Industry-leading roadmap - IonQ

Watch IonQ's Quantum World Congress 2025 Keynote On Demand. Commercial quantum computers with world-record fidelity to tackle the world’s most complex challenges. Securing communications and building the global quantum internet. Protecting critical data from current and future threats. Ultra precise and accurate sensing that advances navigation, imaging, and resource discovery. Creating a secure space-based platform to protect the data of governments and enterprises. Quantum solutions to your most pressing business challenges with IonQ application team Harness the power of IonQ Quantum Systems from the cloud A benchmark that measures what matters most: a system’s value to your organization Case studies and partnerships with the world's most innovative organizations IonQ is at the forefront of practical, scalable quantum solutions and applications, accelerating the timeline for commercial quantum advantage. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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. To keep up with our latest news and announcements, please fill out the form below.