Traditional computers use binary bits, but quantum computers leverage qubits, enabling faster processing. Conventional semiconductors operate on a binary system, like Nvidia H100s, where each transistor represents a 1 or 0.This will become a central part of this IONQ stock outlook later on.

Quantum computing, however, operates on a different principle, utilizing quantum bits (qubits) that can exist in multiple states simultaneously. This allows quantum systems to perform exponentially more calculations than classical systems.

IonQs (NYSE:IONQ) recently achieved a significant milestone in quantum technology, hinting at its potential to revolutionize computing. Its trapped ion tech minimizes QPU size, enhancing power and reducing costs and errors.

As IonQ emerges as a top contender in the quantum computing stocks arena, it has marked a remarkable 117% surge in share price over the past year. Despite a modest 10% gain year-to-date, it presents a suitable entry point for investors.

IonQ, a quantum computing leader, unveiled the USs inaugural quantum computing manufacturing hub in Bothell, WA. The expanded facility accommodates research, development, and production.

The company inaugurated the first US-based factory producing replicable quantum computers for client data centers, enhancing technology innovation and manufacturing in the Pacific Northwest. CEO Peter Chapman highlighted IonQs commitment to commercializing quantum computing.

Sen. Maria Cantwell was also there to show her unending support for IonQ during the ribbon-cutting ceremony. She noted the companys hard work and dedication in innovating quantum computing. She emphasized quantum computings transformative potential in various fields. This is central to this IONQ stock outlook.

IonQ continues its streak of success, achieving milestones like #AQ35 ahead of schedule and expanding partnerships with Amazon Braket and QuantumBasel. It collaborates with global giants and secures projects with the US Air Force Research Lab.

In other news, IonQ renews its partnership with SKKU in South Korea, offering continued access to IonQs quantum systems. This fosters innovation and strengthens South Koreas position in quantum computing.

SKKU Professor Yonuk Chong expressed satisfaction with IonQs research outcomes and commitment to future collaboration. South Korea aims to lead in quantum computing, supported by IonQs tangible contributions. This announcement aligns with IonQs broader efforts in South Korea, including partnerships with Hyundai Motors and the government.

In Q3 2023, IonQ achieved significant year-over-year revenue growth of 122%, signaling strong performance and technological advancements. It also showed several bookings totaling to $26.3 million. This surpassed expectations and demonstrated how in demand IonQ is.

When 2023 closed, IONQ achieved its target to $100 million of cumulative bookings since 2021. The company also altered its revenue forecast to $22 million, showing its confidence in achieving such targets through contract milestones.

Moreover, it raised its 2023 booking guidance to $60-63 million, reflecting sustained demand. IONQ introduced Forte Enterprise and Tempo systems to target diverse market needs, emphasizing compactness and compatibility with existing infrastructure.

Milestones achieved, such as reaching AQ 29, underscore IONQs forefront position in trapped-ion quantum computing.

IonQs major clients comprise research labs and government bodies like the U.S. Air Force Research Lab and QuantumBasel in Switzerland. Collaborations with Seoul National University in South Korea indicate expansion into academia, and these tie ups suggest theres some powerful money and minds behind IonQ and its innovative goals.

While the roadmap to profitability may take years to play out, I do think IonQ remains intriguing as a speculative buy. I wouldnt recommend any investor put all their chips behind this stock. Its far too risky a bet, and should be lumped in with other high-potential growth stocks in the riskier end of a portfolios barbell strategy.

But for those seeking the next big thing beyond AI, quantum computing is a space to consider for long-term growth. Right now, IonQ looks like a company that could be a winner in this space, though time will tell. This concludes my IONQ stock outlook.

On the date of publication, Chris MacDonald did not have (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Chris MacDonalds love for investing led him to pursue an MBA in Finance and take on a number of management roles in corporate finance and venture capital over the past 15 years. His experience as a financial analyst in the past, coupled with his fervor for finding undervalued growth opportunities, contribute to his conservative, long-term investing perspective.

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IONQ Stock Outlook: Why This Quantum Computing Play Could Be a Long-Term Winner - InvestorPlace

In an unprecedented advancement in drug discovery, Zapata Computing, Inc., alongside Insilico Medicine, the University of Toronto, and St. Jude Children's Research Hospital, has showcased the remarkable potential of quantum-enhanced generative AI. This collaboration has led to the first-ever instance where a generative model operating on quantum hardware surpasses traditional classical models in generating viable cancer drug candidates.

This landmark study focused on developing novel KRAS inhibitors, a notoriously difficult target in cancer therapy. Utilizing advanced generative AI models on both classical and quantum hardware, including a 16-qubit IBM device, the team successfully generated one million drug candidates. Following a meticulous process of algorithmic and human filtering, the quantum-enhanced generative model yielded two distinct molecules with superior binding affinity over those produced by classical models. This breakthrough not only underlines the efficacy of quantum computing in drug discovery but also illustrates the transformative role of Industrial Generative AI in addressing complex, domain-specific challenges in various industries.

Industrial Generative AI, a specialized subcategory of generative AI, is particularly adept at tackling such intricate problems. Unlike general-purpose AI tools like ChatGPT and DALL-E from OpenAI, Industrial Generative AI is customized to address specific issues within enterprises or industries. It navigates through challenges such as data disarray, large solution spaces, unpredictability, time sensitivity, compute constraints, and demands for accuracy, reliability, and security. At its core are generative models, like Large Language Models (LLMs), which learn from training data to generate new, realistic outputs. This approach is what enabled the Zapata AI team to pioneer in the field of drug discovery, leveraging AI to create groundbreaking solutions.

Yudong Cao, CTO and co-founder of Zapata AI, highlighted the synergy of quantum and classical computing in providing comprehensive solutions in this groundbreaking project. The research, currently awaiting peer review and available on ArXiv, builds on earlier studies demonstrating the potential of quantum generative AI in drug discovery.

Alex Zhavoronkov, PhD, founder and co-CEO of Insilico Medicine, acknowledged the integration of Insilico's generative AI engine, Chemistry42, with quantum-augmented models, heralding new therapeutic avenues for challenging cancer targets. This step is critical in advancing the future of drug discovery.

With a recent strategic partnership with D-Wave Quantum Inc., Zapata AI is set to further expand the horizons of quantum generative AI models in discovering new molecules for a range of commercial applications. Christopher Savoie, CEO and co-founder of Zapata AI, expressed excitement about this development and the potential for broader application in various industries.

Aln Aspuru-Guzik, a professor at the University of Toronto and a co-founder and Scientific Advisor of Zapata AI, shared his optimism about integrating quantum computing into the drug discovery pipeline. This research is pioneering, setting a precedent for future quantum computers to showcase their unique capabilities.

The research employed Zapata AI's QML Suite Python Package, available on its Orquestra platform, emphasizing the practical application of quantum computing in solving real-world scientific challenges. This integration of Industrial Generative AI into the drug discovery process marks a significant stride in leveraging AI for innovative, industry-specific solutions, driving growth and efficiency in the ever-evolving technological landscape.

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Quantum-Enhanced AI Revolutionizes Cancer Drug Discovery: A Leap Forward with Industrial Generative AI - Unite.AI

Understanding Locally Encoded Defects in Quantum Circuits

The realm of quantum computing is continually evolving, with researchers and scientists making strides in understanding the intricacies of quantum systems. One such concept that has gained significant attention in recent years is the concept of Locally Encoded Defects (LED) in quantum circuits. LED is a method used to approximate a fixed-point state with zero correlation length. This approach is based on the measurement of qubits in a quantum system and the calculation of stabilizer and Wilson loop values, which help identify local fluctuations and anyonic excitations.

LED involves a local decoder that works to remove these fluctuations. Additionally, the coarse-graining of the lattice is performed to reduce uncorrected errors. This strategy has proven to be beneficial in detecting topological order and distinguishing between topological and trivial states. It provides a powerful tool for characterizing topologically ordered states in experimental quantum systems and offering new insights into the nature of different regimes in these systems.

As detailed in a research article, the use of LED in quantum circuits can greatly improve their performance and reliability. Quantum computers, being inherently different from classical computers, are susceptible to errors due to their quantum nature. However, the application of LED can help in identifying and rectifying these errors, thus improving the overall performance of these systems.

Moreover, LED is not just limited to error correction. It could also be utilized for enhancing the computational capabilities of quantum systems. The current research in this area is focused on exploring the potential applications of LED and how it can be leveraged to make quantum computing more practical and efficient.

Despite the promising advancements, understanding atomic-like quantum systems, especially solid-state atom-like systems, impurity-based qubits in semiconductors, and defects in 2D and 3D materials, remains a challenge. The Quantum Staging Group (QSG) has been working to fill this gap by promoting materials science for the development of quantum information sciences and quantum sensing.

In a recent workshop held at the 2022 MRS Spring Meeting, QSG brought together scientists with experimental and theoretical expertise in materials for quantum technologies. The goal was to discuss key near-term challenges to further promote and accelerate the development of solid-state atom-like systems with applications in quantum technologies.

The workshop addressed four main themes, including unifying perspectives on relevant length scales for quantum systems, addressing materials challenges in quantum information technologies, the role of electrical noise in atomic-like systems, and predictive challenges for atomic-like quantum systems. It is clear that a multi-disciplinary approach, involving both theoretical and experimental expertise, is essential for advancing our understanding of atomic-like quantum systems.

In conclusion, the concept of Locally Encoded Defects in quantum circuits holds immense potential for the future of quantum computing. From improving the performance and reliability of quantum computers to helping characterize topologically ordered states in experimental quantum systems, LED is paving the way for a better understanding of the quantum world. However, it is crucial to continue addressing the challenges and to foster collaboration and knowledge sharing in this field to unlock the full potential of quantum technologies.

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Understanding Locally Encoded Defects in Quantum Circuits: A Promising Tool for Quantum Computing - Medriva

Breakthrough technology accelerates development of algorithms for practical use in quantum computers Fujitsu Limited

Tokyo, February 19, 2024

Fujitsu today announced the development of a novel technique on a quantum simulator that speeds up quantum-classical hybrid algorithms, which have been proposed as a method for the early use of quantum computers, achieving 200 times the computational speed of previous simulations. For quantum circuit computations using conventional quantum and classical hybrid algorithms, the number of times of quantum circuit computation increases depending on the scale of the problem to be solved. Larger-scale problems that require many qubits, including simulations in the materials and drug discovery fields, may even require several hundred days.

The newly developed technology enables simultaneous processing of a large number of repetitively executed quantum circuit computations distributed among multiple groups. Fujitsu has also devised a way to simplify problems on a large scale with less loss of accuracy by using one of the world's largest-scale quantum simulators (1) it has developed. Fujitsu has made it possible to perform computations on a quantum simulator in just one day, which would take an estimated 200 days to complete with conventional methods. As a result, it is now possible to complete simulations of large-scale quantum computation within a realistic timeframe and to simulate the behavior of larger molecules computed by a hybrid quantum-classical algorithm, leading to algorithm development.

Fujitsu plans to incorporate this technology into its hybrid quantum computing platform to accelerate research into the practical application of quantum computers in various fields, including finance and drug discovery. Additionally, Fujitsu will not only apply this technology to quantum simulators, but also to accelerate quantum circuit computations on actual quantum computers.

Although the development of fault-tolerant quantum computers (FTQC (2) ) is currently progressing worldwide, current quantum computers face many problems, such as the inability to eliminate the effects of noise. At the same time, in order to demonstrate the usefulness of quantum computers ahead of FTQC, practical applications for small and medium-sized quantum computers (Noisy Intermediate-Scale Quantum Computer, NISQ) with noise tolerance of 100 to 1,000 qubits are being studied.

By applying VQE (3), a typical NISQ algorithm, Fujitsu, for example, has developed a quantum simulator for quantum application development (4) and has been working to speed up quantum circuit computation itself. However, in VQE, the number of iterations of quantum circuit computation increases as the size of the problem increases, so it takes a very long time to perform computation, especially for large problems requiring many qubits, and it is estimated that it takes several 100 days for a quantum simulator. Therefore, it was difficult to develop quantum algorithms for practical use.

In response to this problem, Fujitsu has developed a technology that achieves 200 times higher the performance speed of conventional technologies by simultaneously distributing multiple repetitively executed quantum circuit computations and reducing the amount of quantum circuit computations by reducing accuracy degradation.

Quantum-classical hybrid algorithms seek a quantum circuit that provides the lowest energy state, for example, the ground state of a molecule, by alternating between the process of performing quantum circuit computation and the process of optimizing quantum circuit parameters (5) using a classical computer. However, for parameter optimization of quantum circuits by classical computers, it is necessary to prepare a large number of quantum circuits with small changes in parameters, perform quantum circuit computation for all of them sequentially, and derive the optimal parameters from the results. This requires considerable time for computation, especially for larger-scale problems. Increasing the number of nodes simply to speed up circuit computation has conventionally been limited by communication overhead, and new technologies were required.

Focusing on the fact that quantum circuits with small parameter changes can be executed without affecting each other, Fujitsu has developed a distributed processing technology that enables each group to execute different quantum circuits by dividing the computation nodes of the quantum simulator into multiple groups and using RPC (6) technology to submit quantum circuit computation jobs through the network. Using this technology, multiple quantum circuits with different parameters can be simultaneously distributed and calculated, and the computation time can be reduced to 1/70th of the conventional technology.

In addition, since the computation quantity in the quantum-classical hybrid algorithm is proportional to the number of terms in the equation of the problem to be solved, and the number of terms is the fourth power of the number of qubits in the general VQE, the computation quantity increases as the problem scale increases, and the result cannot be obtained in a realistic time. Through simulations of large molecules using 32 qubits of one of the world's largest 40 qubit quantum simulators, Fujitsu has found that the ratio of terms with small coefficients to the total number of terms increases as the scale increases, and that the effect of terms with small coefficients on the final results of calculations is minimal. By taking advantage of this characteristic, Fujitsu was able to achieve both a reduction in the number of terms in the equation and prevention of deterioration in computation accuracy, thereby reducing the quantum circuit computation time by approximately 80%.

By combining these two technologies, Fujitsu was able to demonstrate for the first time in the world that when distributed processing of 1024 compute nodes into 8 groups for a 32 qubit problem, it was possible to achieve a quantum simulation run time of 32 qubits in one day, compared to the previous estimate of 200 days. This is expected to advance the development of quantum algorithms for problems with a large number of qubits and the application of quantum computers to the fields of materials and finance.

We are investigating the application of quantum computers to materials development. Among them, the use of VQE in NISQ devices is an essential consideration. We expect that this acceleration technology will greatly speed up the principle verification of the VQE algorithm.

We are studying the use of VQE to calculate the energy of molecules related to semiconductor materials, to predict the electronic structure and physical properties of specific materials, and to optimize chemical reactions in semiconductor manufacturing processes. We hope that accelerating this process will enable us to quickly verify the principle and effectiveness of the VQE algorithm and discover its usefulness. NISQ devices whose use is limited by noise and errors will be considered with an eye toward these limitations.

The Sustainable Development Goals (SDGs) adopted by the United Nations in 2015 represent a set of common goals to be achieved worldwide by 2030. Fujitsus purpose to make the world more sustainable by building trust in society through innovation is a promise to contribute to the vision of a better future empowered by the SDGs.

Fujitsus purpose is to make the world more sustainable by building trust in society through innovation. As the digital transformation partner of choice for customers in over 100 countries, our 124,000 employees work to resolve some of the greatest challenges facing humanity. Our range of services and solutions draw on five key technologies: Computing, Networks, AI, Data & Security, and Converging Technologies, which we bring together to deliver sustainability transformation. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.7 trillion yen (US$28 billion) for the fiscal year ended March 31, 2023 and remains the top digital services company in Japan by market share. Find out more: http://www.fujitsu.com.

Fujitsu Limited Public and Investor Relations Division Inquiries

All company or product names mentioned herein are trademarks or registered trademarks of their respective owners. Information provided in this press release is accurate at time of publication and is subject to change without advance notice.

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Fujitsu develops technology to speed up quantum circuit computation in quantum simulator by 200 times - Fujitsu