No quantum robot exists in the world today, meaning a real physical robot, controlled by a “quantum brain”. In this paper a humanoid robot was programmed based on quantum circuits, in contrast to classical animation methods or classical logic circuit robot controllers. The robot actor executes deterministic, probabilistic and quantum probabilistic behaviors which makes him more entertaining to the robot theatre audience.
No quantum robot exists in the world today, meaning a real physical robot, controlled by a “quantum brain”. In this paper a humanoid robot was programmed based on quantum circuits, in contrast to classical animation methods or classical logic circuit robot controllers. The robot actor executes deterministic, probabilistic and quantum probabilistic behaviors which makes him more entertaining to the robot theatre audience.
Today's robots are controlled with classical computers that make decisions based on algorithms designed for a classical computing architecture.However, the amount of data increases continuously -a fact which pushes classical computers that control these robots to their limits.In comparison to classical computing, fundamentally different concepts of physics are the basis of quantum computing and thereby quantum computing has the potential to solve specific computational problems more efficiently.In addition to the aforementioned complications, the subgroup of robots that are subject to the research of this paper, namely humanoid robots, not only need more computational capacity but also require an algorithmic design based on a behavioral model which avoids causing antipathy when interacting with human beings.Using a real quantum computer with quantum phenomena such as superposition, entanglement, and quantum parallelism, new behavioral models and algorithms can be designed to improve human robot interaction and speed up machine learning processes.This paper promotes a hybrid-controlled robot using the combination of a classical computer-and quantum computer.With four quantum circuits to define a behavioral model and three different quantum machine learning algorithms we show the advantage of the hybrid-controlled robot.
In this paper, we propose the concepts that apply quantum circuits to model Fuzzy Sets, thereby creating a new method to model behaviors for a humanoid robot. It is similar to the standard fuzzy sets from two points of view -1) of measured probabilities of state |1〉, and 2) that of the normalized measurement in a quantum ensemble computer. However, one can also look at the internal quantum states created by the operators of the new model. This extends fuzzy sets from [0, 1] interval to some other domain, similar to complex fuzzy logic, but also brings superposed, entangled and parallel quantum behaviors. The standard fuzzy logic aspect (external) of the new model maps the numbers in interval [0,1] to points on one meridian of the Bloch Sphere and after measurement, the results map back to numbers from interval [0,1]. However, the other, more important, aspect of this model (internal) is the operation on arbitrary quantum states, and the realization of additional states via phase measurements. We discuss and illustrate how this model can be used to represent a hidden state of reasoning agents or an emotional state of a humanoid robot.
In this discussion paper, I give an account for non-experts of, arguably, quantum dynamics in the brain, underlying the modeling of affective behavior of humanoid robots in the making. Outreach to the larger audience inevitably leads to abbreviations and simplifications; nonetheless, I try to offer the backgrounds of why it is important to study the virtual aspects of ‘people’ we meet online, what dimensions play a role in assessing such creatures, what humanities, psychology, communication, and computer science provide to help us understand how we become attached to non-existent others. As its capstone for the time being, an approach derived from physics is discussed for a robot to handle emotional ambiguity and vagueness of its user. Two computational models, Silicon and Quantum Coppélia, are discussed for their potential and limitations in explaining human affective behavior while dealing with mediated characters.
This dissertation introduces a new type of quantum automata, their encoding and circuit realization. I concentrate on possible applications in robotics. Several methods and application of quantum automata and quantum circuit-based controllers for elementary robotic systems, with a focus on humanoid robot motion, emotion, and behavior generation are illustrated in detail. The research introduces several novel methodologies that bridge quantum computing principles with robotic control, aiming to overcome the limitations of classical deterministic and probabilistic approaches. The dissertation first presents a quantum-circuit-based framework for generating non-repetitive and expressive (e)motions in a humanoid robot actor, using superposition and entanglement to produce families of synchronized yet probabilistic behaviors. It then introduces a flowchart-based synthesis method for quantum automata using one-hot encoding, which significantly reduces quantum circuit cost by leveraging EXOR-based logic and restricting gate sets to NOT, CNOT, and Toffoli gates. Finally, the work expands into ternary quantum logic, developing hybrid quantum finite automata (QFA) using Chrestenson-family gates and uniformization ternary quantum gates, enabling more efficient pattern and language recognition with smaller state spaces. The key contributions of this dissertation are multifold. First, it presents the first practical implementation of a quantum-controlled humanoid robot for theatrical performance, demonstrating visually distinct, expressive, and entertaining behaviors that extend beyond the capabilities of classical control systems. Second, it introduces a direct synthesis methodology for quantum sequential circuits derived from high-level flowcharts, achieving up to a 95% reduction in quantum cost compared to existing design approaches. Third, the dissertation develops the generalized hybrid quantum finite automata (GHQFA), integrating ternary quantum logic with classical automata to enable complex pattern recognition and robotic gesture interpretation while requiring minimal quantum cost. Although the illustrative example of a robot car simulated in the CoppeliaSim environment is very simple based only on Chrestenson gates, the integration of all methods proposed herein establishes a foundation for controlling highly expressive humanoid robotic systems. Furthermore, the proposed models can be applied to real-world robotic tasks, including gesture recognition, motion synthesis, and adaptive behavior generation, with validation through both physical robotic performances and simulation studies. Finally, this work introduces an r-of-n encoding method that can enable to encode large machines for quantum circuits and can be integrated with the entanglement and superposition methods presented in this dissertation. This dissertation lays the groundwork for scalable and practical quantum-robotic systems, offering a pathway toward more intelligent, adaptive, and engaging robotic agents through quantum-enhanced control architectures.
This study explores how quantum computing can reshape the intelligence, adaptability, and learning capacity of humanoid robotics. It examines how quantum principles such as superposition and entanglement allow robots to process and evaluate information in parallel, leading to faster, more flexible responses than those built on classical computing. The paper connects ideas from quantum machine learning (QML), quantum optimization, and quantum reinforcement learning (QRL) to practical scenarios in humanoid robotics, where rapid reasoning and context awareness are essential. Within a hybrid quantum-classical framework, the study outlines how these methods can enhance robotic perception, decision-making, and natural-language interaction, making cognitive robotics more adaptive in complex domains such as healthcare, manufacturing, and disaster response. Rather than presenting a full solution, this work defines a pathway for integrating quantum algorithms into real robotic architectures. The results indicate that combining quantum computing with humanoid robotics through hybrid quantum-classical systems could lead to a new stage of robotic intelligence machines able to handle uncertainty, learn continuously, and reason in ways that reflect deeper, more human-like awareness.