Industry-leading roadmap
IonQ is at the forefront of practical, scalable quantum solutions and applications, accelerating the timeline for commercial quantum advantage.
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The fastest path to fault tolerance
IonQ is delivering the world’s most powerful quantum computers with 2 million physical qubits and 80,000 logical qubits by 2030 to solve the world’s most complex problems.
Our roadmap commits to the highest number of physical and logical qubits of any commercial quantum computing company in the world.
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Quality that compounds
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Nature’s perfect qubit
Qubits are the heart of our quantum processing units. IonQ uses individual atoms as naturally perfect quantum systems, unlike synthetic approaches like supercooled superconducting wire or silicon imperfections. We trap ions in 3D space and use lasers for everything from initial preparation to final readout.
World-record fidelity
at 99.99%
High-quality qubits in IonQ's trapped ion systems deliver industry-leading performance. We execute deeper, more accurate quantum circuits without wasting physical qubits on heavy error correction overhead. Fewer, higher-quality qubits outperform many low-quality qubits through reliable, stable computation.
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Error correction at scale
Applying quantum error correction to physical qubits yields usable logical qubits. We're building both high-quality physical qubits and the robust error correction needed for superior logical qubits. With enough high-quality logical qubits, you can tackle complex problems and run large-scale algorithms that deliver real value.
Quantum memory technology
IonQ's advancements in quantum memory, supported by Lightsynq technologies, are crucial for storing quantum information reliably and efficiently in large-scale systems. Combined with the inherent capability of our trapped ion architecture, these technological advancements provide long-lived quantum states, reducing the impact of decoherence and errors, and allowing for more stable quantum operations.
Scaling
our systems
IonQ employs a modular approach, connecting smaller, highly functional trapped ion systems to scale quantum computing. This practical and cost-effective strategy aims to achieve fault-tolerant quantum computing at incredible speed and practicality. Unlike monolithic designs, this modularity offers significant advantages in scalability and resource utilization.
Industry use case highlights
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Quantum-accelerated drug development
In collaboration with AstraZeneca, AWS, and NVIDIA, IonQ executed the largest quantum-accelerated electronic structure simulation performed to date. This groundbreaking approach accelerated the complex chemistry simulation by at least 656 times.
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Enterprise-grade LLMs with quantum fine tuning
IonQ's quantum fine-tuning is a hybrid approach that adds a quantum layer to classical AI models. This method makes LLMs smarter and more efficient by capturing higher-dimensional patterns that classical systems miss.
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Advancing automotive materials with quantum AI
IonQ partnered with a leading global automotive manufacturer, applying quantum-classical AI to solve critical materials science challenges. Our model successfully generated higher-fidelity synthetic data for steel microstructures.
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Unlocking foundry-scale
quantum production
IonQ is pioneering the use of quantum-grade diamond thin films, a breakthrough that allows diamond quantum devices to be manufactured at an industrial scale. This approach leverages semiconductor techniques, dramatically simplifying manufacturing.
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Machine Learning Image Recognition
For self driving cars to become a reality, vehicles must interface with road signs in the physical world. In our work with Hyundai, we explored loading images of road signs into our quantum computers for analysis. With only 8 qubits, we successfully trained a quantum machine learning image recognition algorithm to recognize common road signs with fewer input parameters than comparable classical approaches.
Our systems.
Evolving fast.
Our hardware achieves high fidelity in these operations (e.g., 99.9% one-qubit gate fidelity thanks to the stability of trapped ions. We also employ error mitigation techniques and algorithmic qubits (#AQ) as our performance benchmark to reflect useful computational power.