Natural Language Processing and Semantic Web include several NP complete/hard problems that are intractable for classical computing machines. Even though distributed computing has provided remarkable advances (more precisely in dealing with big data), non-decomposable NP problems are still intractable in many real-world applications. And, from quantum computing perspective, solving complex problems with universal quantum gates requires developing of quantum algorithms. Considering commercializing quantum annealing machines by D-Wave, achieving global optimum for discrete optimization problems has been realized. In this study, a novel approach has been introduced to convert symbolic AI problems into quadratic unconstrained binary optimization (QUBO) form. More narrowly, this method represents classification of text documents (fragments) as optimizing a QUBO function. After embedding the train corpus into a QUBO function, D-Wave quantum annealer is used to classify new observations with finding the minimum energy level of the system.
Natural Language Processing and Semantic Web include several NP complete/hard problems that are intractable for classical computing machines. Even though distributed computing has provided remarkable advances (more precisely in dealing with big data), non-decomposable NP problems are still intractable in many real-world applications. And, from quantum computing perspective, solving complex problems with universal quantum gates requires developing of quantum algorithms. Considering commercializing quantum annealing machines by D-Wave, achieving global optimum for discrete optimization problems has been realized. In this study, a novel approach has been introduced to convert symbolic AI problems into quadratic unconstrained binary optimization (QUBO) form. More narrowly, this method represents classification of text documents (fragments) as optimizing a QUBO function. After embedding the train corpus into a QUBO function, D-Wave quantum annealer is used to classify new observations with finding the minimum energy level of the system.
The convergence of quantum computing and artificial intelligence represents a paradigm shift in computational capability, enabling solutions to previously intractable optimization problems, accelerated machine learning training, and enhanced natural language understanding through quantum state exploitation. This paper presents a comprehensive framework encompassing five distinct quantum artificial intelligence architectures: Quantum Machine Learning utilizing quantum circuit-based training and inference, Quantum-Inspired AI implementing quantum algorithmic principles on classical hardware, Hybrid Quantum-Classical AI leveraging collaborative processing between central processing units and quantum processing units, Quantum Optimization AI for solving complex combinatorial problems, and Quantum Natural Language Processing exploiting quantum superposition for semantic reasoning. The quantum machine learning architecture implements variational quantum eigensolvers and quantum kernel methods for feature space transformation, achieving exponential speedup in specific classification tasks. The quantum-inspired approach applies tensor network decomposition and quantum annealing simulation on classical systems, demonstrating polynomial speedup for optimization problems. The hybrid quantum-classical framework orchestrates workload partitioning between conventional processors and quantum accelerators through dynamic task allocation algorithms, optimizing for quantum circuit depth and classical preprocessing overhead. Quantum optimization leverages quantum annealing and quantum approximate optimization algorithms for solving large-scale combinatorial problems in logistics, finance, and molecular simulation. Quantum natural language processing implements quantum word embeddings in Hilbert space, enabling superposition-based semantic analysis with logarithmic dimensional complexity compared to classical word vector representations. This research establishes theoretical foundations and architectural blueprints for quantum-enhanced artificial intelligence systems, positioning quantum computing as a transformative technology for next-generation machine learning, optimization, and cognitive computing applications.
To date, conventional computers have never been able to efficiently handle certain tasks, where the number of computation steps is likely to blow up as the problem size increases. As an emerging technology and new computing paradigm, quantum computing has a great potential to tackle those hard tasks efficiently. Among all the existing quantum computation models, quantum annealing has drawn significant attention in recent years due to the realization of the commercialized quantum annealer, sparking research interests in developing applications to solve problems that are intractable for classical computers.However, designing and implementing algorithms that manage to harness the enormous computation power from quantum annealer remains a challenging task. Generally, it requires mapping of the given optimization problem into quadratic unconstrained binary optimization(QUBO) problem and embedding the subsequent QUBO onto the physical architecture of quantum annealer. Additionally, practical quantum annealers are susceptible to errors leading to low probability of the correct solution.In this study, we focus on solving Boolean satisfiability (SAT) problem using quantum annealer while addressing practical limitations. We have proposed a mapping technique that maps SAT problem to QUBO, and we have further devised a tool flow that embeds the QUBO onto the architecture of a quantum annealing device. Additionally, We have optimized the proposed embedding flow to reduce run-time in addition to shortening the qubit chain length, leading to robust quantum annealing. To further improve the reliability of quantum annealing, we have also developed a post processing embedding technique that enlarges the energy gap between ground state and the first excited state. To demonstrate the effectiveness of proposed methods, we have conducted experiments on real quantum annealing devices manufactured by D-Wave Systems, showing compelling result of using quantum annealer to solve SAT problem.
Many artificial intelligence (AI) problems naturally map to NP-hard optimization problems. This has the interesting consequence that enabling human-level capability in machines often requires systems that can handle formally intractable problems. This issue can sometimes (but possibly not always) be resolved by building special-purpose heuristic algorithms, tailored to the problem in question. Because of the continued difficulties in automating certain tasks that are natural for humans, there remains a strong motivation for AI researchers to investigate and apply new algorithms and techniques to hard AI problems. Recently a novel class of relevant algorithms that require quantum mechanical hardware have been proposed. These algorithms, referred to as quantum adiabatic algorithms, represent a new approach to designing both complete and heuristic solvers for NP-hard optimization problems. In this work we describe how to formulate image recognition, which is a canonical NP-hard AI problem, as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The QUBO format corresponds to the input format required for D-Wave superconducting adiabatic quantum computing (AQC) processors.
The new era of artificial intelligence (AI) aims to entangle the relationships among models (characterizations), algorithms, and implementations toward the high-level intelligence with general cognitive ability, strong robustness, and interpretability, which is intractable for machine learning (ML). Quantum computer provides a new computing paradigm for ML. Although universal quantum computers are still in infancy, special-purpose D-Wave machine hopefully becomes the breaking point of commercialized quantum computing. The core principle, quantum annealing (QA), enables the quantum system to naturally evolve toward the low-energy states. D-Wave's quantum computer has developed some applications of quantum ML based on quantum-assisted ML algorithms, quantum Boltzmann machine, etc. Additionally, working with CPUs, quantum processing units is likely to advance ML in a quantum-inspired way. Thus, a new advanced computing architecture, quantum-classical hybrid approach consisting of QA, classical computing, and brain-inspired cognitive science, is required to explore its superiority to universal quantum algorithms and classical ML algorithms. It is important to explore hybrid quantum/classical approaches to overcome the defects of ML such as high dependence on training data, low robustness to the noises, and cognitive impairment. The new framework is expected to gradually form a highly effective, accurate, and adaptive intelligent computing architecture for the next generation of AI.
Quantum Annealing is an alternative type of computation in which problems are encoded in quantum Hamiltonians (energy functions) and quantum dynamics is used to find solutions (ground states of minimal energy). Quantum computers such as the D-Wave systems are indeed implementing those ideas in hardware, as well as "quantum-inspired" devices based on classical electronics such as Fujitsu’s Digital Annealing Unit. All those systems use the same modeling language: Quadratic Unconstrained Binary Optimization (QUBO). However, QUBO is a low-level language and for modeling combinatorial problems such as constraint satisfaction and constrained optimization problems, we need to introduce higher-level abstractions in order to define complex constraints.
Quantum annealing device has shown a great potential in solving discrete problems that are theoretically and empirically hard. Boolean Satisfiability (SAT) problem, determining if there is an assignment of variables that satisfies a given Boolean function, is the first proven NP-complete problem widely used in various domains. Here, we present a novel mapping of the SAT problem to the quadratic unconstrained binary optimization problem (QUBO), and further develop a tool flow embedding the proposed QUBO to the architecture of the commercialized quantum computer D-Wave. By leveraging electronic design automation techniques including synthesis, placement and routing, this is not only the first work providing the detail flow that embeds the QUBO, but also a technique scalable for real world applications and some hard SAT problems with over 6000 variables in QUBO. Based on our results, we discuss the challenges in solving SAT using the current generation of annealing device, and explore the problem solving capability of future quantum annealing computers.