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In reinforcement learning, it is often difficult to automate high-dimensional, rapid decision-making in dynamic environments, especially when domains require real-time online interaction and adaptive strategies such as web-based games. This…

Machine Learning · Computer Science 2024-05-30 Prabhath Reddy Gujavarthy

Two-dimensional constrained coding is a problem that is much more difficult than its one-dimensional counterpart. Indeed, in two dimensions, obtaining the answers to very natural questions becomes uncomputable. In particular, it is…

Signal Processing · Electrical Eng. & Systems 2019-04-17 Danny Dubé

Remote entanglement enables coordinated decision making without communication and produces correlations beyond those achievable by any classical strategy, representing a practical quantum advantage in time-critical distributed…

Quantum Physics · Physics 2026-04-10 Changhao Li , Seigo Kikura , Akihisa Goban , Hayata Yamasaki , Shinichi Sunami

In its normal form prisoners' dilemma (PD) is represented by a payoff matrix showing players strategies and payoffs. To obtain distinguishing trait and strategic form of PD certain constraints are imposed on the elements of its payoff…

Quantum Physics · Physics 2013-07-25 Ahmad Nawaz

The satisfiability problem for branching-time temporal logics like CTL*, CTL and CTL+ has important applications in program specification and verification. Their computational complexities are known: CTL* and CTL+ are complete for doubly…

Logic in Computer Science · Computer Science 2015-07-01 Oliver Friedmann , Martin Lange , Markus Latte

We recast move generators for solving board games as operations on compressed sets of strings. We aim for compressed representations with space sublinear in the number of game positions for interesting sets of positions, move generation in…

Data Structures and Algorithms · Computer Science 2025-04-15 Jeffrey Considine

Quantum technologies, i.e., technologies benefiting from the features of quantum physics such as objective randomness, superposition, and entanglement, have enabled an entirely different way of distributing and processing information. The…

Quantum Physics · Physics 2024-06-05 Lea Kopf , Markus Hiekkamäki , Shashi Prabhakar , Robert Fickler

Graph games played by two players over finite-state graphs are central in many problems in computer science. In particular, graph games with $\omega$-regular winning conditions, specified as parity objectives, which can express properties…

Logic in Computer Science · Computer Science 2018-03-20 Tomáš Brázdil , Krishnendu Chatterjee , Jan Křetínský , Viktor Toman

Quantum low-density parity-check (QLDPC) codes offer a promising route to scalable fault-tolerant quantum computation, but their performance under iterative decoding is strongly influenced by short-cycle structure and other harmful…

Information Theory · Computer Science 2026-05-05 Anthony Gómez-Fonseca , Gretchen L. Matthews , Kirsten D. Morris , Tefjol Pllaha

Quantum branching programs (quantum binary decision diagrams, respectively) are a convenient tool for examining quantum computations using only a logarithmic amount of space. Recently several types of restricted quantum branching programs…

Quantum Physics · Physics 2007-05-23 Matthias Homeister , Stephan Waack

We present a systematic investigation of the quantum games, constructed using a novel repeated game protocol, when played repeatedly ad infinitum. We focus on establishing that such repeated games -- by virtue of inherent quantum-mechanical…

Quantum Physics · Physics 2024-02-27 Archan Mukhopadhyay , Saikat Sur , Tanay Saha , Shubhadeep Sadhukhan , Sagar Chakraborty

While recent progress in quantum hardware open the door for significant speedup in certain key areas, quantum algorithms are still hard to implement right, and the validation of such quantum programs is a challenge. Early attempts either…

Programming Languages · Computer Science 2022-02-04 Christophe Chareton , Sébastien Bardin , François Bobot , Valentin Perrelle , Benoit Valiron

Neutral atom technologies have opened the door to novel theoretical advances in surface-code protocols for fault-tolerant quantum computation (FTQC), offering a compelling alternative to lattice surgery by leveraging transversal gates.…

Quantum Physics · Physics 2025-06-25 Shinichi Sunami , Akihisa Goban , Hayata Yamasaki

Reinforcement learning (RL) with limited samples is common in real-world applications. However, offline RL performance under this constraint is often suboptimal. We consider an alternative approach to dealing with limited samples by…

Machine Learning · Computer Science 2025-11-14 Outongyi Lv , Yewei Yuan , Nana Liu

Despite the successes of recent works in quantum reinforcement learning, there are still severe limitations on its applications due to the challenge of encoding large observation spaces into quantum systems. To address this challenge, we…

Quantum Physics · Physics 2021-07-12 Owen Lockwood , Mei Si

In this paper, we settle the sampling complexity of solving discounted two-player turn-based zero-sum stochastic games up to polylogarithmic factors. Given a stochastic game with discount factor $\gamma\in(0,1)$ we provide an algorithm that…

Machine Learning · Computer Science 2019-08-30 Aaron Sidford , Mengdi Wang , Lin F. Yang , Yinyu Ye

Protocols for quantum position verification (QPV) which combine classical and quantum information are insecure in the presence of loss. We study the exact loss-tolerance of the most popular protocol for QPV, which is based on BB84 states,…

Quantum Physics · Physics 2025-07-10 Llorenç Escolà-Farràs , Florian Speelman

We introduce Decision Tree Decoders (DTDs), which rely only on the sparsity of the binary check matrix, making them broadly applicable for decoding any quantum low-density parity-check (qLDPC) code and fault-tolerant quantum circuits. DTDs…

Quantum Physics · Physics 2025-02-25 Kai R. Ott , Bence Hetényi , Michael E. Beverland

We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. We…

Machine Learning · Computer Science 2017-11-09 Richard Y. Chen , Szymon Sidor , Pieter Abbeel , John Schulman