A team of researchers from Penn State and Colorado State has demonstrated how a gold cluster can mimic gaseous, trapped atoms, allowing scientists to take advantage of these spin properties in a system that can be easily scaled up. The researchers show that gold nanoclusters have the same key spin properties as the current state-of-the-art methods for quantum information systems. They can also manipulate an important property called spin polarization in these clusters, which is usually fixed in a material. These clusters can be easily synthesized in relatively large quantities, making this work a promising proof-of-concept that gold clusters could be used to support a variety of quantum applications. An electron’s spin not only influences important chemical reactions but also quantum applications like computation and sensing. The direction an electron spins and its alignment with respect to other electrons in the system can directly impact the accuracy and longevity of quantum information systems. Gold clusters can mimic all the best properties of the trapped gaseous ions with the benefit of scalability. Scientists have heavily studied gold nanostructures for their potential use in optical technology, sensing, therapeutics, and to speed up chemical reactions, but less is known about their magnetic and spin-dependent properties. In the current studies, the researchers specifically explored monolayer-protected clusters, which have a core of gold and are surrounded by other molecules called ligands. The researchers determined the spin polarization of the gold clusters using a similar method used with traditional atoms. The research team plans to explore how different structures within the ligands impact spin polarization and how they could be manipulated to fine tune spin properties. This presents a new frontier in quantum information science, as chemists can use their synthesis skills to design materials with tunable results.
D-Wave’s study finds 27% of business leaders whose company has implemented quantum optimization or plans to do so within the next two years expect a return on investment of more than $5 million in the first 12 months
D-Wave Quantum study highlights the potential for quantum optimization to create value across industries. According to the study, 46% of surveyed business leaders whose company has implemented quantum optimization or plans to do so within the next two years expect a return on investment of between $1 and $5 million, while 27% predict a return of more than $5 million in the first 12 months. A majority of the business leaders surveyed (81%) believe that they have reached the limit of the benefits they can achieve through optimization solutions running on classical computers. Against that backdrop, many are starting to explore whether quantum technologies can help. 53% are planning to build quantum computing into their workflows and 27% are considering doing so, indicating a growing recognition of quantum computing’s real-world business value. 22% are seeing quantum make a significant impact for those who have adopted it, while another 50% anticipate it will be disruptive for their industry. The results of the study show that quantum computing is gaining recognition among business leaders for its ability to potentially deliver major efficiencies in addressing complex optimization problems and operational improvements. 60% respondents expect quantum computing-based optimization to be very or extremely helpful in solving the specific operational challenges that their companies face. In fact, among those respondents most familiar with quantum, this figure rises to 73%, including nearly a quarter who describe it as “a game changer.” The areas in which business leaders expect to benefit from an investment in quantum optimization include: supply chain and logistics (50%), manufacturing (38%), planning and inventory (36%), and research and development (36%). Most respondents (88%), especially those in the manufacturing industry, believe that their company would go “above and beyond” for even a 5% improvement in optimization.
D-Wave’s study finds 27% of business leaders whose company has implemented quantum optimization or plans to do so within the next two years expect a return on investment of more than $5 million in the first 12 months
D-Wave Quantum study highlights the potential for quantum optimization to create value across industries. According to the study, 46% of surveyed business leaders whose company has implemented quantum optimization or plans to do so within the next two years expect a return on investment of between $1 and $5 million, while 27% predict a return of more than $5 million in the first 12 months. A majority of the business leaders surveyed (81%) believe that they have reached the limit of the benefits they can achieve through optimization solutions running on classical computers. Against that backdrop, many are starting to explore whether quantum technologies can help. 53% are planning to build quantum computing into their workflows and 27% are considering doing so, indicating a growing recognition of quantum computing’s real-world business value. 22% are seeing quantum make a significant impact for those who have adopted it, while another 50% anticipate it will be disruptive for their industry. The results of the study show that quantum computing is gaining recognition among business leaders for its ability to potentially deliver major efficiencies in addressing complex optimization problems and operational improvements. 60% respondents expect quantum computing-based optimization to be very or extremely helpful in solving the specific operational challenges that their companies face. In fact, among those respondents most familiar with quantum, this figure rises to 73%, including nearly a quarter who describe it as “a game changer.” The areas in which business leaders expect to benefit from an investment in quantum optimization include: supply chain and logistics (50%), manufacturing (38%), planning and inventory (36%), and research and development (36%). Most respondents (88%), especially those in the manufacturing industry, believe that their company would go “above and beyond” for even a 5% improvement in optimization.
Cornell–IBM researchers demonstrate a new method of building fault-tolerant universal quantum computers through the ability to encode information by braiding Fibonacci string net condensate (Fib SNC) anyons in two-dimensional space
Researchers at IBM, Cornell, Harvard University, and the Weizman Institute of Science have made two major breakthroughs in the quantum computing revolution. They demonstrated an error-resistant implementation of universal quantum gates and demonstrated the power of a topological quantum computer in solving hard problems that conventional computers couldn’t manage. The researchers demonstrated the ability to encode information by braiding Fibonacci string net condensate (Fib SNC) anyons in two-dimensional space, which is crucial for being fault tolerant and resistant to error. The researchers demonstrated the power of their method on a known hard problem, chromatic polynomials, which originated from a counting problem of graphs with different colored nodes and a few simple rules. The protocol used, sampling the chromatic polynomials for a set of different graphs where the number of colors is the golden ratio, is scalable, so other researchers with quantum computers can duplicate it at a larger scale. Studying topologically ordered many-body quantum systems presents tremendous challenges for quantum researchers. The researchers at IBM were critical in understanding the theory of the topological state and designing a protocol to implement it on a quantum computer. Their other colleagues made essential contributions with the hardware simulations, connecting theory to experiment and determining their strategy. The research was supported by the National Science Foundation, the U.S. Department of Energy, and the Alfred P. Sloan Foundation.
BDx Data Centres unveils Southeast Asia’s first hybrid quantum AI testbed aligned with Singapore’s Green 2030 and Smart Nation strategies
BDx Data Centres has launched Southeast Asia’s first hybrid quantum AI testbed, aiming to integrate quantum computing capabilities into its flagship SIN1 data centre in Paya Lebar. Developed in collaboration with Singapore-based Anyon Technologies, the testbed is designed to catalyze breakthroughs in AI innovation. “A modern computer today is essentially a whole data centre. Deploying a state-of-the-art hybrid quantum computing system at BDx’s SIN1 facility marks a transformative step in modern computing infrastructure,” said Dr Jie (Roger) Luo, president and CEO of Anyon Technologies. “By integrating QPUs (Quantum Processing Units) with CPUs (Central Processing Units) and GPUs (Graphics Processing Units), we’re enabling breakthroughs in quantum algorithms and applications. This lowers adoption barriers for enterprise customers, like financial institutions.” The testbed serves as a gateway for startups, enterprises, and government agencies to explore the vast potential of quantum-enhanced AI applications, made possible through the integration of Anyon’s quantum systems with BDx’s AI-ready infrastructure. Aligned with Singapore’s Green 2030 and Smart Nation strategies, the initiative also sets a benchmark for sustainable, high-performance computing.
New algorithm enables simulating quantum computations using codes that distribute information across multiple subsystems allowing errors to be detected and corrected without destroying the quantum information
Researchers from Chalmers University of Technology in Sweden, along with teams from Milan, Granada, and Tokyo, have developed a groundbreaking method for simulating certain types of error-corrected quantum computations. This is a major step forward in the race to build powerful, dependable quantum technology. Quantum computers have the potential to transform fields like medicine, energy, encryption, artificial intelligence, and logistics. However, they still face a critical obstacle: errors. Quantum systems are far more prone to errors and much harder to fix than traditional computers. Researchers often turn to classical computers to simulate the process, but simulating advanced quantum behavior is incredibly complex. The limited ability of quantum computers to correct errors stems from their fundamental building blocks, qubits, which have the potential for immense computational power but are highly sensitive to disturbances. To address this issue, error correction codes are used to distribute information across multiple subsystems, allowing errors to be detected and corrected without destroying the quantum information. The researchers developed an algorithm capable of simulating quantum computations using the Gottesman-Kitaev-Preskill (GKP) code, which makes quantum computers less sensitive to noise and disturbances. This new mathematical tool allows researchers to more reliably test and validate a quantum computer’s calculations, opening up entirely new ways of simulating quantum computations that have previously been unable to test.
New system lets multiple users share a single quantum computer by dynamically allocating quantum resources and intelligently scheduling jobs
Columbia Engineering researchers have developed HyperQ, the first system to enable multiple users to run quantum programs simultaneously on a single machine using quantum virtual machines (qVMs). By dynamically allocating quantum resources and intelligently scheduling jobs, HyperQ analyzes each program’s needs and steers them to the best parts of the quantum chip, so multiple tasks can run at once without slowing each other down. HyperQ is a software layer, a hypervisor, inspired by the virtualization technology that powers modern cloud computing. It divides a physical quantum computer’s hardware into multiple, smaller, isolated quantum virtual machines. A scheduler then acts like a master Tetris player, packing multiple of these qVMs together to run simultaneously on different parts of the machine. The system reduced average user wait times by up to 40 times, transforming turnaround times from days to mere hours. It also enabled up to a tenfold increase in the number of quantum programs executed in the same time frame, ensuring much higher utilization of expensive quantum hardware. Remarkably, HyperQ’s intelligent scheduling could even enhance computational accuracy by steering sensitive workloads away from the noisiest regions of the quantum chip. For quantum cloud providers such as IBM, Google, and Amazon, the technology offers a powerful way to serve more users with existing hardware infrastructure, increasing both capacity and cost-effectiveness. For academic researchers and industry researchers, HyperQ means much faster access to quantum computing resources.
Startup Qedma’s software specializes in quantum error suppression and error mitigation by analyzing noise patterns to suppress some classes of errors while the algorithm is running and mitigate others in post-processing
Startup Qedma specializes in error-mitigation software. Its main piece of software, QESEM, or quantum error suppression and error mitigation, analyzes noise patterns to suppress some classes of errors while the algorithm is running and mitigate others in post-processing. IBM is both working on delivering its own “fault-tolerant” quantum computer by 2029 and collaborating with partners like Qedma. That’s because IBM thinks driving quantum further requires a community effort. “If we all work together, I do think it’s possible that we will get scientific accepted definitions of quantum advantage in the near future, and I hope that we can then turn them into more applied use cases that will grow the industry,” VP of Quantum, Jay Gambetta said. In all likelihood, it will first apply to an academic problem, not a practical one. In this context, it may take more than one attempt to build consensus that it’s not just another artificial or overly constrained scenario. Since last September, Qedma has been available through IBM’s Qiskit Functions Catalog, which makes quantum more accessible to end users. Qedma’s plans are hardware-agnostic. The startup has already conducted a demo on the Aria computer from IonQ, a publicly listed U.S. company focused on trapped ion quantum computing. In addition, Qedma has an evaluation agreement with an unnamed partner Sinay described as “the largest company in the market.” Recently, it also presented its collaboration with Japan’s RIKEN on how to combine quantum with supercomputers.
Penn State study shows diffusion-based approach to automatically generate valid quantum circuits achieves 100% output validity by learning the patterns of circuit structure directly from graph-structured data, offering a scalable alternative to LLM-based approaches
A recent study from Penn State researchers introduces a diffusion-based approach to automatically generate valid quantum circuits—offering a scalable alternative to today’s labor-intensive quantum programming methods. The proposed framework, dubbed Q-Fusion, achieved 100% output validity and demonstrates promise for accelerating progress in quantum machine learning and quantum software development. Unlike LLM-based approaches that treat circuit generation like language modeling, or reinforcement learning that requires trial-and-error with human-defined rules, Q-Fusion learns the patterns of circuit structure directly from data. This bypasses the need for hand-crafted heuristics and enables the model to discover novel circuit layouts. Q-Fusion points toward a more scalable future, where models can rapidly explore vast design spaces and generate circuits that are physically viable on actual quantum hardware. The authors note that diffusion models offer advantages over generative adversarial networks (GANs) and other common generative techniques due to their stability and flexibility with graph-structured data. Q-Fusion also incorporates hardware-specific constraints such as limited qubit connectivity and native gate sets, ensuring that generated circuits can potentially be deployed on real quantum devices without extensive post-processing. As quantum computing continues to mature, tools like Q-Fusion could play an essential role in making the technology more accessible and productive. Automating the generation of valid, deployable quantum circuits will reduce the workload on quantum software engineers and accelerate the pace of experimentation. The model’s diffusion-based approach is not only a strong alternative to other QAS methods but also opens new possibilities for combining machine learning with quantum program synthesis. It also aligns with trends in AI where graph-based diffusion models are showing strong performance across domains ranging from drug discovery to chip design.
Israeli quantum startup Qedma just raised $26 million, with IBM joining in
Startup Qedma specializes in error-mitigation software. Its main piece of software, QESEM, or quantum error suppression and error mitigation, analyzes noise patterns to suppress some classes of errors while the algorithm is running and mitigate others in post-processing. IBM is both working on delivering its own “fault-tolerant” quantum computer by 2029 and collaborating with partners like Qedma. That’s because IBM thinks driving quantum further requires a community effort. “If we all work together, I do think it’s possible that we will get scientific accepted definitions of quantum advantage in the near future, and I hope that we can then turn them into more applied use cases that will grow the industry,” VP of Quantum, Jay Gambetta said. In all likelihood, it will first apply to an academic problem, not a practical one. In this context, it may take more than one attempt to build consensus that it’s not just another artificial or overly constrained scenario. Since last September, Qedma has been available through IBM’s Qiskit Functions Catalog, which makes quantum more accessible to end users. Qedma’s plans are hardware-agnostic. The startup has already conducted a demo on the Aria computer from IonQ, a publicly listed U.S. company focused on trapped ion quantum computing. In addition, Qedma has an evaluation agreement with an unnamed partner Sinay described as “the largest company in the market.” Recently, it also presented its collaboration with Japan’s RIKEN on how to combine quantum with supercomputers.