Quantum AI (QAI) is associate degree knowledge domain field that focuses on building quantum algorithms for up procedure tasks at intervals AI, as well as sub-fields like machine learning. The whole idea of quantum-enhanced AI algorithms remains in abstract analysis domain. Quantum mechanics phenomena, superposition and trap, area unit permitting quantum computing to perform computations that area unit way more economical than classical AI algorithms employed in laptop vision, linguistic communication process and AI. The whole idea of quantum-enhanced AI algorithms remains in abstract analysis domain. Building on recent theoretical proposals, initial sensible studies recommend that these ideas have the likelihood to be enforced within the laboratory, below strictly controlled conditions’.
Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI, a technological synergy with expected significant benefits for both. In this paper, we provide a brief overview of what has been achieved in QAI so far and point to some open questions for future research. In particular, we summarize some major key findings on the feasability and the potential of using quantum computing for solving computationally hard problems in various subfields of AI, and vice versa, the leveraging of AI methods for building and operating quantum computing devices.
Quantum AI (QAI) is an emerging area that blends quantum physics and AI. Technology has advanced thanks to quantum computing and AI. Quantum artificial intelligence is useful for difficult tasks like medical diagnostics because quantum systems can investigate several states at the same time. Quantum artificial intelligence might solve healthcare problems, including overworked doctors and the need for better diagnosis and treatment, despite these obstacles. Artificial intelligence can evaluate massive medical databases, and quantum computing can simulate biological processes, promising global healthcare advancements. Quantum artificial intelligence (QAI) will solve previously intractable problems using quantum computing's improved processing power. Quantitative artificial intelligence (QAI) revolutionises computation and analysis in healthcare, finance, climate forecasting, and logistics. Implementation requires careful thought, but medical applications that save lives have great potential.
Abstract Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI, a technological synergy with expected significant benefits for both. In this paper, we provide a brief overview of what has been achieved in QAI so far and point to some open questions for future research. In particular, we summarize some major key findings on the feasability and the potential of using quantum computing for solving computationally hard problems in various subfields of AI, and vice versa, the leveraging of AI methods for building and operating quantum computing devices.
Quantum field theory (QFTh) simulators simulate physical systems using quantum circuits that process quantum information (qubits) via single field (SF) and/or quantum double field (QDF) transformation. This review presents models that classify states against pairwise particle states | i j 〉 , given their state transition (ST) probability P | i j 〉 . A quantum AI (QAI) program, weighs and compares the field's distance between entangled states as qubits from their scalar field of radius R ≥ | r i j | . These states distribute across 〈 R 〉 with expected probability 〈 P distribute 〉 and measurement outcome 〈 M ( P distribute ) 〉 = P | i j 〉 . A quantum-classical hybrid model of processors via QAI, classifies and predicts states by decoding qubits into classical bits. For example, a QDF as a quantum field computation model (QFCM) in IBM-QE, performs the doubling of P | i j 〉 for a strong state prediction outcome. QFCMs are compared to achieve a universal QFCM (UQFCM). This model is novel in making strong event predictions by simulating systems on any scale using QAI. Its expected measurement fidelity is 〈 M ( F ) 〉 ≥ 7 / 5 in classifying states to select 7 optimal QFCMs to predict 〈 M 〉 's on QFTh observables. This includes QFCMs' commonality of 〈 M 〉 against QFCMs limitations in predicting system events. Common measurement results of QFCMs include their expected success probability 〈 P success 〉 over STs occurring in the system. Consistent results with high F 's, are averaged over STs as 〈 P distribute 〉 yielding 〈 P success 〉 ≥ 2 / 3 performed by an SF or QDF of certain QFCMs. A combination of QFCMs with this fidelity level predicts error rates (uncertainties) in measurements, by which a P | i j 〉 = 〈 P success 〉 < ∼ 1 is weighed as a QAI output to a QFCM user. The user then decides which QFCMs perform a more efficient system simulation as a reliable solution. A UQFCM is useful in predicting system states by preserving and recovering information for intelligent decision support systems in applied, physical, legal and decision sciences, including industry 4.0 systems.