Item 1. 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.
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. 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. A July 2021 Boston Consulting Group report predicts that fully fault tolerant quantum computers could ultimately produce between $450 billion and $850 billion in annual value creation on an operating income basis for end users after 2040.
To unlock this opportunity, 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 a clear advantage over classical computers.
We are a vertically integrated company. We own and operate Fab-1, a unique 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 QCS platform, and also through cloud service providers.
We have developed strong customer relationships and collaborative partnerships to accelerate 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, Ampere, Astex Pharmaceuticals, Deloitte, Microsoft, Nasdaq and Standard Chartered Bank, along with U.S. government organizations such as DARPA, DOE, and NASA.
The company is 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.
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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-premise. Company management estimates the global market for HPC to be approximately $54 billion by 2027. We believe our 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.
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 that 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.
Emerging Quantum Advantage (“eQA”) Phase
This phase is characterized by the availability of practical, fully functional and operational quantum computers, whose capabilities do not yet enable them to demonstrate clear performance advantages relative to traditional computers. Currently, our quantum computers are of sufficient scale and capability to be useful in applied research for quantum algorithm development, the exploration of potential applications of quantum computing, and for understanding the skill gaps an organization must resolve in order to be prepared to take advantage of quantum computing capabilities.
We consider the eQA phase to have begun three years ago, and during this time we have worked with business and government researchers, commercial software developers and academic institutions who access our quantum computers via cloud-based services.
We anticipate that indications that this phase is coming to a close will occur when there are repeated demonstrations solving practical problems, of substantial commercial or customer value, with a level of performance that is competitive with the best available classical computing performance.
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Narrow Quantum Advantage (“nQA”) Phase
If and when our quantum computing processing capabilities have scaled to the point where they can be used to solve practical, operationally relevant problems with improved accuracy, speed or cost over classical computers, we believe we will have reached the phase of nQA.
In the nQA phase, we expect that large enterprises and government organizations would increase their investment in quantum computing as the superior computational capabilities of the technology will have progressed from projected to verifiably advantaged for certain applications. In addition to quantum-based research and development, quantum machine learning (“QML”) is likely to emerge as a strong avenue for growth as it can be leveraged in a wide range of business and scientific applications. Research into quantum simulation and quantum optimization opportunities is predicted to increase in the nQA phase.
Broad Quantum Advantage (“bQA”)
We will consider the phase of bQA to have begun if and when our quantum computing processing abilities have scaled to the point where they can be used to solve practical problems that would be physically impossible to solve on any classical computer. At such point, with both scaled qubit counts and strong error correction capabilities, we believe our quantum computers would be suitable for many applications of quantum machine learning and begin to be used for a growing number of quantum simulation and quantum optimization problems. In the event we demonstrate bQA, we expect many new potential clients to emerge as the range and value of the problems that are addressable by our quantum computing systems significantly increases.
Large-Scale Fault Tolerant Quantum Computing (“lFTQC”)
We will consider the phase of lFTQC to begin when 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 likely requires systems with 10,000 to 1,000,000 physical qubits. We believe our scalable multi-chip architecture paves the way to scale up to these large systems.
We anticipate the beginning of the large-scale fault tolerant phase to be likely at least a decade away. As quantum computing further matures through this phase, systems will likely continue to grow in scale and performance, culminating in full-scale fault tolerance that operates using potentially thousands of effectively perfect logical qubits. This ultimate goal of full-scale fault tolerance represents the largest commercial opportunity at an estimated $850 billion per year in potential annual value creation for end users and technology providers.
Business Strategy
Our approach to developing and sustaining strong competitive advantage relies on a four-pronged strategy:
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Create high performance quantum computing systems through full-stack product development. From the outset, we have approached the market opportunity with a strategy to build quantum computers, the superconducting processors that power them, and the software required to access and program these systems. We believe that vertical integration, from chip manufacturing through cloud delivery, unlocks the fastest and lowest risk path to broad commercialization and the largest, long-term market opportunity. This was underscored by our announcement of the industry’s first multi-chip quantum processor for scalable quantum computers, a capability realized through many innovations from Fab-1.
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Leverage cloud to provide broad access to our quantum computers. We have been providing cloud access to our quantum computers since 2017 and have since expanded the availability of our machines through distribution agreements with other solution providers including Amazon Braket, Microsoft, Oak Ridge National Laboratory (“ORNL”) and Strangeworks. Cloud services efficiently simplify access to our quantum computers and allow for pricing that enables a broad range of scientific, commercial and academic developers to readily participate in the development of quantum computing algorithms, applications and software development tools. Collectively, these cloud services provide a range of choices and capabilities designed to meet the diverse needs of large and small organizations alike.
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Develop deep partnerships that accelerate the development and commercialization of quantum computing. We have formed commercial partnerships with business and government entities that are designed to advance their mutual understanding of the opportunities, challenges and solutions necessary for quantum computing to excel in specific real-world applications. Examples of these partnerships include our contracted relationships with DARPA, the DOE’s Fermi National Accelerator Laboratory (“Fermilab”) and ORNL. We believe these types of highly collaborative, multi-year relationships will yield specialized and proprietary market insights and technological advancements. We expect the number and scope of these types of partnerships to expand as the capabilities of our quantum computers continue to grow.
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Advance our technology leadership position . We have invested heavily in a world-class and multidisciplinary team of scientists, hardware and software engineers, system designers and algorithm and application developers to rapidly innovate, invent, engineer and commercialize our quantum computing technologies. We have also developed numerous proprietary technologies required to create quantum computing chips, quantum computer systems, software and cloud-based services and we rigorously protect our unique intellectual property through a portfolio of 165 patents issued and pending (as of the date hereof). We intend to continue deeply investing in finding and fostering the talent required to remain at the forefront of quantum computing innovation, while protecting our growing base of intellectual property.
In February 2023, we updated our business strategy and revised our technology roadmap to focus on nearer-term priorities and focus efforts to achieve narrow quantum advantage. We made further refinements following our internal deployment for testing in March 2023 of Ankaa-1, our 84-qubit system delivering denser qubit spacing and tunable couplers.
We now plan to:
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Concentrate on refining the performance of Ankaa-1.
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Upon the anticipated external launch of the Ankaa-1 84-qubit system, which is expected to be to select customers, continue efforts to improve the performance of the system with the goal of reaching at least 98% 2-qubit gate fidelity to support the anticipated Ankaa-2 84-qubit system.
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Launch the anticipated Ankaa-2 84 qubit system, continuing to work to improve performance with the goal of reaching at least 99% gate fidelity on Ankaa-2.
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If the above targets are achieved, we plan to shift focus to scaling to develop Lyra, an anticipated 336-qubit system.
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 recurring revenues generated from quantum computing systems made accessible via the cloud in the form of QCaaS products.
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Quantum Computing as a Service (QCaaS)
We design, build, own, and operate quantum computers and sell access to these systems 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.
Rigetti Quantum Cloud Services
The company’s flagship product is Rigetti Quantum Cloud Services. QCS is a platform to deliver high-performance quantum computing over the cloud. QCS features a hybrid quantum-classical computing environment that incorporates Rigetti 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 our quantum processing units (“QPUs”), and our quantum operating system, as described below.
Rigetti Quantum Processing Units . At the heart of QCS are the proprietary QPUs that perform quantum computations. 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.
Rigetti QPUs are designed and fabricated at Fab-1, leveraging novel manufacturing methods to create state-of-the-art superconducting qubits.
Production versions of QCS currently utilize our Aspen-M series chips with 80 qubits.
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:
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Enabling customers to access Rigetti QPUs through a broad range of quantum application software, development frameworks and algorithm libraries;
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Providing software and algorithm developers with the performance and fine-grained control required to expedite a new era of computational breakthroughs; and
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Facilitating the implementation of high performance public and private clouds with ultra-low latency connectivity between classical hardware and Rigetti QPUs.
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Rigetti’s quantum computing facility in Berkeley, California includes both research and development and production quantum processing units, which are each housed in a cryogenic refrigerator.
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 the company’s full-stack product development approach, and strategy of forging collaborative customer partnerships, positions the company 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.
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The indirect distribution model is enabled by the same QCS platform used in the direct distribution model, providing us with powerful business leverage in addressing 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 signed a distribution agreement with Amazon’s Braket service and Microsoft’s Azure Quantum Service, providing access to our quantum computing systems to AWS and Azure customers. We have also signed a distribution agreement with ORNL, a U.S. government entity that provides state-of-the-art computational infrastructure to government researchers. Similarly, we have signed a distribution agreement with Strangeworks, a provider of quantum computing enablement software, services and computational resources.
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:
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Fermilab and the U.S. DOE’s Superconducting Quantum Materials and Systems Center (“SQMS”), to advance the development of scalable and high performance quantum processors;
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DARPA and National Aeronautics and Space Administration (“NASA”) to create quantum computing systems, software and algorithms for optimization applications; and
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Innovate UK, as part of the British government’s effort to accelerate commercialization of quantum computing in the United Kingdom and to pursue practical applications in machine learning, molecular simulation and financial optimization.
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 US-based in-house fabrication facility 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.
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A Rigetti employee inspects a silicon wafer with superconducting quantum integrated circuits that was fabricated at Rigetti’s Fab-1 facility.
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
The computational properties of a quantum computer inherently support the problem-solving requirements of extremely complex optimization calculations because quantum computers possess the ability to simultaneously evaluate very large numbers of variables, and each additional qubit in a quantum computer exponentially scales our information processing capacity. We believe that quantum computers could allow highly accurate optimization models to be continuously refreshed to reflect the impact of changing conditions on available solutions, ultimately leading to better and more responsive plans and decision-making.
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Many of the world’s largest and most valuable industries could potentially benefit from enhanced optimization enabled by quantum computing. In financial services, optimization could be applied to portfolio management, algorithmic trading and risk assessment. In telecommunications, optimization could be applied to call routing and network capacity planning. In manufacturing, optimization could 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 could benefit from further optimization.
These types of problems can quickly overwhelm classical computers due to the large numbers of variables that need to be evaluated, which exponentially scales the computational power required with each additional possibility to be considered. For example, in a vehicle routing problem involving roundtrips to just 10 destinations there can be more than 300,000 permutations to be considered; with 15 destinations, the number of possible routes exceeds 87 billion. If you factor in other real-world considerations such as delivery cost, fleet size, driver availability, or service level agreements, you can see the intractable nature of these kinds of problems in full display.
One of the most active fields of quantum algorithm research is the area of constrained combinatorial optimization. These mathematical equations can arrive at approximate results with a close-to-optimal solution across many possible outcomes-a result that would create high value in many different industries, particularly when the exact solution is unknowable utilizing a classical computer.
We are exploring the application of our quantum computers for high value optimization problems including a partnership with NASA and DARPA for secure dynamic message scheduling using high-demand space and national security assets. In January 2022, we were selected to deliver hardware, software and benchmarks for Phase 2 of DARPA’s ONISQ program to develop quantum computers capable of solving complex scheduling optimization problems, with a focus on quantum advantage for currently available or soon to be available quantum processors. The award was based upon us successfully completing our performance milestones in Phase I of the program, and is targeting Rigetti’s Aspen-M 80 processor, and our next generation Ankaa processor.
In March 2022, we were selected to lead a program to develop benchmarks for quantum application performance on large-scale quantum computers as part of DARPA’s Quantum Benchmarking Program. The goal of this program is to re-invent key quantum computing metrics, make those metrics testable, and estimate the required quantum and classical resources needed to reach critical performance thresholds
Machine Learning
Machine learning is a well-established field, with broad application, that today is already having a transformative impact on a myriad of markets. The potential market opportunity for machine-learning is currently estimated at $16 billion with expected compound annual growth rates through 2028 of 39%, according to market research from Fortune Business Insights. Boston Consulting Group projects that machine learning applications with fully fault tolerant quantum computers could produce $150 billion to $220 billion in annual potential value creation for end users and technology providers. At the core of any machine learning application is a series of computations, typically expressed in linear algebra, applied to vast amounts of data in order to do things such as reliably classifying objects and making data-driven predictions. Today, cloud computing and HPC have been the predominant sources of the computational capabilities required to create effective machine learning algorithms, models and data analysis applications.
But, the efficiency of HPC-powered machine learning algorithms is limited when faced with richer and larger data sets. For that reason, computer scientists have looked toward the computational promise of quantum computers, and the development of quantum-based algorithms, as a means of both accelerating current machine learning algorithms and creating new approaches that are currently impossible on classical computers.
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Given these factors, the emerging field of QML is the focus of much of the current research and development occurring on quantum computers today. 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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healthcare - for medical image analysis used to detect and categorize tumors and predict their growth;
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drug discovery - for generating molecular structure candidates for medicines to target or cure diseases;
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finance and banking - for creating models that can detect financial fraud based upon predictive patterns rather than rules determined by previously observed behaviors; and
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defense and intelligence - for reliably enhancing low resolution satellite imagery into high resolution photography.
In 2022, we previously partnered with researchers at a U.S. government agency on a generative modeling application for weather forecasting. In this instance, we leveraged a combination of classical and QML techniques to produce high-quality synthetic weather radar data. Meteorological scoring metrics for storm prediction were in some cases augmented using the QML relative to the purely classical implementation. With additional refinement to the underlying methods, combined with the current pace of scaling and performance improvements in quantum hardware, the researchers believe the synthetic weather data application could be a strong candidate for quantum advantage and operational deployment.
In addition, we believe QML for finance is poised to be an early domain of quantum advantage. We recently partnered with Standard Chartered using QML to provide a deeper understanding of QML capabilities and the value of their datasets.
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.
The global market for simulation software alone is projected to grow from $12.7 billion in 2020 to $26.9 billion in 2026 according to Markets & Markets. BCG projects that simulation applications with fully fault tolerant quantum computers could produce $160 billion to $330 billion in annual potential value creation for end users and technology providers over the next 15 to 30 years. 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.
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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.
We have several active partnerships with clients developing simulations of quantum mechanical systems. One such partnership is with Astex Pharmaceuticals, a United Kingdom-based company, which is working with our quantum computers on approaches that may speed up the process of drug discovery. We are also partnering with two U.S. DOE agencies on simulation applications in the areas of nuclear fusion and high energy physics.
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.
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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 between a few hundred to 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 over 99% are likely necessary to enable performance benefits on practical workloads. An error per operation is defined as (1-fidelity).
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 per unit time.
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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 Rigetti Quantum Cloud Services 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 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 ten-year period from 2011 to 2021, superconducting systems have successfully scaled up to the range of 30 to 60 or more 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.
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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.
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.
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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 technology 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.
Rigetti’s proprietary multi-chip architecture enables larger quantum processors to be constructed by assembling individual chips together, thereby supporting multiple quantum processor generations of increasing scale and performance
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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 four 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. Over the last several years, algorithms have been developed on processors with average two-qubit gate fidelities of 95-98%. As processors scale to broad quantum advantage, fidelity will need to improve, likely to 99% 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.
Our commercially available 80-qubit Aspen systems typically have similar gate fidelities to its 32-qubit systems. Significantly, our 80-qubit multi-chip processors leverage our interchip coupling technology to form an 80-qubit lattice of similar fidelity to the 40-qubit chips on which they are based. Looking forward, we plan to combine core elements of this multi-chip scaling technology with the expected higher performance and connectivity of our next generation chips.
We currently see higher performance in test devices of our next generation core quantum processor, the Ankaa-1 84 qubit system, which we recently deployed internally within the company in March 2023 for testing. This next-generation chip design uses tunable couplers to enable active cancellation of unwanted interactions between qubits to improve control and decrease error. Recent tests on a 9-qubit system utilizing these new chips have demonstrated mean two-qubit gate fidelities of 98.3%, median fidelities of 98.1% and maximum fidelities of 99.6%. As development has progressed, we continue to see high two-qubit gate fidelities around 99% on test devices.
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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 slowest class of gate operations, two-qubit entangling gates, have a median duration of less than 200 nanoseconds. Moreover, for future computer systems from us, high quality entangling gates as fast as 36 nanoseconds have recently been achieved through the introduction of an additional circuit element to tune the interaction strength between qubits, showcasing the value of engineered approaches. 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.
In February 2022, we announced speed test results for Circuit layer operations per second, or CLOPS, for our Aspen 11 and Aspen-M series processors. CLOPs is a quantum computer performance metric initially developed and published by IBM in October 2021. Conducting tests based on 100 shots, as set forth in the original published definition, the 40-qubit Aspen-11 system demonstrated a CLOPS of 844, while the 80-qubit Aspen-M system demonstrated a CLOPS of 892. These results suggest that our current systems perform as well or better on this CLOPS speed test as the number of qubits in the system increases. To reflect what users can potentially expect in typical use cases, we also evaluated CLOPS using 1000 shots. In this case, Aspen-11 performed at 7512 CLOPS and Aspen-M performed at 8333 CLOPS, demonstrating that comparable or better system speed persists at both higher shot counts and higher qubit counts. In July and August, 2022, we successfully achieved a CLOPS performance greater than 4,000 on both our 80-qubit Aspen-M-2 system and our 40-qubit Aspen-11 system, respectively, representing a 4.5 times acceleration since February. These speed tests were conducted using our production QCS environment. CLOPS, characterizes quantum processing speeds inclusive of gate speeds, reprogrammability, and co-processing capabilities, among other factors. CLOPS is calculated as M × K × S × D / time taken where: M = number of templates = 100; K = number of parameter updates = 10; S = number of shots = 100 or 1000; and D = number of QV layers = log2 QV.
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.
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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, 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 the date hereof, we have 165 issued and pending patents 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.
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Sales & Marketing
During this period of eQA, 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 Department of Defense, the DOE and the Intelligence Community have each been making significant investments in quantum computing, and we have technology development partnerships with leading 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 Nasdaq and Standard Chartered Bank. We also have distribution relationships with customers like Amazon Web Services, Microsoft, ORNL and Strangeworks.
As we work to develop new generations of our hardware with the goal of continuing to scale and achieve nQA and then BQA, we anticipate increasing our investment in both sales and marketing to expand the number of enterprise companies directly licensing our QCS platform. However, with the reorganization and reduction in force announced in February 2023, we anticipate that the costs will decrease in the near term until we achieve narrow quantum advantage, at which point we expect costs to increase.
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:
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enterprise-sized organizations working on quantum-assisted breakthroughs in applications areas like drug discovery, network optimization, financial modeling, weather forecasting and fusion energy with organizations like Astex Pharmaceuticals, Deloitte, NASA, Nasdaq, Standard Chartered Bank, the U.S. DOE and certain military branches within the U.S. Department of Defense;
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materials science researchers and quantum algorithm developers at renowned laboratories like Fermilab, Lawrence Livermore National Laboratory, MIT Lincoln Laboratory, NASA Quantum Artificial Intelligence Laboratory and ORNL;
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quantum-focused software and algorithm companies like 1Qbit, Phasecraft, Riverlane, Q-CTRL and Zapata;
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Cloud service providers like Amazon Web Services, Microsoft Azure, and Strangeworks; and
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We also enter into multi-year technology development partnerships with organizations that possess specialized technical expertise and strong interests in advancing the development of quantum computing (as referenced in Business - Key Technology Development Partnerships ). These organizations include DARPA, SQMS, and Innovate UK.
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.
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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, Quantinuum, PsiQuantum, Xanadu and ColdQuanta. 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 and Intel are 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 Rigetti’s Business and Industry-The quantum computing industry is competitive on a global scale and Rigetti 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 audits and to other government reviews covering issues such as cost, performance, internal controls and accounting practices.
Employees & Core Values
Our deep and talented workforce is the key to our success. As of March 1, 2023, we employ 144 people globally, the majority of whom are employed in 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.
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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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Text extracted from the filing as submitted to EDGAR. Formatting, tables and exhibits are simplified for reading; the original document is authoritative for anything you rely on.