Embracing Edge AI can be a game-changer for businesses, propelling them toward a future where real-time data processing and decision-making are at the forefront of innovation. Edge AI is a term that refers to the deployment of artificial intelligence (AI) models closer to users and devices, either on-premises — such as in a retail store or bank branch — or on edge computing platforms. With this approach, AI processing occurs near where data is created, on the “edge” of the network, in a decentralized manner, instead of using cloud-based solutions or centralized computing. This results in reduced latency, improved performance, and enhanced privacy and security.

Many industries, including retail, finance, manufacturing, healthcare, automotive, and telecommunications, are investing in Edge AI to increase efficiency in their operations — essentially by providing a large set of automation possibilities — and to improve their customer experiences. Demand for this technology is growing, and the overall state of Edge AI adoption is gaining traction.

According to Gartner, “More than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021.” And according to IDC, by “2023 more than 70% of organizations will run varying levels of data processing at the IoT edge.”

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The Benefits of an Edge AI Approach

One of the main benefits of Edge AI over running AI in centralized computing is improved efficiency. By processing data locally rather than sending it to a central server, Edge AI can drastically reduce latency and remove bandwidth constraints, leading to faster decision-making and improved operational efficiency.

This allows for real-time or near-real-time processing of data, a critical feature in many applications, such as autonomous vehicles, manufacturing processes, and healthcare monitoring. This type of processing is also a key advantage when the goal is to improve the customer experience.

Businesses also save on bandwidth and data storage costs by minimizing the need to transmit vast amounts of raw data back to the cloud or a data center; instead, only the relevant preprocessed information is sent.

This is much more scalable and sometimes can enable solutions that otherwise wouldn’t be technically or economically feasible, and it also brings increased resiliency: Edge AI systems can continue to process data even when they lose connectivity, enhancing the reliability of weak communication links and optimizing costs.

Last, but not least, the Edge AI approach can offer enhanced privacy and security: since data is processed locally, Edge AI reduces the risk of data breaches or loss during transmission. This makes it suitable for industries with stringent privacy regulations because sensitive data does not need to leave the premises.

Edge AI in Action

It has been said that “data is the new oil,” which means that any connected source of information is also a juicy target for criminals. AI is increasingly important in these scenarios as the attacks become more advanced.

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Edge AI security measures, like access control, can be delegated to edge nodes, which can run sophisticated AI-based algorithms for inference and behavioral analysis that can detect and stop suspicious activity from cyberattacks — including the feared “zero-day” attacks — before they even enter business networks and cause any harm.

In retail, Edge AI can enable smarter customer experiences, such as personalized recommendations based on real-time in-store behavior, including monitoring self-checkouts to reduce losses or running real-time inventory management. In manufacturing, it can assist in predictive maintenance by processing data from numerous sensors to predict equipment failures and suggest timely maintenance, minimizing downtime.

In healthcare, patient monitoring devices can use Edge AI to process health data in real-time, alert healthcare providers of any immediate risks, and ensure patient privacy by keeping sensitive health data localized. Security systems equipped with Edge AI can process video footage in real-time for facial recognition, anomaly detection, or immediate threat analysis and alerting.

Telecom operators can use Edge AI to optimize network operations by analyzing network traffic in real-time and dynamically managing network resources. Similarly, Edge AI can be used for managing smart power grids, detecting anomalies, and predicting energy demand to optimize power generation and distribution.

Steps for Preparing to Adopt Edge AI

Organizations looking to adopt Edge AI will first need to identify the specific use cases or areas in their business where Edge AI could provide a considerable benefit. This should be followed by an evaluation of the technical requirements since Edge AI involves different technology than traditional cloud-based AI.

The next step is to ensure your team has the necessary skill set or that your organization has a mechanism in place for training. Teams will need to be educated on edge computing, AI model development, and edge node orchestration.

At this stage, a partnership with edge computing providers and companies that provide ready-to-use AI models is all it takes to start collecting results, as your company will be able to leverage an integrated solution and vendor expertise.

Edge AI aligns with strategic growth initiatives, seamlessly integrates with change management practices, and may even catalyze the emergence of groundbreaking business models. The adoption of Edge AI signifies a commitment to staying ahead of the curve, ensuring that your enterprise remains competitive in a rapidly evolving digital landscape.

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RWS  announces the beta launch of Evolve, a pioneering linguistic AI innovation by Language Weaver that harnesses the power of secure neural machine translation, linguist-verified quality estimation and large language models. This unique combination of human and artificial intelligence has the potential to revolutionize translation processes by significantly reducing the time it takes to achieve near human-like quality output.

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The patent-pending capability of Evolve first analyzes Language Weaver’s initial translation of the content, checks the quality and suggests areas where the translation can be further improved. It then uses a large language model, fine-tuned by Language Weaver’s linguistic experts, to target those parts of the content that require further edits and automatically delivers a better translation of that content – in near real-time. Designed with security in mind, Language Weaver ensures that client content and information remain confidential throughout the process.

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RWS’s in-house language specialists and data scientists have been involved in every step of Evolve’s development. Language Weaver’s pioneering innovation will enable companies to benefit from a platform that achieves human-like translation quality, almost instantly, significantly reducing the time required for a human-in-the-loop during the translation process. This allows language specialists to focus their skills and cultural expertise on content that requires their attention.

As a result of RWS and Language Weaver enabling greater translation volumes and faster turnaround times, organizations will be able to benefit from these efficiencies in several ways. High tech companies can improve the global product experience across all touchpoints; retailers can improve their customer experience to increase global revenue; and highly regulated industries can gain greater confidence through increased translation accuracy.

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Thomas Labarthe, President of RWS Language and Content Technology, explains the significance: “The AI market is booming and, while novel use cases based on generative AI spring up every day, few have Evolve’s potential to meaningfully transform an organization’s ability to engage with international audiences at scale. This is the natural next step in the evolution of Language Weaver and is a true example of the powerful benefits of combining the best of artificial and human intelligence.”

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The company’s technology, which removes the engineering complexity around AI systems for image analysis, will initially assist medical professionals

L’école AI, creator of ÉO, a “machine teaching” technology that removes the engineering complexity around deep-learning systems for computer vision, announced that it has raised 3 million USD in Seed funding from Sofinnova Partners. The funds will be used to develop the company’s proprietary technology, which will be rolled out to medical professionals and researchers, enabling them to create bespoke AI systems to assist in their area of expertise. In addition, the financing will fuel the expansion of the team, accelerate product development and structure business efforts. Also participating in the round are notable business angels including Preston-Werner Ventures, the fund started by co-founder and former CEO of GitHub, Tom Preston-Werner.

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“We’re building a user interface for AI so anyone can create and benefit from their own personalized AI assistant,” said Louis-Alexandre Etezad-Heydari, Co-Founder and President of L’école AI, which means “AI school” in French. “We also are creating a system that will open possibilities for secure collaboration between organizations.”

Etezad-Heydari co-founded Madbits, a deep-learning image-analysis start-up, with Clément Farabet in 2013. A year after its founding, Madbits was acquired by Twitter, where the two entrepreneurs ran Twitter Cortex, an internal team that built a Deep Learning platform to power recommendation systems, search, ranking and filtering at Twitter.

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Kim Nilsson and Jonathan Alexander Brown teamed up with Etezad-Heydari to perfect the AI development framework for computer vision that initially inspired Madbits, and the three founders set off with the goal to make creating computer vision models simple enough for non-engineers.

“L’école’s technology is designed to democratize machine learning by enabling life science researchers and other non-machine learning experts to utilize tailored computer vision systems, thus accelerating life sciences research,” said Edward Kliphuis, Partner at Sofinnova Partners. “The focus on digital medicine is a logical entry point,” he noted.

“We’re starting with a focus on health care and life sciences because we want to make a positive impact right away,” said Jonathan Alexander Brown, Co-Founder and Chief Executive Officer. “With Sofinnova’s support, we are confident we have the right skills to partner with researchers and clinicians in these tightly regulated markets.”

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Farabet, an investor in L’école, is an AI pioneer. Currently VP of Research at Google DeepMind, he spent six years as a senior executive at NVIDIA, working on its autonomous vehicles and the company’s data science platform. Farabet is also famous in the AI world as one of the creators of Torch, a machine learning framework that provides a simple and flexible interface for building and training deep neural networks.

L’école AI counts a number of other AI pioneers among its investors, including Nicolas Pinto, head of Deep Learning at Apple, Clément Delangue, Co-Founder and CEO at Hugging Face.

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Generative AI creates a new wave of innovation and possibility but is not without risk. Executives feel driven to take advantage of AI but are also jump-out-of-their-skin scared of the risks — and rightfully so.

AI has been around for decades, but beginning in the late ‘90s, there was a sea change. With the increased application of machine learning, and particularly the use of neural networks, a new path opened and cracked long-existing challenges. Instead of applying logic or rules-based algorithmic approaches to problem-solving, data scientists turned neural net and reinforcement machine learning loose on vast amounts of data with a goal-oriented approach in which the system trained itself by trying all possible paths to a goal and scoring those most likely to yield the desired result.

Examples might be found in figuring out which moves led to a win in chess at various stages, or in machine translation of text (where the model searches for similarities in semantics rather than following the path of more literal translations, in which individual words are substituted).

The model also receives ongoing feedback, which refines the knowledge of the semantics. One of the characteristics of this “generative” approach, and perhaps the scariest, is that although the solutions it produces can be shown to work, the way in which the model arrived at them is not always clear.

Any shiny, buzzy concept usually has highs and lows, and the world of generative AI is no exception.

Teams applying it must temper high excitement with concern to ensure responsible use. Here are a few considerations enterprises can employ to use the technology meaningfully without getting carried away.

Recognize the Present and Future of AI

Companies must think ahead to where they believe generative AI will have the most business impact but also realize and accept that we are in a period of discovery. While focusing on near-term needs, enterprises need to simultaneously develop a plan for applying these technologies at scale and understand how they can ensure the accuracy or truth of responses from AI-based systems. We know that the data volumes generative AI applications require will increase dramatically. Planning now for this increase in data, both from within the enterprise and from external sources, is vital. Enterprises should start to catalog the data they’ll need to provide to the large language models (LLM) but also ground the LLM responses and condition the questions in facts and truths.

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Our first impulse may be to apply generative AI broadly. There’s near-term value to be harvested in doing so. However, those who thoughtfully apply contextual data specific to their enterprise will gain the strongest advantage.

Don’t Let Hallucinations Go Unchecked

Generative AI is at the heart of LLMs.

The term “generative” implies that an LLM produces output that appears to result from intelligent thought. An LLM strings together text, video frames, or other assets to generate responses that are not original. However, it may miss logical, biological, ethical, social, and cultural factors. Without contextual data to inform LLMs, inaccurate answers or hallucinations may be unavoidable. Think of the LLM as a well-read individual willing to provide an answer to any question, but one who has no feel for the potential impact of a wrong answer. Providing LLMs with data specific to certain circumstances minimizes the probability of hallucinations. Users must ground outcomes in “truths.” Accuracy is context-sensitive. Generalized LLMs need to be amended with information specific to the question and, more to the point, with information that’s up to date.

Ensuring that decisions are based on the most current information makes a difference in a world where increasing rates of change are becoming the norm.

Cache Validated Results

One of the most adjacent applications of generative AI is answering questions for customers and employees about products, processes, and policies. By caching validated results and searching there first, enterprises can not only return answers faster but also optimize resource use. A side benefit is knowing the answers are correct.

Overall, organizations that are both excited and concerned about AI are approaching it responsibly. We are all in new territory here. AI can provide immense cost savings and open routes of innovation in science, product design, and decision-making within business. But we must be intentional and aware as we apply this technology to avoid mistakes that we won’t be able to perceive until possibly too late. By applying context and using data to augment and guide LLMs, we can take advantage of it safely.

And, by caching results from LLMs and searching through these, we can be more cost-efficient.

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Study Reveals Potential for New AI-Based Tools to Transform Enzyme Design, Catalyzing Cell-Free Bioproduction Across Multiple Industries

Enzymit, a specialty biochemical company developing cell-free enzymatic manufacturing technology, announced the publication of a new study published on bioRxiv* demonstrating the efficacy of its deep learning-based technology for novel enzyme design.

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“The inherent instability of artificial enzymes, combined with production challenges and the limited range of reactions they can facilitate, has restricted their use in real-world applications, while the creation of novel enzymes has proved challenging due to the complex nature of such proteins,” said Gideon Lapidoth, PhD, CEO of Enzymit. “This research highlights the role of AI in overcoming the fundamental challenges developing commercially viable enzymes.”

The study, Context-Dependent Design of Induced-fit Enzymes using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes, proposes an alternative approach to novel enzyme design, by modifying existing enzymes to work with new molecules under a variety of different conditions. This was achieved through the development of two new proprietary AI-based tools, CoSaNN (Conformation Sampling using Neural Network) and SolvIT.

CoSaNN leverages advanced deep learning capabilities to generate new enzyme conformations based on the relationships between an enzyme’s genetic sequence and its three-dimensional structure. This ability to redesign the shape of enzymes enabled new chemical reactions unattainable with current design tools. SolvIT, a graph neural network tool, served as a predictive model for protein solubility in the bacterium E. coli and provided an additional layer of optimization for producing highly expressed enzymes.

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Combined, the tools enabled the creation of new enzymes with significantly higher thermal stability and exhibiting superior expression levels compared to alternative methods. 54% of the enzyme designs were successfully expressed in E. coli, of which 30% exhibited higher thermal stability than the template enzyme.

“This research demonstrates the transformative potential of artificial intelligence in novel enzyme sequence design, through its ability to capture complex interactions within a protein and to maintain its inherent mechanisms, even when heavily modified,” said Prof. Joseph Jacobson, board member and scientific advisor for Enzymit. “Harnessing the power of AI enables us to create vastly more stable enzymes in significantly greater volumes, and at significantly reduced costs for use in a wide range of commercial applications.”

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International Data Corporation (IDC) published its second forecast for the worldwide quantum computing market, projecting customer spend for quantum computing to grow from $1.1 billion in 2022 to $7.6 billion in 2027. This represents a five-year compound annual growth rate (CAGR) of 48.1%. The forecast includes base quantum computing as a service as well as enabling and adjacent quantum computing as a service.

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A new IDC forecast for the worldwide quantum computing market projects customer spend for quantum computing to grow from $1.1 billion in 2022 to $7.6 billion in 2027.

The new forecast is considerably lower than IDC’s previous quantum computing forecast, which was published in 2021. In the interim, customer spend for quantum computing has been negatively impacted by several factors, including: slower than expected advances in quantum hardware development, which have delayed potential return on investment; the emergence of other technologies such as generative AI, which are expected to offer greater near-term value for end users; and an array of macroeconomic factors, such as higher interest and inflation rates and the prospect of an economic recession.

IDC expects the quantum computing market will continue to experience slower growth until a major quantum hardware development that leads to a quantum advantage is announced. Until then, most of the growth will be driven by maturation in quantum computing as a service infrastructure and platforms and the growth of performance intensive computing workloads suitable for quantum technology.

IDC also expects investments in the quantum computing market will grow at a CAGR of 11.5% over the 2023-2027 forecast period, reaching nearly $16.4 billion by the end of 2027. This includes investments made by public and privately funded institutions, internal allocation (R&D spend) from technology and services vendors, and external funding from venture capitalists and private equity firms. Of particular note is the growing interest in quantum computing by global government agencies of which 14 (13 countries plus the European Union) have announced quantum initiatives that span multiple years and will generate billions of dollars for quantum computing research.

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The billions of dollars being allocated to the research and development of quantum computing have led to recent advancements in quantum computing hardware and software, as well as new error mitigation and suppression techniques. These advancements fuel speculation that achieving a near-term quantum advantage may be possible using today’s noisy intermediate-scale quantum (NISQ) systems. Over the long term, these investments are expected to result in the delivery of large-scale quantum systems capable of solving some of the most complex problems that challenge today’s scientists and engineers, causing a surge in customer spend towards the end of the forecast period.

IDC sees 2022 as an eventful year in the quantum computing industry. Strategic approaches implemented to reach a near-term quantum advantage using NISQ systems became more defined as vendors published quantum computing road maps emphasizing methods for improving qubit scaling, as well as new techniques for error mitigation and suppression. To improve the accessibility and usability of quantum systems, previously inaccessible quantum modalities became accessible for end-user experimentation, while other quantum hardware vendors announced partnerships for on-premises quantum deployments and quantum software vendors provided frictionless software offerings for nonquantum specialists. Finally, quantum hardware and software vendors announced the anticipated launch of new scientific accelerator platforms that will help with the integration of quantum, AI, and HPC.

“There has been much hype around quantum computing and when quantum computing will be able to deliver a quantum advantage, for which use cases, and when,” said Heather West, Ph.D., research manager, research manager within IDC’s Enterprise Infrastructure Practice. “Today’s quantum computing systems may only be suitable for small-scale experimentation, but advances continue to be made like a drumbeat over time. Organizations should not be deterred from investing in quantum initiatives now to be quantum ready in the future.”

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WiMi Hologram Cloud, a leading global Hologram Augmented Reality (“AR”) Technology provider, announced that edge detection algorithm based on deep learning and image fusion is being researched to improve the accuracy and efficiency of edge detection through multi-scale analysis and feature extraction of images, and to improve the edge detection and its accuracy.

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This is an algorithm utilizing deep learning techniques and image fusion methods for edge detection. Specifically, the algorithm uses a convolutional neural network to perform feature extraction on the original image and abstracts the image information into higher-level semantic features through multi-layer convolution and pooling operations. These features are then utilized for edge detection to improve the accuracy of edge detection. After completing the initial edge detection, the algorithm will also use image fusion methods to further optimize the edge detection results. Multiple edge detection results are synthesized to obtain more accurate edge information. Each pixel is labeled according to the different edge detection results, and the final edge location is determined based on the labeling of the pixel.

The process of the edge detection algorithm mainly includes the following steps: firstly, the image needs to be analyzed at multiple dimensions, and divided into multiple dimensions, each of which contains edge information of different sizes and shapes. This can help the algorithm to better capture the edge information in the image and improve the detection accuracy. For each dimension, features need to be extracted from the image. WiMi uses a deep convolutional neural network (CNN) as a feature extractor, which inputs the image into the network and extracts the image features through multiple convolutional and pooling layers, which can help the algorithm to better identify the edge information in the image and filter out some irrelevant information. By fusing image features of different dimensions, more comprehensive and accurate edge information can be obtained. Image fusion technique is used to fuse feature images of different scales by some weighting coefficients and use convolution operation for edge detection, which can better capture the edge information and improve the detection accuracy and efficiency.

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WiMi’s edge detection algorithm based on deep learning and image fusion has various technical features such as a deep learning model, image fusion technology, adaptive learning, high efficiency and parallel computing, which make the algorithm of high research value and practical significance in the field of edge detection. It utilizes a deep learning model for feature extraction, and abstracts the information in the original image into higher-level semantic features through multi-layer CNN, making edge detection more accurate. At the same time, it improves the accuracy of edge detection by combining the results of multiple edge detection results, and optimizes the results using image fusion technology to improve the robustness of edge detection. In addition, it adopts an adaptive learning method, which can adjust the parameters according to different scenes and data sets to further improve the effect of the algorithm. And it can effectively deal with large-scale image data, and at the same time, it has a faster speed to meet real-time requirements, and it adopts parallel computing methods to make full use of computer hardware resources to improve the efficiency and performance of the algorithm.

The algorithm is widely used in the field of computer vision due to its high accuracy and robustness, for example, for object recognition, video analysis, image segmentation, automatic driving, medical image processing and so on. In the future, WiMi will continue to explore innovative applications based on deep learning and image processing technologies to further improve the accuracy, efficiency and applicability of edge detection algorithm, and to promote the change of image processing technologies.

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• Phasecraft designs quantum algorithms for the imperfect quantum computers of today, accelerating practical quantum advantage from decades to years away
• Early focus is on applying algorithmic advances to discover the novel materials that will power the clean energy transition
• Founding team of world-leading quantum scientists brings decades of experience leading top research teams at UCL and the University of Bristol

Phasecraft – a startup led by top academics developing world-leading quantum algorithms – has raised a £13 million Series A funding round led by Silicon Valley deeptech VC, Playground Global. AlbionVC also joined the round along with participation from existing investors Episode1, Parkwalk Advisors, LCIF, and UCL Technology Fund.

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Founded in 2019 as a spinout from UCL and the University of Bristol by Professors Ashley Montanaro (CEO), Toby Cubitt (CTO and Chief Science Officer) and John Morton, each of whom has been at the cutting edge of quantum computing research for 20 years, Phasecraft will use this funding to further develop its quantum algorithms to the point of practical quantum advantage – when quantum computers outperform classical computers for useful real-world applications like developing new materials.

Bridging the gap between theory and reality 

Quantum computing promises to revolutionise the way humanity tackles its most complex problems. However, the noisy and unstable quantum computers of (known as Noisy Intermediate Scale Quantum, or NISQ, devices) aren’t capable of running the algorithms that currently exist to solve them. Based on the best quantum algorithms known prior to Phasecraft’s founding, a useful calculation like simulating and discovering a new battery material would require billions of operations on a quantum computer – today’s best-performing hardware can perform at most thousands. Significant recent investment in such hardware has seen it soar in capacity, but the algorithms needed to harness these advances have remained largely theoretical – to date, no algorithms have been run on a quantum computer to solve a problem of genuine practical interest.

Phasecraft is bridging this gap by radically reimagining how such quantum algorithms are designed. Its algorithms are based on novel insights from theoretical physics and computer science, coupled with knowledge gained from extensive numerical simulations and a deep understanding of quantum hardware. This helps them develop record-breaking algorithms with significantly superior computational efficiency compared to others in existence, whilst their partnerships with the three most advanced superconducting quantum hardware providers in the world, Google, IBM and Rigetti – the only quantum algorithms company to work with all three – help put these algorithms to work in the real world.

The company to date has published 17 scientific papers, with results including reducing the complexity of simulating the time-evolution of a quantum materials system by 400,000x, running the largest-ever simulation of a materials system on actual hardware by 10x, and proving for the first time ever that quantum optimisation algorithms can outperform classical ones.

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Practical applications

Phasecraft’s early focus is on applying these algorithmic improvements to the discovery of new materials important for the clean energy transition. Classical computing fails to capture many of these materials’ fundamental features, meaning we rely on experimental discovery which can take decades. Quantum computing promises to accelerate the entire process by capturing these features computationally, thus reducing the number of experiments required and drastically increasing the variety of material combinations which can be tested for any given use case.

Phasecraft has already developed a software pipeline which delivers an improvement of 1,000,000x or more in modelling real materials compared with the best previous quantum algorithms, bringing the number of operations required to model a material down to around 80,000 and within touching distance of existing hardware capability. Its work is also informed by industry partnerships including speciality materials developer Johnson Matthey and solar cell developer Oxford PV.

Unrivalled expertise

Phasecraft has hired some of the world’s leading quantum scientists, and recently hired computational chemistry and materials science expert Glenn Jones, former head of computational modelling at Johnson Matthey, to lead its materials work. Entrepreneur and investor Ian Hogarth, recently appointed chair of the UK’s AI Foundational Model Taskforce, is Chairman of the Board.

The new funding brings the total raised by Phasecraft to £17.25M in venture funding, as well as a further £3.75M in grant funding from the likes of Innovate UK and the European Research Council, which will be used to continue building the team of world-leading quantum scientists, researchers and engineers and to further establish Phasecraft as the world leader in quantum algorithms.

Ashley Montanaro, co-founder and CEO of Phasecraft, said: “For all the advances that have been made in quantum hardware, and for all quantum computing’s promise, such progress could end up being for nothing if we can’t build the applications needed to make the technology truly useful. With our record-breaking algorithms and groundbreaking techniques, we are pushing the boundaries of what is possible in this space. With support from such a renowned deep-tech visionary as Playground, we think practical quantum advantage is achievable in years, not decades.”

Peter Barrett, general partner, Playground Global: “Phasecraft’s team of world-class quantum scientists and engineers bring an unmatched expertise and a fresh perspective to one of the biggest challenges facing our quantum future – bridging the gap between quantum hardware capacity and real-world applications. At Playground, we’ve always believed that unlocking quantum’s potential requires a very special, dedicated and experienced team and we believe Phasecraft is uniquely positioned to help deliver our quantum future.”

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