Automated theorem proving seeks to use computational systems to prove or disprove mathematical and logical statements [1, 2]. It underpins a wide range of applications, and enhancing theorem-proving capabilities remains a central objective in artificial intelligence [3]. Although recent neuro-symbolic systems have achieved remarkable progress [4-7], their operation is ultimately constrained by classical computational architectures. Quantum computing [8], by contrast, enables information encoding and coherent parallelism beyond classical limits [9-14], raising the possibility of accelerating structured symbolic deduction [15]. Here we report the experimental realization of automated geometry theorem proving on a fully programmable superconducting quantum processor. We develop two complementary quantum proving frameworks. The first implements Wu's algebraic elimination method using quantum pseudo-division, with multivariate polynomials represented in superposition states, enabling quantum algebraic theorem proving. The second implements the full-angle method as backward symbolic reasoning through a hybrid quantum strategy-guided architecture, demonstrating a general route toward quantum symbolic proof search. As illustrative examples, we prove two theorems on a superconducting quantum processor: the perpendicularity of the diagonals of a square and a 1978 International Mathematical Olympiad geometry problem. Our results establish, at the experimental level, automated logical reasoning as a viable task for near-term quantum processors and provide a concrete pathway toward quantum-enhanced symbolic intelligence.
Automated theorem proving seeks to use computational systems to prove or disprove mathematical and logical statements [1, 2]. It underpins a wide range of applications, and enhancing theorem-proving capabilities remains a central objective in artificial intelligence [3]. Although recent neuro-symbolic systems have achieved remarkable progress [4-7], their operation is ultimately constrained by classical computational architectures. Quantum computing [8], by contrast, enables information encoding and coherent parallelism beyond classical limits [9-14], raising the possibility of accelerating structured symbolic deduction [15]. Here we report the experimental realization of automated geometry theorem proving on a fully programmable superconducting quantum processor. We develop two complementary quantum proving frameworks. The first implements Wu's algebraic elimination method using quantum pseudo-division, with multivariate polynomials represented in superposition states, enabling quantum algebraic theorem proving. The second implements the full-angle method as backward symbolic reasoning through a hybrid quantum strategy-guided architecture, demonstrating a general route toward quantum symbolic proof search. As illustrative examples, we prove two theorems on a superconducting quantum processor: the perpendicularity of the diagonals of a square and a 1978 International Mathematical Olympiad geometry problem. Our results establish, at the experimental level, automated logical reasoning as a viable task for near-term quantum processors and provide a concrete pathway toward quantum-enhanced symbolic intelligence.
Automated theorem proving, or more broadly automated reasoning, aims at using computer programs to automatically prove or disprove mathematical theorems and logical statements. It takes on an essential role across a vast array of applications and the quest for enhanced theorem-proving capabilities remains a prominent pursuit in artificial intelligence. Here, we propose a generic framework for quantum automated theorem proving, where the intrinsic quantum superposition and entanglement features would lead to potential advantages. In particular, we introduce quantum representations of knowledge bases and propose corresponding reasoning algorithms for a variety of tasks. We show how automated reasoning can be achieved with quantum resolution in both propositional and first-order logic with quadratically reduced query complexity. In addition, we propose the quantum algebraic proving method for geometric theorems, extending Wu's algebraic approach beyond the classical setting. Through concrete examples, including geometry problems from the International Mathematical Olympiad, we demonstrate how a quantum computer may prove geometric theorems with quadratic better query complexity. Our results establish a primary approach towards building quantum automatic theorem provers, which would be crucial for practical applications of both near-term and future quantum technologies.
This paper presents a novel approach to automated theorem proving specifically tailored for quantum circuit verification. The core idea is to construct a formal system that leverages logical reasoning and proof techniques to rigorously analyze quantum circuit designs. This system aims to identify errors and inconsistencies within the circuit, ensuring compliance with the fundamental principles of quantum mechanics. The proposed method moves beyond traditional circuit simulation, offering a mathematically sound guarantee of correctness. We detail the architecture of the theorem prover, focusing on the representation of quantum circuits and the application of proof strategies. The system utilizes a combination of techniques including logical deduction, model checking, and constraint solving, adapted for the unique characteristics of quantum computation. The paper outlines the key components of this system and highlights its potential to significantly improve the trustworthiness and reliability of quantum computing technologies. The central aim is to establish a robust framework for automated verification, reducing the risk of errors and accelerating the development of quantum software.
In quantum information and computation research, symbolic methods have been widely used for human specification and reasoning about quantum states and operations. At the same time, they are essential for ensuring the scalability and efficiency of automated reasoning and verification tools for quantum algorithms and programs. However, a formal theory for symbolic specification and reasoning about quantum data and operations is still lacking, which significantly limits the practical applicability of automated verification techniques in quantum computing.
In this paper, we present a general logical framework, called Symbolic Operator Logic SOL, which enables symbolic specification and reasoning about quantum data and operations. Within this framework, a classical first-order logical language is embedded into a language of formal operators used to specify quantum data and operations, including their recursive definitions. This embedding allows reasoning about their properties modulo a chosen theory of the underlying classical data (e.g., Boolean algebra or group theory), thereby leveraging existing automated verification tools developed for classical computing. It should be emphasised that this embedding of classical first-order logic into SOL is precisely what makes the symbolic method possible.
We envision that this framework can provide a conceptual foundation for the formal verification and automated theorem proving of quantum computation and information in proof assistants such as Lean, Coq, and related systems.
This paper presents a novel interdisciplinary framework that integrates quantum supersymmetry with artificial intelligence to develop an algebraically structured foundation for generalized intelligence. Drawing from the mathematical rigor of supersymmetric quantum mechanics and Lie superalgebras such as osp(1∣2) and sl(2∣1), we propose AI architectures where learning dynamics are governed by graded algebraic transformations and symmetry-preserving constraints. By modeling intelligent agents as evolving quantum systems under the action of supercharges, the framework introduces dual representations—bosonic and fermionic—into latent spaces, enabling semantically coherent, interpretable, and generalizable learning. The research outlines a hybrid simulation environment combining symbolic computation and quantum frameworks to validate superalgebra-guided models on symbolic reasoning and structured prediction tasks. This approach not only enhances model robustness and generalization but also opens new frontiers in quantum-algebraic AI, offering a blueprint for the design of principled, high-order intelligence. Keywords: quantum supersymmetry, Lie superalgebras, generalized intelligence, artificial intelligence, algebraic AI, osp(1∣2), symmetry-preserving learning, supercharges, graded vector spaces, quantum AI, interpretability, symbolic reasoning, scientific discovery, algebraic machine learning, Hamiltonian learning
This paper introduces a novel approach to quantum computation leveraging high-dimensional symbolic logic, aiming to accelerate the evaluation and verification of complex logical expressions. We propose a quantum-enhanced symbolic logic system that utilizes quantum entanglement and superposition to achieve significantly faster computation compared to classical methods. The core mechanism centers around exploiting these quantum phenomena to represent and manipulate logical statements with unprecedented efficiency. The system focuses on automating the generation of logical inference models, reducing the reliance on manual effort. This work addresses the limitations of traditional symbolic logic computation by introducing a fundamentally different algorithmic structure. We present initial results demonstrating the potential for substantial speedups in specific scenarios, highlighting the promise of this approach for complex problem-solving.
This paper introduces a novel quantum computing-based symbolic reasoning algorithm designed to enhance the efficiency and accuracy of symbolic reasoning. Traditional symbolic reasoning methods often struggle with computational complexity, particularly when dealing with large and complex knowledge bases. This work proposes a quantum algorithm leveraging quantum bit entanglement and superposition to accelerate the process of logical inference and knowledge retrieval. We explore a specific implementation of this algorithm using a simplified model of logical deduction, demonstrating its potential to outperform classical approaches in certain scenarios. The core mechanism centers on quantum state manipulation to facilitate the exploration of multiple possible reasoning paths concurrently. The paper concludes with a discussion of the challenges and future directions of this research, highlighting the potential for quantum computing to revolutionize symbolic reasoning. The primary goal is to provide a solid theoretical foundation and initial experimental results demonstrating the algorithm's efficacy.