Rigetti Computing, Inc. (RGTI) Business

ITEM 1.BUSINESS

Overview

Our mission is to build the world’s most powerful computers to help solve humanity’s most important and pressing problems. Our strategy is to be at the forefront of superconducting quantum computing.

Classical computers are plateauing, Moore’s law has slowed, returns for parallelization are diminishing and energy requirements can’t keep up. Today, many of the world’s most important computational challenges remain intractable, lying beyond the capabilities of traditional supercomputers and cloud infrastructure. We build and operate quantum computers. We believe quantum computing represents one of the most transformative emerging capabilities in the world today. By leveraging quantum mechanics, our quantum computers process information in fundamentally new, more powerful ways compared to classical computing with meaningful power efficiency. When scaled, we believe these systems are poised to solve problems of staggering computational complexity at unprecedented speed.

The availability of scalable quantum computers is expected to enable scientists and engineers to address problems in areas like climate change, fusion energy, quantitative finance, drug development and discovery, materials science, and artificial intelligence (“AI”). Our quantum computers are based on superconducting qubits, which we believe is the leading quantum computing modality based on their fast gate speeds and defined pathway to scaling. Our quantum computers currently achieve gate speeds of 50-70 nanoseconds, which is about 1,000 times faster than other modalities such as trapped ions or pure atoms based on publicly available information. We have developed the world’s first multi-chip quantum processor for scalable quantum computing systems. We expect this patented and patent pending modular chip architecture to be the building block for new generations of quantum processors that we expect to achieve an advantage over classical computers. We have already demonstrated the potential of our modular chip architecture with the launch of Cepheus-1-36Q, which is based on four 9-qubit “chiplets” tiled together.

We are a vertically integrated company. We own and operate Fab-1, a wafer fabrication facility dedicated to prototyping and producing our quantum processors. Through Fab-1, we own the means of production of our breakthrough multi-chip quantum processor technology. We leverage our chips through a full-stack product development approach, from quantum chip design and manufacturing through cloud delivery. We believe this full-stack development approach offers both the fastest and lowest risk path to building commercially valuable quantum computers.

We have been deploying our quantum computers to end users over the cloud since 2017. We offer our full-stack quantum computing platform as a cloud service to a wide range of end-users, directly through our Rigetti Quantum Cloud Services (QCS®) platform, and also through public cloud service providers.

We began selling quantum computers to end users in 2023. In December 2023, we launched the Novera™ QPU, our first commercially available quantum processing unit (“QPU”), which includes a 9-qubit chip that features tunable couplers for two-qubit operations and a 5-qubit chip for testing single-qubit operations. The Novera QPU is based on our fourth generation Ankaa™-class architecture. In December 2024, we sold a Novera QPU to Montana State University, which was our first QPU delivered to an academic institution. In 2025, we received purchase orders for two Novera systems totaling approximately $5.7 million. Both systems are upgradeable, allowing the customers to increase the system qubit count for more complex computations and research.

In the fourth quarter of 2024, we announced the public launch of our 84-qubit Ankaa-3 system, which featured an extensive hardware redesign. We achieved a key two-qubit gate fidelity milestone with Ankaa-3: successfully halving error rates in 2024 to achieve a 99.0% median two-qubit gate fidelity based on our internal testing. For information on gate fidelity, see “—Our Technology—Our Superconducting Quantum Processors—Fidelity.”

In the second quarter of 2025, we announced the public launch of our 36-qubit Cepheus-1-36Q system, our newest flagship quantum computer that utilizes our modular chip architecture and demonstrates our path to scaling to higher qubit count and higher performing systems. Made of four 9-qubit “chiplets,” we believe that Cepheus-1-36Q is the industry’s largest multi-chip quantum computer. As of January 2026, we achieved a 99.6% median two-qubit gate fidelity (based on internal testing) with Cepheus-1-36Q, successfully halving our error rate from our previous, single-chip 84-qubit Ankaa-3 system.

Ankaa-3 and Cepheus-1-36Q are available to our partners via the Rigetti QCS platform. Cepheus-1-36Q is intended to enable users to operate our universal CZ gates for a wide range of algorithmic research, with a median gate time of 76 nanoseconds. Our CZ gates are designed to be optimized for fast gate times while reducing coherent errors, which improves fidelity and is key for executing quantum error correction techniques. Cepheus-1-36Q features scalable chip architecture with 3D signal delivery while incorporating enhancements to key technologies, such as enhanced intermodule coupler design to enable higher performance.

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Leveraging our full-stack platform and in-house quantum foundry capabilities, we believe that Cepheus-1-36Q demonstrates our ability to deliver increasingly higher performance quantum computers with larger qubit counts using our proprietary chiplet-based architecture. We have developed strong customer relationships and collaborative partnerships for the purpose of accelerating the development of key technologies for high-value use cases to potentially unlock strategic market opportunities.

Our partners and customers include commercial enterprises such as Amazon Web Services (“AWS”) Standard Chartered Bank and Moody’s, along with U.S. government organizations such as Defense Advanced Research Projects Agency (“DARPA”), Department of Energy (“DOE”), and Air Force Research Laboratory (“AFRL”), and international government entities such as India’s Centre for Development of Advanced Computing (“C-DAC”), India’s premier R&D organization of the Ministry of Electronics and Information Technology. In April 2025, Rigetti UK Limited, our wholly owned subsidiary, announced that it was selected as one of the winners of Innovate UK’s Quantum Missions Pilot Competition to advance quantum error correction capabilities on superconducting quantum computers. As part of the project, we are upgrading our existing quantum computer hosted at the UK’s National Quantum Computing Centre to a larger 36-qubit quantum processing unit and integrating it with Riverlane Ltd.’s quantum error correction stack.

In January 2026, Rigetti Computing India P L, a wholly owned subsidiary of Rigetti Computing, Inc., announced that it received an $8.4 million purchase order to deliver a 108-qubit quantum computer to C-DAC. The system will be installed on-premises at C-DAC’s Bengaluru center and is expected to be deployed in the second half of 2026.

We are focused on continuing to improve our system performance. We recently achieved a two-qubit gate fidelity as high as 99.9% at 28 nanosecond gate speed on a prototype platform by using a new proprietary adiabatic CZ scheme. We continue to be at 99.9% one-qubit gate fidelity. In January 2026, we announced achievement of a median two-qubit gate fidelity (based on internal testing) of 99.7% on our 9-qubit system, 99.6% on our 36-qubit system and 99.0% on our 108-qubit system (Cepheus-1-108Q). Cepheus-1-108Q is based on twelve 9-qubit chiplets and leverages our proprietary modular chip architecture. We are enabled by a deep technical team that includes global experts in quantum chip design and manufacturing, quantum computing systems architecture, quantum software, and quantum algorithms and applications.

Powered by the production of our scalable multi-chip quantum processors in Fab-1 and our full-stack product development approach, we are working to develop quantum computing systems that demonstrate clear performance advantages over classical computing alternatives for multiple high-impact application areas.

Quanta Collaboration Agreement

In February 2025, we entered into a Collaboration Agreement (the “Collaboration Agreement”) with Quanta, whereby the parties may enter into written statements of work from time to time pursuant to which Quanta will develop Covered Components listed in such statement of work that meet the specifications and requirements provided by us. “Covered Components” may include control systems, dilution refrigerators, flexible cables, and select other non-QPU components suitable for our quantum computing products. In addition, the parties have each agreed to invest at least $250 million over the next five years in the field of quantum computing (and Quanta’s investment will be towards personnel and capital expenditures for developing products and services and manufacturing capability in furtherance of our product roadmap). Further, in connection with the Collaboration Agreement, in April 2025 Quanta purchased approximately $35 million of shares of Common Stock at approximately $11.59 per share, pursuant to a securities purchase agreement.

Potential Market Opportunity

Demand for computing power capable of solving computationally complex problems is increasing. Many of these types of problems are approached through the use of High-Performance Computing (“HPC”), which relies primarily on large classical computers located either in the cloud or on-premises. We believe that quantum computers will be able to solve many computational problems with greater speed and at a lower cost than today’s high-performance computers, thereby unlocking considerable value for the users of current HPC systems. Furthermore, we believe that quantum computing will be applicable to many use cases that today lie within the realm of the much larger cloud computing market.

Advanced scientific and technical computing applied in fields like drug discovery, materials science, computational fluid dynamics, machine learning, and quantitative finance have underpinned many of society’s greatest scientific and industrial advancements over the past half-century. Yet, despite the availability of the latest cloud and supercomputing capabilities, these and many other fields remain constrained by the intractable nature of their thorniest problems. Typically, the computational limits of classical computers are reached because of either the size or complexity of the required calculations. In certain cases, algorithms have been developed that in theory solve a particular computation problem; however, classical computers are limited in their ability to implement and process such algorithms.

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For decades, classical computing power increased exponentially as the number of transistors on a microchip were doubling about every two years, while the cost of computing simultaneously decreased significantly. Over the past ten years, this rate of progress in classical computing power has significantly slowed as physical limits on the miniaturization of transistors in nano-scale devices are being reached.

Stages of Evolution of Quantum Computing Maturation

We believe that market demand for our quantum computers will grow in phases that map to the increasing capabilities of our commercially available quantum computing systems similar to those of classical computer technology. With each new phase, we expect quantum computers to solve an ever-increasing breadth of high-impact commercial problems and to do so with greater speed and accuracy. Qubits do not need the latest semiconductor lithography node and, in fact, can be made using 1990’s era lithography.

Quantum Advantage (“QA”)

We define QA as the point at which quantum computers can solve a practical problem that would be physically impossible to solve on a classical computer. We believe that a quantum computer with over 1,000 qubits with a two-qubit gate fidelity of above 99.9% and gate speed of less than 50 nanoseconds is needed to achieve QA.

Upon achievement of QA, we believe a quantum computer would be suitable for many applications, including quantum machine learning, quantum simulation, and quantum optimization problems. If QA were to be demonstrated, we would expect a meaningful number of new potential clients to emerge, as the range and value of the problems that would be addressable by quantum computing systems would significantly increase.

Large-Scale Fault Tolerant Quantum Computing (“LFTQC”)

We will consider the phase of LFTQC to begin if and when quantum computing systems are available with hundreds of logical qubits, which can be universally controlled and measured with substantially error-free operation through the full course of a quantum computation. It is currently believed in the quantum computing industry that this is likely to require systems with 10,000 to 1,000,000 physical qubits. We believe our multi-chip architecture provides a pathway to scale up to these large systems.

We anticipate the beginning of the large-scale fault tolerant phase to be roughly a decade away. As quantum computing matures through this phase, systems would likely continue to grow in scale and performance, culminating in full-scale fault tolerance that operates using potentially thousands of effectively logical qubits. This ultimate goal of full-scale fault tolerance represents the largest commercial opportunity.

Business Strategy

Our approach to developing and sustaining what we believe is a strong competitive advantage relies on a six-pronged strategy:

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We believe that we will be able to achieve our plans described above and elsewhere in this Annual Report on Form 10-K; however, we face various risks and uncertainties relating to our business that could cause actual results to differ materially from our expectations stated herein. This Annual Report on Form 10-K, including this Business Section, should be read in conjunction with the section entitled “Risk Factors” in Part I, Item 1A of this Annual Report on Form 10-K.

Business Model & Services

Currently, we generate the majority of our revenues from technology development contracts with various partners. We believe our longer-term business model will be more weighted towards QPU sales and recurring revenues generated from quantum computing systems made accessible via the cloud in the form of QCaaS and QCS services.

Rigetti Quantum Processing Units

Our QPUs contain fabricated silicon-based chips featuring superconducting qubits. These high-performance chips provide fast gate times, low latency conditional logic, and fast program execution times. Our QPUs are designed and fabricated at Fab-1, leveraging novel manufacturing methods to create state-of-the art superconducting qubits.

Novera™, our first commercially available QPU, includes a 9-qubit chip that features tunable couplers for fast two-qubit operations and a 5-qubit chip for testing single-qubit operations. The Novera™ QPU is based on our fourth generation Ankaa-class architecture. We announced our most technically advanced QPU yet based on our proprietary chiplet-based architecture, the 36-qubit Cepheus-1-36Q system, which we believe is the industry’s largest multi-chip quantum computer. We have also released our 84-qubit Ankaa-3 quantum computer, which is available on Amazon Braket. We intend to design and fabricate more advanced QPUs in the future with improved fidelities, faster gate speeds and higher qubit counts. We believe these anticipated improvements and advances in technology will hopefully lead to QA and LFTQC in the future.

Quantum Computing as a Service (QCaaS)

We sell access to our quantum computers through cloud-based services, commonly referred to as QCaaS. This approach enables us to serve a wide range of customers without the complexity and cost associated with shipping, operating and servicing complex and cryogenic computing equipment on customer premises.

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Rigetti Quantum Cloud Services

Rigetti Quantum Cloud Services (“QCS”) is a proprietary platform to deliver high-performance quantum computing over the cloud. QCS features a hybrid quantum-classical computing environment that incorporates our quantum computers operating in tandem with cloud infrastructure. It provides support for a broad range of programming capabilities, the ability to integrate over public or private clouds, and high-speed connectivity to auxiliary classical computing resources.

The product is designed to meet the needs of a diverse set of customers that all benefit from the high-performance nature of its core computational capabilities. Central to QCS are two very powerful sets of technologies developed by us, our QPUs, described above, and our quantum operating system, as described below:

Quantum Operating System Software

QCS’s computing environment is powered by a distributed quantum operating system that natively supports both public and private cloud architectures.

The operating system software includes a rich set of quantum application and software development tools designed to unlock the capabilities of the quantum computing ecosystem by:

QCS Outpost

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Customers who purchase an on-premises quantum system from us also have access to QCS Outpost, our distributed software environment for operating, administering, and monitoring the overall system. In addition, QCS Outpost includes utilities for QPU characterization and calibration, user access management, quantum program compilation, scheduling, and execution.

QCS Outpost also serves as the foundation for integrating with other systems, in particular high-performance computing systems, and can be accessed through a variety of tools and web-based services as well as through software development kits (SDKs) that support Python, C, and Rust.

Direct QCaaS Distribution

We provide access on a commercial basis to our quantum computers over QCS, directly engaging with enterprises and government organizations making significant investments in quantum computing research, development, and readiness.

We believe many of these customers will have performance, customization and integration requirements best met by our ability to engage deeply and directly with these kinds of clients. We believe our full-stack product development approach, and strategy of forging collaborative customer partnerships positions us to be a highly valued and long-term provider of quantum computing services to these organizations.

To date, these direct customer relationships have been with customers using QCS for general quantum computing research, algorithm development, algorithm benchmarking and software development activities. They represent a cross section of industries, government agencies and partners in the quantum computing ecosystem.

Indirect QCaaS Distribution

There are a large and growing number of providers of classical computing services over the cloud. This creates an opportunity for us to efficiently reach a broad set of end-users, indirectly, by partnering with cloud computing service providers, who in turn sell access to our quantum computer systems to their own customers.

The indirect distribution model is enabled by the same QCS platform used in the direct distribution model, allowing us to address the needs of customers in different market segments. In this instance, we can capitalize on our full-stack product development capabilities to meet the unique requirements of cloud-service providers. For example, one cloud provider or HPC operator might need deep and high-performance integration with a specific machine learning service they provide, while another might desire a fast and easy way for small customers to be introduced to quantum computing. We have distribution agreements with Amazon’s Braket service and Microsoft’s Azure Quantum Service, providing access to our quantum computing systems to AWS and Azure customers.

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Key Technology Development Partnerships

We enter into multi-year development partnerships with organizations that have specialized technical expertise and a strong interest in advancing their understanding and application of quantum computing technology. These partnerships can provide us with deep insight into the unique requirements of market leaders in key industries; advance our engineering and product development capabilities; and lead to the creation of new hardware and software products.

Examples of our development partnerships include contracts with:

We expect to add new development partnerships as the capabilities of our quantum computer systems grow and the market’s readiness and interest in quantum computing continues to mature.

Rigetti Foundry Services

Rigetti Foundry Services leverages the company’s U.S. based in-house wafer fabrication facility (“Fab-1”) to deliver superconducting quantum chips to advance and accelerate quantum information science and technology research and development efforts. Customers include researchers spanning academia, defense laboratories, and national laboratories.

Professional Services

In certain engagements, we provide professional services that enhance and advance our customers’ ability to consume our core products and services. Our engineers can augment a client’s internal capabilities with expertise in algorithm development, benchmarking, quantum application programming and software development. These fee-based services can enhance our customer’s readiness for quantum, accelerate our customer’s timelines for meaningful discoveries, and increase our depth of knowledge about key application domains and customer requirements for quantum computing in different industries.

Key Applications

Quantum computing is expected to drive value across many different applications and industries. We believe that many of the principal benefits in these areas will spring from four different types of computational problems that are particularly well suited to quantum computing: optimization, machine learning, simulation and quantum mechanical system simulation.

Optimization

Discrete optimization, also known as combinatorial optimization, focuses on problems where variables are restricted to specific, discrete values (e.g., 0 or 1). Unlike continuous variables, which can assume any value, discrete variables are constrained. The primary goal of this field is to determine the optimal assignment of values to these discrete variables to solve a given problem. This is typically achieved by formulating the problem around an objective function—a mathematical construct that one seeks to either minimize or maximize through the selection of the variables' values.

Such optimization problems are ubiquitous, spanning areas like logistics, routing, manufacturing, scheduling, telecommunication, energy, chemistry, biology, physics, finance, and basic science. For example, in financial services, optimization can be applied to portfolio management, algorithmic trading, and risk assessment. In telecommunications, optimization can be applied to call routing and network capacity planning. In manufacturing, optimization can help with workforce, warehouses, and supply chain planning. In transport, there are logistics applications like fleet routing, driver scheduling, and package loading and delivery that can benefit from further optimization. In energy, optimization can be applied to effectively deliver power distribution over the grid. In biology, optimization can be applied to accelerate or improve drug discovery processes.

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Optimization problems can be computationally intensive, and the run-time required for classical solvers to identify an optimal solution increases exponentially with the number of variables. For problems of meaningful scale, obtaining a provably optimal solution is effectively infeasible, necessitating the use of estimated or approximate solutions. As a result, optimization in practice focuses on identifying the best attainable solution within practical run-time limitations, which are shaped by industry-specific factors. Because even marginal improvements in solution quality can translate into meaningful gains in areas such as cost, timing, or resource allocation, there is demand for the development of more efficient and accurate optimization methods.

Quantum computers introduce a fundamentally different computational model that extends beyond classical computer systems. It leverages distinctive mechanisms—such as entanglement, superposition, and interference—to enable new algorithmic strategies for addressing complex optimization problems. These mechanisms underpin a range of quantum algorithmic frameworks, including quantum adiabaticity, variational quantum circuits, and quantum interferometry, among others, which may be applied in optimization contexts. While certain quantum optimization algorithms are designed to offer mathematical performance guarantees, others are developed as heuristics. Whether quantum optimization algorithms can outperform current state-of-the-art classical methods in terms of accuracy or run-time remains an open question.

We have conducted research in the field of quantum optimization for several years, pioneering original work and collaborating with experts to develop, understand, benchmark, and apply quantum optimization algorithms. We have examined the role of entanglement in quantum optimization, including its tradeoff with hardware noise, and have identified instances in which quantum optimization exhibits capabilities that exceed those of classical approaches. We have invented several quantum algorithms, and in 2025 we unified these techniques under the umbrella of quantum preconditioning. Quantum preconditioning is a quantum-boosted heuristic algorithm, which uses the quantum computer to modify a problem in a manner that makes it more readily solvable on a classical solver. We investigated the potential for QA on standard benchmark problems as well as an energy-grid optimization problem, targeting the QA window described above. We have also investigated other approaches to optimization, such as a qubit-efficient solver, a quantum-based multilevel approach, and quantum algorithms with co-designed circuits or logical gates. In addition to numerous peer-reviewed publications in leading journals, our works have been presented at a number of conferences nationally and internationally.

Our expertise in quantum optimization has been recognized through our participation in U.S. government programs such as DOD’s DARPA ONISQ (2020-2024), DOD’s DARPA IMPAQT (2023-2024), and DOE’s National Quantum Initiative with the superconducting quantum materials and systems center.

Machine Learning

Machine learning is a well-established computer science field that is already having a transformative impact on a myriad of markets today. At the core of any machine learning algorithm is a series of computations, typically linear algebra, applied to vast amounts of data that can reliably classify objects in pictures and make data-driven forecasts, for example. Today, cloud computing and HPC have been the predominant sources of the computational capabilities required to create and deploy effective machine learning algorithms, models and data analysis applications.

When faced with increasing amounts of data and while trying to grasp more complex patterns, the energy consumption of HPC-powered machine learning systems may become exorbitant due to computational and cooling demands. For that reason, computer scientists have looked toward the promise of higher computational efficiency of quantum computers, and the development of quantum machine learning (“QML”) algorithms, as a means of both accelerating current machine learning applications and creating new approaches that are currently impossible with classical computers and may lead to more efficient and accurate models.

Given these factors, the emerging field of QML is the focus of much of the current quantum computing research. We already see emerging machine learning algorithms that take advantage of the unique capabilities of quantum computing to tackle the complex linear algebra problems at the heart of many machine learning tasks. In fact, recent research has emerged demonstrating that quantum algorithms could work better than classical ones for critical machine learning classification problems. As algorithmic research continues to progress, some of these quantum algorithms are improving to the point where their benefits may be realized on smaller scale quantum computers.

Research has also demonstrated the promising application of QML, for Generative Adversarial Networks, (“GANs”), a deep learning technique where a neural network is used to generate highly accurate and new examples that could plausibly have come from an original dataset. The potential utilization of quantum computing for GANs alone is far-reaching and could be impactful in large markets like:

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The following are examples of our work related to machine learning:

In November 2023, we were awarded an Innovate UK grant as part of the Feasibility Studies in Quantum Computing Applications competition. Joining us in this work were Amazon Web Services (AWS), Imperial College London, and Standard Chartered. The consortium aimed to use quantum computing to improve current classical machine learning techniques used by financial institutions to analyze complex data streams. Financial institutions need to continuously interpret complex data streams to extract information necessary for providing accurate credit risk evaluation, managing market-making services, and predicting emissions in the context of green finance, among other things. Classical machine learning techniques used to assist and provide insights to these services have limitations as these data streams are, in general, complex. Combining quantum computing with classical machine learning methodology could offer more powerful resources for processing these data streams, given the potential for quantum computers to process some types of information more efficiently than with classical resources alone.

In October 2023, we were awarded a separate Innovate UK grant as part of the Feasibility Studies in Quantum Computing Applications competition. Joining us in this work were HSBC, the Quantum Software Lab (QSL) based at the University of Edinburgh, and the National Quantum Computing Centre (NQCC). Together, the consortium aimed to enhance existing anti-money laundering techniques by using quantum machine learning techniques with the goal of improving the performance of current-state-of-the-art machine learning algorithms. In this work, the consortium aimed to extend current anomaly detection quantum machine learning models to detect anomalous behavior indicating money laundering.

Simulation

Classical computers have been used for decades in critical applications that model real-world processes or systems in order to study their behaviors over time. These computer-based simulations have had an enormous impact on fields like pharmaceuticals, material science, finance, logistics, aerospace, defense and computer-aided design and engineering. Simulations are essentially mathematical models of a system and hence are logical candidates to benefit from quantum computing. Many important systems, such as molecular structures, cannot be accurately modeled due to the level of complexity associated with representing the properties and behaviors of the key elemental components.

We believe that quantum computers possess inherent advantages that will allow them to accurately model systems with large numbers of variables that are far outside the reach of classical computers today.

Quantum Mechanical System Simulations

The essential building blocks of nature, whose understanding has been the driver of many breakthrough innovations in pharmaceuticals, healthcare, energy, and material science, are the microscopic systems of molecules, atoms and subatomic particles like electrons and protons. The properties and behaviors of these quantum mechanical systems can be expressed in mathematical rules that have been verified experimentally with high degrees of accuracy, but the complexity associated with such calculations, and their applicability to existing and potential molecular and atomic structures, has proven to be outside the realm of capability for today’s classical computers.

Scientists have not found a way to rapidly and accurately model most quantum mechanical systems on a computational device that itself is not quantum in nature. Conversely, we believe quantum computers have the potential to efficiently model the relevant set of potential interactions between quantum mechanical elements because they natively reflect the essential properties of quantum systems and behaviors like entanglement, superposition and wave functions.

Drug discovery is among the fields where research into the applicability of quantum computing for simulating quantum mechanical systems is producing considerable enthusiasm. With the growing high costs to develop new drugs, a quantum-based approach that could help pharmaceutical companies evaluate thousands of potential compounds for a targeted therapeutic, and avoid failed outcomes in costly clinical trials, would have an enormously positive economic and societal effect. Other high potential impact areas for quantum mechanical simulations include the design of chemical catalysts, computational fluid dynamics in aerospace engineering, and nuclear fusion for clean energy.

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Our Technology

Introduction to Quantum Computing

Quantum computers encode and process data using a new kind of information storing electrical circuit called a quantum bit, or qubit. By leveraging the quantum mechanical principle of superposition, qubits can represent complex mathematical combinations of both zero and one at the same time. In contrast, classical computers are composed of transistors, electronic devices that hold binary zero or one states, therefore requiring billions of transistors in order to execute complex algorithms. This qubit property of superposition creates unique capabilities. By enabling qubits to encode more information than classical bits, it allows for a quantum computer’s power to scale exponentially, rather than linearly as with traditional computers based on transistors. Additionally, it makes it possible to construct algorithms that can evaluate all possible solutions to a problem simultaneously, rather than sequentially as is the case with classical computing. Furthermore, making qubits does not require expensive, continually shrinking lithography in order to improve performance, as transistor-based computers do. Qubits can be made using trailing edge semiconductor tools, so computer performance is decoupled from chip manufacturing cost.

These properties enable quantum computers to excel at solving problems with a large number of variables, highly complex and numerous solutions, or strong correlations or interactions. Many of these problems are currently intractable due to the scaling limits of classical computers and thus represent opportunities for computational advancement across many industries, including finance, pharma and biotechnology, energy, logistics, aerospace, defense and intelligence, and basic research and development.

How Quantum Computers Compute

To execute a quantum computation, classical data, which represents the problem to be solved and the algorithm, is translated into control sequences, or quantum logic gates, and applied to the qubits in the quantum computer. These sequences are called quantum circuits. Once the circuit has been executed on the quantum computer, the qubits are measured, resulting in classical data flowing out of the quantum computer and back into classical memory. The level of performance of a quantum computer in executing these circuits and solving computational problems is dictated by many factors. These include the scale, or number of qubits available in the quantum processor to encode the problem and algorithm, with more qubits enabling exponentially more complex and challenging problems to be represented; the fidelity of the quantum logic gates from which circuits are composed, which determines how often errors occur when the circuit is executed; the gate speed, which shapes the time taken to execute a given circuit; the co-processing technology and integration, which determines the rate at which classical data representing the problem and algorithm can be loaded into the quantum computer, and the rate at which it flows back out upon completion of the circuit execution; and re-programmability, or the speed with which the specific quantum circuit being executed may be updated to move on to the next step in a computational process.

Several candidate physical systems, or modalities, have been proposed or are being pursued, to form the basic physical qubits in quantum computers. These include, first and foremost, the superconducting qubit technology leveraged by us. They also include approaches based on trapped ions, trapped neutral atoms, and photonics. There is a varying degree of promise, potential and risk in building machines capable of meeting the above requirements for broad commercial utility. As outlined below, it is widely believed that superconducting qubit technology is the most mature, the most advanced, and most likely to ultimately lead to broad commercial success.

Requirements for Practical Workloads: Path to Quantum Advantage

Unlocking the broad commercial market for quantum computing calls for quantum computers that are able to solve practical commercial problems better, faster, or cheaper than the best alternative classical computing solution, including even the most powerful supercomputers. This inflection point is referred to as quantum advantage. Achieving quantum advantage imposes requirements on the quantum computer itself, the most important of which relate to the above performance factors of scale, fidelity, speed, co-processing, and re-programmability.

Scale. In order for quantum computers to solve problems out of reach for classical computers, such as modeling molecules with many electrons in order to enhance drug discovery, they require a significant number of high-performing qubits, likely starting at around 1,000 qubits.

Fidelity. A gate fidelity estimates the reliability of an operation. For instance, a two-qubit gate with a gate fidelity of 99% means that 99 out of 100 times the operation will provide the correct result. Errors can be caused by imperfect control, natural manufacturing variations, finite qubit lifetimes (coherence) or other sources. Overall, high fidelities of close to 99.9% are likely necessary to enable performance benefits on practical workloads. An error per operation is defined as (1-fidelity).

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Speed. Speed is a crucial metric for all types of computers, both quantum and classical. Since quantum algorithms are ultimately composed of logic gates applied sequentially to qubits in a quantum computer, the speed with which these gates can be executed translates directly into processing speed and workload throughput. Therefore, faster quantum processing speeds can result in a larger number of addressable problems and larger market opportunity, as well as a more direct path to outperforming classical alternatives and a higher intrinsic revenue potential.

Co-processing. Hybrid architectures that leverage quantum computers as co-processors, pioneered by us since the company’s inception, have now become widely adopted in the quantum computing industry. Quantum co-processing delivered over the cloud, such as our QCS platform, is the predominant framework for building and using quantum computers today. In this paradigm, quantum processors are tightly integrated with classical computing systems and infrastructure to ensure the rate of data flowing in and out of the quantum processor can meet the needs of commercial applications. Effective implementation of co-processing hinges on both the intrinsic technological features of the specific qubit technology, as well as product innovations and system architectures aimed to prioritize this capability. For example, just as in classical computing architecture, fast gate speeds, coupled with a network architecture that achieves low network latency for data flow, are some of the requirements for high performance co-processing.

Reprogrammability. Reprogrammable quantum computers are general purpose machines that should be able to run any quantum algorithm, provided the machine has the scale, fidelity, and other attributes needed to support the particular problem instance. While gate-model quantum computers, such as those made by us, IBM, IonQ and Google, are typically reprogrammable, different technology approaches and architecture choices lead to varying constraints in applying this capability in a practical setting. Specifically, the ability to dynamically reprogram the quantum processor during the execution of a quantum circuit or within the coherence time of its qubits is of particular importance for many anticipated applications and use cases.

While research and development funding and investments into quantum computing have accelerated, we believe that long-term commercial demand for quantum computing systems hinges on the ability to meet the above criteria for running practical workloads. Multiple quantum hardware modalities are being pursued. Among these, we believe the superconducting qubit is the only such modality that has, to date, demonstrated viability across all these requisite metrics.

Our Superconducting Quantum Processors

Introduction to Superconducting Qubits

We build and operate quantum computers based on superconducting qubits. Superconducting qubits are silicon-based electronic devices that encode information in quantum states associated with currents and voltages. Superconducting qubits benefit from the fact that their basic properties can be engineered through well-established semiconductor industry design and manufacturing techniques. This enables chip design and architecture tradeoffs to be made to overcome various practical constraints in building commercial quantum computing systems. They are also improving along these key metrics faster than approaches based on other qubit modalities, such as ion traps, photonics and neutral atoms.

As an example, in June 2011, the largest algorithms demonstrated on programmable, gate model quantum computers across these modalities were in the range of a few qubits. In the ensuing period from 2012 to 2025, superconducting systems have successfully scaled up to over 100 qubits, including demonstrations of quantum supremacy. This rate of scaling has easily outpaced other approaches. We believe this leadership results in part from an intrinsic advantage: superconducting qubits have many inherent similarities to traditional silicon-based chips. As a result, progress in superconducting quantum computers may be achieved by leveraging the existing capabilities – expertise, technologies, workforces, and supply chains, for example – of the semiconductor manufacturing industry, rather than needing to establish such capabilities anew.

Rigetti Quantum Processors

Rigetti quantum processors are based on transmon-style superconducting qubits. Quantum logic gates are actuated by applying electronic signals to the qubits. Chips are packaged, connected to input and output circuitry, and operated in a low-temperature environment. Control and readout signals are generated and processed in a control system operating at room temperature. This control system is subsequently integrated with, or networked into, auxiliary classical computing hardware to enable co-processing system requirements. Our competitive advantage begins at the chip level and extends through the full-stack, with a distinct focus on fabricating scalable hardware meeting the requirements for practical workloads.

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Scale

Achieving the scale of quantum processor needed for practical workloads is perhaps the hardest requirement of all. To address this, we have developed a unique patented and patent-pending multi-chip quantum processor technology. This approach leverages techniques long used in classical computer microprocessors and memory (“RAM”). Our scalable processor architecture enables multiple core processor chips, each having many qubits, within a multi-chip assembly to function cohesively as a single, large quantum computer-without introducing additional error sources, network latency or other overhead. Using our modular chip architecture, larger quantum processors may be constructed by assembling more core processors together. From a manufacturing perspective, this enables a single type of core processor chip to support multiple quantum processor generations of increasing scale and performance. We believe that this solution facilitates rapid scaling and can enable even faster development cycles in future chip generations.

In addition to accelerating the pace of scaling, we believe our proprietary modular chip architecture has significant manufacturability and cost benefits. For example, rather than producing large, complex individual chips with 1,000 qubits, we may fabricate 10 chips with 100 qubits each and use our multi-chip technology to assemble them together to produce a 1,000 qubit quantum processor. This solution makes it much easier to produce large processor chips with high yield. As a result, we believe our modular approach to be fundamentally more manufacturable, predictable, and scalable.

Our multi-chip technology incorporates several advances in integrated circuit design, architecture, and silicon device manufacturing. These advances include superconducting multi-chip bonding technology for chip-level 3D integration, superconducting through-silicon via process technology and interchip coupling technology that enables high-fidelity two-qubit logic gates between qubits disposed on different silicon dies. These innovations have resulted from our investment in more than five years of technological development to establishing the essential capabilities to produce quantum processors meeting the requirements for broad commercial utility. We believe our approach to scaling quantum computers will accelerate us toward quantum advantage systems.

Fab-1

We have developed, own and operate the distinctive manufacturing capabilities needed to produce quantum processors in our proprietary scalable architecture. In 2017, we became the first company to build a dedicated and integrated Fab for producing quantum processors. In addition to vertically integrating the process capabilities to produce our proprietary chips, Fab-1 delivers a high mix of development chips to internal teams. This in-house fabrication capability allows for rapid design-fab-test cycles of learning, enabling an innovation cycle we estimate to be two to five times faster than a typical MEMS or semiconductor foundry. In Fab-1, our engineers focus their efforts on rapidly exploring, then optimizing new chip designs and establishing repeatable manufacturing processes. Fab-1 also includes semi-automated chip testing and characterization capabilities. Additionally, by leveraging traditional semiconductor tools and processes, Fab-1 builds on expertise from the existing semiconductor industry, a distinct advantage over other qubit modalities.

This in-house fab capability has enabled us to accumulate the hands-on experience and intellectual property, including know-how, patents, and trade secrets, to produce quantum computer chips within our scalable, proprietary architecture. Furthermore, we believe Fab-1 has enough wafer capacity to supply all of our chip needs for at least the next three years.

Cooling

Like all high-performance computing systems, Rigetti quantum computers require an advanced cooling system. In this case, commercially available dilution refrigerators maintain chip temperatures at around 0.02 Kelvin. Cooling power requirements and associated electricity costs will scale approximately linearly with qubit count, while expected computational utility increases exponentially.

As a result, we expect the electricity costs to run the cooling systems of our quantum computers to make up an ever-decreasing fraction of the overall revenue generated from each machine. In addition, we work closely with refrigerator vendors and anticipate the commercial availability of dilution refrigerator systems with the capabilities to support our product roadmap.

Fidelity

Improvements to the coherence times of superconducting qubits, combined with methods for ever faster and more precise quantum logic gates, have kept superconducting qubits on a pace of continuous fidelity improvement for approximately two decades. In recent years, algorithms have been developed on processors with average two-qubit gate fidelities of 98-99.5%. As processors scale to broad quantum advantage, fidelity will need to continue to improve, likely to 99.9% and beyond.

We are focused on delivering advances to fidelity through a systematic engineering approach centered on our design-fab-test flywheel powered by our in-house design and manufacturing. Uniquely, our modular processor technology enables improvements to fidelity to be achieved separately from efforts to increase scale; fidelity advancements can be developed on the individual core processor chips, and these improvements can be rapidly integrated into scaled processors through our multi-chip integration technology.

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As described above, a quantum logic gate is how computation is expressed on a quantum computer. There are a large number of possible gates that can be used for computation. We physically implement quantum gates through the application of microwave pulses (electronic signals) to the physical qubits on the quantum integrated circuit.

One way that the performance of the system is assessed is by measuring the errors that are introduced in actuating the gates with the application of electronic signals to the physical qubits. There are a variety of metrics that are used to measure these errors; we currently report performance and indicate a measure of error through a fidelity metric applied to two-qubit gate error or fidelity, usually expressed as a percentage. Gate fidelity represents the reliability of an operation. For example, a two-qubit gate with a fidelity of 99% means that 99 out of 100 times the measurement of the gate will produce the correct result. Fidelities are related to errors in the following way: 100% - error rate % = gate fidelity %. So, an error rate of .5%, is the same as a fidelity of 99.5%. There are a number of standard benchmarks that are used to measure qubit errors and are explained further below.

We measure the performance of iSWAP and CZ gates with an industry standard technique called Randomized Benchmarking, a commonly used method to measure fidelity. This protocol requires creating random sequences of quantum gates of different sequence length, executing each sequence, and then measuring the outcome of the execution against the mathematically expected results.

On iSWAP-enabled devices, such as Ankaa-3, we also implement a family of two-qubit gates referred to as fSim. Generally, any specific fSim gate may not be part of a universal gate set. We use fSIM gates with the goal of achieving high performance for specific algorithms.

We measure the performance of fSim gates with an industry standard technique called cross entropy benchmarking, another commonly used method to measure fidelity. This protocol requires creating random circuits from the provided gate set measuring the results and comparing the outcomes to an expected probability distribution of outcomes.

In the past we have implemented gate sets based on two-qubit gates other than CZ, iSWAP and fSIM, and may, in the future, choose different gate sets. At the moment there is no standard set of gates agreed on in the industry, and there may never be. Furthermore, other standards for measurement may emerge to measure quantum gate fidelity or performance of quantum computers generally. Accordingly, undue reliance should not be placed on the fidelity measures that we present. See also “Risk Factors— We face significant technical and engineering challenges in completing the development of our quantum computers, producing our quantum computers at scale, achieving our targeted performance milestones, and realizing quantum advantage or LFTQC, any of which if not accomplished would adversely impact our business, financial condition, and results of operations.”

Cepheus-1-36Q is our newest flagship quantum computer featuring our proprietary modular chip architecture, optimized two-qubit gates and advances in intermodule coupler design that is intended to enable superior performance. With Cepheus-1-36Q, we successfully halved our error rates from our previous Ankaa-3 system, achieving a median two-qubit fidelity of 99.6% (based on internal testing) as of January 2026. We believe Cepheus-1-36Q is the first multi-chip quantum computer in the industry to achieve this level of performance based on publicly available information. Improving our median two-qubit fidelities is a crucial part of our mission to build the world’s most powerful computers. Useful quantum computers will need not only a large number of qubits, but also high-quality qubits. Reaching 99.6% median two-qubit fidelity on the Cepheus-1-36Q system is the result of years of innovation and commitment from our teams across the technology stack.

We believe that Cepheus-1-36Q validates our modular architecture approach to scaling. Tiling multiple chips together demonstrates what we believe is the way forward towards building larger systems. We believe a densely connected square lattice with tunable couplers that allows us to control qubit interactions is the foundation for driving qubit performance. We believe a 2.0x improvement in error performance compared to our previous QPUs, coupled with our scaling approach, shows that we have a promising strategy for building increasingly higher performing QPUs to help our customers solve their most pressing problems.

Speed

One of the strengths of superconducting qubit technology, and our technology in particular, is that gate operations on superconducting processors are faster than other commercially available modalities today.

The speed of gate operations in superconducting qubits are determined by the intentional design of circuit elements on-chip and their optimized parameters, rather than relying on atomic properties. Our recently introduced Cepheus-1-36 system achieves a median gate time of 76 nanoseconds with universal CZ gates. Median gate time is measured by internal testing.

We believe that superconducting processors’ speed advantage will result in a larger market for superconducting quantum computers compared to other modalities, as there are a multitude of high value use cases that require timely results, such as real-time decision making, risk calculations, and more. As in conventional computing, faster gate speeds also equate to higher throughput in commercial deployment and therefore greater potential revenue opportunity.

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Co-processing

It is widely believed that unlocking the commercial value of quantum computing requires quantum computers to be tightly integrated with classical computing systems and technology. High-performance co-processing integration accelerates the path to quantum advantage by enabling both quantum and classical computing resources to work in tandem to address computational bottlenecks best suited to their particular strengths. This approach also facilitates adoption and usability by end users who are more familiar with classical programming.

The inherent speed with which superconducting processors can execute circuits and be dynamically re-programmed makes them ideally suited to high-speed co-processing integration. Other modalities have not demonstrated the gate speeds necessary to support high-performance co-processing.

We have invented and patented capabilities at the hardware and software level, such as parametric code compilation, to enable high performance co-processing on a cloud platform. Parametric code compilation supports running faster hybrid algorithms through memory registers shared between classical programs and embedded logic on a QPU control system. This means that users can run algorithms without incurring latency that would otherwise be caused by updating parameters at each step.

Reprogrammability

Our systems are dynamically reprogrammable. Instructions are streamed into the quantum computer or updated within the execution time of the quantum logic circuit. This allows our machines to effectively run both the hybrid variational algorithms that underpin current use cases and quantum error correction routines in future systems. In a production setting, dynamic reprogrammability translates to higher customer job throughput per unit time. Since many applications are expected to require streamed data processing or error correction, we believe this dynamic reprogrammability is central to unlocking the full market potential of quantum computing systems, especially in comparison to alternative modalities that are unable to implement high speed re-programming.

Our quantum computers are orchestrated with a control system operated at room temperature. In our architecture, reprogramming the quantum processor occurs exclusively within this control system. Unlike photonics, for example, reprogramming the system to run a new quantum circuit does not require slow on-chip updates, but only requires changes to the sequence of signals applied to the chip.

Our QPUs today support dynamic programming protocols within microsecond feedback loops. For example, re-setting registers of qubits conditional on the outcomes of previous measurements, can increase overall quantum circuit throughput by 5x relative to non-dynamic implementations of the same workload.

The QPU control system includes hardware for networking, classical microprocessors, field-programmable gate arrays (FPGAs) for control and readout pulse sequencing, and analog signal processing. The integrated system is designed and built to meet the requirements for co-processing and reprogrammability over the cloud.

This capability enables high-speed data flow within the quantum processor, and between the quantum processor and auxiliary classical compute and networking infrastructure. Our systems are thus enabled for high-performance hybrid quantum-classical computing, the implementation of high-throughput quantum programs for practical workloads, and the dynamic control flow and feedback that underpins practical quantum error correction. The control system drives the quantum processor, calibrates and operates gates, and measures qubit states at the end of a computation.

Quantum Error Correction

Direct improvements to qubits and gate fidelities are currently the primary means of advancing the performance of quantum computers. However, at the scale of a few hundred qubits and beyond, a method called quantum error correction can be applied to further accelerate this rate of progress.

In quantum error correction, a large number of individual physical qubits can be transformed, through repeated application of gate and readout operations designed to detect and fix physical errors, into single “logical” qubits, whose properties are exponentially improved relative to the constituent physical qubits. While the methodology of quantum error correction is well-established in the field of quantum computing, systems capable of running such codes at a commercially useful scale are not currently available. Eventually, solving certain classes of problems will require the ability to compute with tens to hundreds or even thousands of logical qubits. This makes the ability to build large qubit count processors at this commercial scale an even more crucial capability.

Additionally, because errors must be identified at a specific physical location within the quantum processor in order to be corrected, those errors must also be well-localized within small regions of the quantum processor. For example, a qubit in one region must not induce errors on some distant qubit but rather be constrained to influencing errors on nearby qubits. This essential requirement underpins modern quantum error correction theory and practice.

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Turning to the processor’s physical qubit array, the necessity of localizing errors has led to the predominance of nearest-neighbor connectivity graphs in quantum processor design. Our quantum processors meet these essential requirements with a nearest-neighbor, planar connectivity graph. Planar codes are expected to show a high error threshold of approximately 1% error probability per operation. This means that if error rates are below the required threshold (e.g., 1%), then increasing the redundancy (i.e., the number of physical qubits making up a single logical qubit) results in an exponential reduction in logical error. In other words, adding a small number of additional physical qubits per logical qubit will provide exponential improvements. Notably, codes for other modalities, such as Bacon-Shor codes for trapped ion qubits, lack such a threshold behavior and is one reason why we believe superconducting quantum computers to be superior to trapped ion modalities.

We aim to deliver the physical qubit count needed, with the requisite nearest-neighbor connectivity, to enable developers and customers to benefit from this exponential error reduction. In contrast to known approaches for other qubit modalities, our systems are expected to be able to run the same code family at multiple different levels of redundancy without requiring additional complexity such as code concatenation. This approach enables developers to scale the effective error rate and associated overheads up and down as dictated by their use-case requirements. For example, the smallest surface code logical qubit for superconducting processors is 17:1 physical qubits to logical qubits, in comparison to 16:1 for trapped ions. However, for complex applications, the ability to pack more physical qubits into the code (such as 100:1 or 1000:1) is critical because it allows developers to further reduce errors for algorithms based on many quantum gates where errors are more likely to accumulate. In comparison to trapped ions, we believe superconducting processors are better positioned to scale up to the large number of qubits required to run these valuable large codes while also having the fast gate speeds for them to be useful.

Our processor architecture, software tools, and cloud services platform are designed to enable users and partners to directly construct, test and deploy error correction and error mitigation protocols, and to tailor such codes to specific computational tasks through software. This capability is enabled by the re-programmability, co-processing integration, and system design we have established.

Intellectual Property

Our intellectual property portfolio plays a strategic role in advancing our innovation and leadership in quantum computing.

Our patent portfolio seeks to protect our current developments and the intellectual property space for the company’s technology roadmap and anticipated areas of development. We rely upon a combination of protections afforded to owners of patents, copyrights, trade secrets, and trademarks, along with confidentiality and proprietary rights agreements with employees, consultants, contractors, vendors, and business partners to establish and protect our intellectual property rights.

As of December 31, 2025, we have 121 patents issued and 160 patents pending that are designed to protect our full-stack technology across hardware, software, and services. These patents cover a broad range of key technology areas of the business including: (i) quantum computing systems, software, and access; (ii) quantum processor hardware; (iii) algorithms and applications for problem solving; and (iv) chip design & fabrication.

We pursue international registration of our domain names and trademarks. We are the registered holder of a variety of domain name registrations, including “rigetti.com.” Our trademark registrations include “Rigetti” in the US, U.K. and EU.

Sales & Marketing

During this period of emerging quantum advantage, our go-to-market strategy is focused on being a leader in the key market segments driving the early application of quantum computing. Our sales and marketing efforts are focused on technology development and distribution partnerships with the leading organizations in these markets. In the U.S. government, for example, the Departments of Defense and Energy have each been making significant investments in quantum computing. We have technology development partnerships with leading government agencies and national laboratories.

We are pursuing similar arrangements with customers in other important vertical market segments, like finance, where we are developing specific expertise in several application areas and are collaborating with Moody’s, HSBC and Standard Chartered Bank. We also have distribution relationships with customers like Amazon Web Services, Microsoft Azure and Strangeworks.

As we work to develop new generations of our hardware with the goal of continuing to scale and achieve QA, we anticipate increasing our investment and expenses in both sales and marketing in the future to expand the number of enterprise companies buying our QPUs and directly licensing our QCS platform.

Suppliers

We source our components from multiple industries including from the electronics and semi-conductor industries with low-noise microwave components, CPUs, GPUs, FPGAs; from the cryogenic industry with dilution refrigerators and associated helium gas products; and from the semiconductor industry with silicon wafers and other specialty materials, tooling and measurement equipment.

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Customers & Key Partners

We believe that the realization of quantum computing’s promise requires strong relationships across an ecosystem of innovative and quantum-committed organizations and have been developing commercial relationships and collaborative partnerships with organizations that possess a keen understanding of specific industry problems and deep technical expertise in key scientific and engineering disciplines.

To date, we have focused on developing a range of client relationships and research partnerships with:

Competition

The quantum computing market is evolving and highly competitive. With the introduction of new innovations and the potential entry of new competitors into the market, we expect competition to increase in the future, which could harm our business, results of operations, or financial condition.

Our current and prospective competitors include companies engaged in the research, development, and operation of quantum computing capabilities. Major companies now developing both quantum hardware and software include IBM, Google, Microsoft, IonQ, D-Wave, Quantinuum and PsiQuantum, among others. In addition, because of the importance of quantum computing, most large public cloud providers and traditional chip makers are researching and investing in quantum computing initiatives, in some cases seeking to build quantum computers. For example, Amazon is engaged in the research and development of quantum computers. A number of development-stage companies are also seeking to build quantum computers, quantum software and applications, and quantum cloud computing services.

We believe our primary direct competition will come from other companies building or seeking to build universal, gate-model quantum computing systems that can meet the requirements for solving commercial problems. We believe competition will be based on a number of factors, including: different approaches to building quantum computers; quantum computer system performance, including scale, speed, and fidelity; system accessibility and ease of use; supported software and applications; compatibility with existing classical workflows; rate of technological innovation; ability to create value through long-term partnerships; end-user support and customer experience; solutions and insight delivery; price; brand recognition and trust; financial resources; and access to key personnel.

We believe that we are favorably positioned to compete on the basis of these factors. However, we face various risks relating to competition as described in “Risk Factors-Risks Related to Our Business and Industry-The quantum computing industry is in its early stages and volatile and is competitive on a global scale and we may not be successful in competing in this industry or establishing and maintaining confidence in our long-term business prospects among current and future partners and customers.”

Regulatory

U.S. government contracts, grants, and agreements are subject to regulations and procurement laws. The majority of our current programs are subject to Title 2 of the Code of Federal Regulations, covering Grants and Agreements. We also perform programs authorized under Other Transaction Authority and the Federal Acquisition Regulation. Several of our agreements are also subject to agency level acquisition regulation supplements, including the Defense Federal Acquisition Regulation Supplement and the Department of Energy Acquisition Regulation. These regulations mandate uniform policies and procedures for the administration of government funded programs. This includes requiring compliance with eligibility and responsibility requirements, contractor qualifications, financial and reporting requirements, as well as subjecting the company to audits and to other government reviews covering issues such as cost, performance, internal controls and accounting practices.

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Our products and technologies are subject to U.S. export control and import laws and regulations, including the U.S. Export Administration Regulations, U.S. Customs regulations, and various economic and trade sanctions regulations administered by the U.S. Treasury Department’s Office of Foreign Assets Controls. U.S. export control and economic sanctions laws include restrictions or prohibitions on the sale or supply of certain products, technologies, and services to U.S. Government embargoed or sanctioned countries, governments, persons and entities. In addition, certain products and technology may be subject to export licensing or approval requirements. Exports of our products and technology must be made in compliance with export control and sanctions laws and regulations.

We are also subject to numerous U.S. state, federal and foreign laws, regulations and rules related to privacy, data use and security. In addition, we are subject to the U.S. Foreign Corrupt Practices Act of 1977, as amended, the U.S. domestic bribery statute, the U.S. Travel Act, and other anti-bribery, and anti-corruption laws in countries in which we conduct activities, and numerous federal, state and local environmental laws and regulations governing, among other things, solid and hazardous waste storage, treatment and disposal, and remediation of releases of hazardous materials.

See also “Risk Factors—Risks Related to Litigation and Government Regulation.”

Employees

Our deep and talented workforce is the key to our success. As of March 1, 2026, we employ 164 people globally, of which 162 were full-time employees. The majority of our employees are employed in the areas of quantum physics, chip and hardware engineering and software development. Most of our employees are based in the United States with the remainder based in the United Kingdom, Australia and Canada. In addition, we also engage a small number of consultants and contractors to enhance our research and development and selling general and administrative areas of our business.

To date, we have not experienced any work stoppages and maintain good working relationships with our employees. None of our employees are subject to a collective bargaining agreement or are represented by labor unions at this time.

Corporate Information

Rigetti Computing, Inc., formerly known as Supernova Partners Acquisition Company II, Ltd. (“Supernova”), was incorporated on December 22, 2020 as a Cayman Islands exempted company and a special purpose acquisition company.

On October 6, 2021, Supernova entered into an Agreement and Plan of Merger (the “Merger Agreement”) with Supernova Merger Sub, Inc., a Delaware corporation and a direct wholly owned subsidiary of Supernova (“First Merger Sub”), Supernova Romeo Merger Sub, LLC, a Delaware limited liability company and a direct wholly owned subsidiary of Supernova (“Second Merger Sub”) and Rigetti Holdings, Inc., a Delaware corporation (“Legacy Rigetti”). Pursuant to the Merger Agreement, on March 1, 2022, Supernova effected a domestication after which it continues as a Delaware corporation, changing its name to “Rigetti Computing, Inc.”

On March 2, 2022, pursuant to the Merger Agreement, First Merger Sub merged with and into Legacy Rigetti, the separate corporate existence of First Merger Sub ceasing and Legacy Rigetti being the surviving corporation (the “Surviving Corporation” and, such merger, the “First Merger”) and (ii) immediately following the First Merger, the Surviving Corporation merged with and into the Second Merger Sub, with the separate corporate existence of the Surviving Corporation ceasing and the Second Merger Sub being the surviving entity and changing its name to “Rigetti Intermediate LLC”.

Our principal executive offices are located at 775 Heinz Avenue, Berkeley, CA 94710 and our telephone number is (510) 210-5550.

Available Information

Our corporate website address is www.rigetti.com. We make available on our website, free of charge, our Annual Reports on Form 10-K, our Quarterly Reports on Form 10-Q and our Current Reports on Form 8-K and any amendments to those reports filed or furnished pursuant to Section 13(a) or 15(d) of the Exchange Act, as soon as reasonably practicable after we electronically file such material with, or furnish it to, the Securities and Exchange Commission (the “SEC”). The SEC maintains a website that contains reports, proxy and information statements and other information regarding our filings at www.sec.gov. We use our corporate website as a channel of distribution of material company information. For example, financial and other material information regarding our company is routinely posted on and accessible on our website. Accordingly, investors should monitor this channel, in addition to following our press releases, SEC filings and public conference calls and webcasts. The information found on our website is not incorporated by reference into this Annual Report on Form 10-K or any other report we file with or furnish to the SEC.

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