Quantum-inspired AI uses mathematical and theoretical constructs from quantum mechanics to enhance classical algorithms. Unlike quantum-native
This review gives an overview of QML, from advancements in quantum-enhanced classical ML to native quantum algorithms and hybrid quantum-classical frameworks.
Quantum computing's integration with AI and ML could offer scalable, energy-efficient solutions to address these issues to potentially offer enhanced AI capabilities. ### D-Wave's Quantum AI Development Initiative. D-Wave is focused on advancing quantum AI solutions that draw upon the optimization capabilities of annealing quantum computing to help customers build more efficient, rapid, and energy-saving AI and machine learning workloads. D-Wave’s Quantum AI development initiative is exploring how to enhance pre-training optimization and model accuracy, which could provide significant advantages for businesses and researchers alike. #### “We’re seeing early evidence that annealing quantum computing could play a key role in helping AI/ML with more efficient model training, reduced energy consumption and faster time-to-solution.”. **TRIUMF**, Canada's particle accelerator center, and its partner institutions are showing significant speed-ups using D-Wave’s quantum computers over classical approaches for simulating high-energy particle-calorimeter interactions—potentially leading to major efficiencies where the AI model is used to create synthetic data.
From quantum processors and qubits to quantum AI and cryptography, these breakthroughs ... AI, including the marvels of ChatGPT and advancements
At the same time, building functional quantum computers requires solving problems that classical optimization and machine learning handle well: calibrating hardware parameters, designing control pulses, routing quantum circuits, and mitigating errors in real time. **AI for quantum computing** applies machine learning techniques to improve quantum hardware and algorithms. This includes using neural networks to calibrate qubits, reinforcement learning to discover optimal quantum circuits, and classical AI to decode error correction syndromes. Using AI to help quantum computing requires only classical machine learning techniques applied to quantum control problems – something researchers can do today. IBM also applies classical AI to improve its quantum systems, using machine learning for calibration, error mitigation, and circuit optimization. **Google Quantum AI** conducts research on quantum algorithms for optimization and sampling, with applications to machine learning. **IonQ** explores quantum AI applications in optimization and machine learning while using classical AI to calibrate and optimize its trapped-ion quantum computers.
**Broomfield, CO, and London, U.K., February 4th, 2025 –** Quantinuum today announced a groundbreaking Generative Quantum AI framework (Gen QAI) – leveraging unique quantum-generated data to enable commercial applications in areas ranging from the development of new medicines, precise predictive modeling of financial markets and real-time optimization of global logistics and supply chains. This important development will enable Quantinuum to deepen collaboration with the nation’s research and industrial ecosystem, together with the company’s plan to deploy its Helios quantum computer in Singapore later this year. * **Dr.** **Marvin Lee, Country Leader for Quantinuum Singapore**, who recently joined the company following senior appointments at A\*STAR, EDB, and NRF, where he played a key role in shaping the National Quantum Strategy, said: “The new Centre will enable local talent and industry to work hands-on with quantum technologies, co-develop solutions aligned with national priorities, and support high-value jobs.
A number of quantum algorithms for machine learning are based on the idea of amplitude encoding, that is, to associate the [amplitudes](/wiki/Probability_amplitude "Probability amplitude") of a quantum state with the inputs and outputs of computations.[[30]](#cite_note-Patrick_Rebentrost_2014-30)[[31]](#cite_note-Nathan_Wiebe_2012-31)[[32]](#cite_note-Maria_Schuld_2016-32) Since a state of n {\displaystyle n}  qubits is described by 2 n {\displaystyle 2^{n}}  complex amplitudes, this information encoding can allow for an exponentially compact representation. One of these conditions is that a [Hamiltonian](/wiki/Hamiltonian_(quantum_mechanics) "Hamiltonian (quantum mechanics)") which entry-wise corresponds to the matrix can be simulated efficiently, which is known to be possible if the matrix is sparse[[34]](#cite_note-34) or low rank.[[35]](#cite_note-35) For reference, any known classical algorithm for [matrix inversion](/wiki/Matrix_inversion "Matrix inversion") requires a number of operations that grows [more than quadratically in the dimension of the matrix](/wiki/Computational_complexity_of_mathematical_operations#Matrix_algebra "Computational complexity of mathematical operations") (e.g. O ( n 2.373 ) {\displaystyle O{\mathord {\left(n^{2.373}\right)}}} ), but they are not restricted to sparse matrices. **[^](#cite_ref-118)** ["Can quantum machine learning move beyond its own hype?"](https://www.quantamagazine.org/job-one-for-quantum-computers-boost-artificial-intelligence-20180129/).
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