markov Chain Monte Carlo methods have been essential for simulating statistical physical systems and have proven well applicable to other systems with many degrees of fre…