A study led by researchers at the University of California, Riverside has shown that scalable quantum architectures can be created, consisting of many small chips working together as one powerful unit. The researchers simulated realistic architectures and found that even imperfect links between quantum chips can still produce a functioning, fault-tolerant quantum system. This is a leap forward in scaling quantum hardware, as it allows for the detection and correction of errors automatically, giving reliable outputs even with imperfect hardware. The team found that even when the links between chips were up to 10 times noisier than the chips themselves, the system still managed to detect and correct errors. This means that quantum computers can be built without waiting for perfect hardware, as long as each chip is operating with high fidelity. The research was motivated by published work at the Massachusetts Institute of Technology and supported by the National Science Foundation.
Phasecraft creates hardware‑agnostic, hybrid quantum‑classical algorithms that make today’s NISQ devices useful for materials, energy and logistics, not just future fault‑tolerant machines
Quantum algorithm company Phasecraft Ltd. has raised $34 million in new funding to accelerate its work to transform quantum computing’s theoretical promise into practical applications. Phasecraft focuses on making quantum computing useful sooner by bridging the gap between today’s noisy, intermediate-scale quantum devices and future large-scale systems. Differing from other firms that rely on the eventual arrival of fault-tolerant quantum computers, Phasecraft says, it’s developing ultra-efficient algorithms that allow current imperfect machines to deliver meaningful results in real-world settings. The company doesn’t focus on one platform or possible solution but instead takes a hardware-agnostic approach. Doing so, it says, delivers compatibility with multiple platforms and maximizes the chances of early commercial adoption. It works across different industries where quantum computing has the potential to deliver a measurable impact, such as materials discovery, chemistry, energy systems and logistics optimization. Phasecraft methodology — pairing quantum devices with classical computing — delivers hybrid solutions that can simulate complex materials, optimize energy grids and even tackle problems in biological research. The algorithms are already showing promise in making material simulations millions of times more efficient, which could accelerate the design of new solar cells, catalysts or medicines. Areas of exploration include drug discovery, where quantum simulations could shorten the process of understanding molecular interactions and energy resilience, where optimized networks could improve efficiency and sustainability.
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.
Chinese researchers add hybrid parallelism to Q2Chemistry simulations — batch‑buffered overlap and dependency‑aware gate contraction — delivering speedups and HPC scalability on CPU and GPU simulators
Quantum simulation is crucial for developing practical quantum algorithms, as limitations in current hardware necessitate robust classical methods for testing and refinement. Researchers from the University of Science and Technology of China have developed a scalable approach to simulating quantum circuits within the Q Chemistry software package, delivering substantial performance gains on both conventional CPUs and powerful GPUs. This research demonstrates a significant leap forward in simulation speed and portability, consistently outperforming existing open-source simulators across a range of quantum circuit designs and paving the way for more complex algorithm development. Key technologies underpinning these advancements include multi-core CPU parallelization, distributed computing, and the use of tensor network methods to efficiently represent quantum states. State vector simulation alongside techniques like matrix product states are employed to balance accuracy and computational cost, enabling researchers to tackle increasingly complex quantum systems. The Q2Chemistry software package has significantly enhanced the performance of full-amplitude quantum circuit simulation within the software package, enabling accurate and efficient simulations of complex quantum circuits. The team implemented Batch-Buffered Overlap Processing, a multi-buffering strategy that partitions quantum state amplitudes into smaller batches, and Staggered Multi-Gate Parallelism, a two-dimensional thread block strategy for GPU execution. These optimizations enable researchers to tackle increasingly complex quantum circuits and explore the potential of quantum chemistry with greater efficiency and accuracy.
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 releases developer toolkit and demo to accelerate quantum AI exploration, enabling seamless ML integration and practical RBM (GenAI-like) training
D-Wave Quantum recently released a collection of offerings to help developers explore and advance quantum artificial intelligence (“AI”) and machine learning (“ML”) innovation, including an open-source quantum AI toolkit and a demo. Available now for download, the quantum AI toolkit enables developers to seamlessly integrate quantum computers into modern ML architectures. The demo shows how developers can use the toolkit to experiment with using D-Wave(TM) quantum processors to generate simple images, reflecting what D-Wave believes is a pivotal step in the development of quantum AI capabilities. By releasing this new set of tools, D-Wave aims to help organizations accelerate the use of annealing quantum computers in a growing set of AI applications. The quantum AI toolkit, part of D-Wave’s Ocean(TM) software suite, provides direct integration between D-Wave’s quantum computers and PyTorch, an ML framework widely used to train and create deep learning models. The toolkit includes a PyTorch neural network module for using a quantum computer to build and train ML models known as a restricted Boltzmann machine (“RBM”). Used to learn patterns and connections from complex data sets, RBMs are employed for generative AI tasks such as image recognition and drug discovery. Training RBMs with large datasets can be a computationally complex and time-consuming task that could be well-suited for a quantum computer. By integrating with PyTorch, D-Wave’s new toolkit aims to make it easy for developers to experiment with quantum computing to address computational challenges in training AI models. “With this new toolkit and demo, D-Wave is enabling developers to build architectures that integrate our annealing quantum processors into a growing set of ML models,” said Dr. Trevor Lanting, chief development officer at D-Wave.
Research shows spin polarization property in gold nanoclusters can be easily synthesized in relatively large quantities to support and scale a variety of quantum applications
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.
Fujitsu developing a superconducting quantum computer with a capacity exceeding 10,000 qubits by utilizing an early-stage fault-tolerant quantum computing and using 250 logical qubits
Fujitsu is developing a superconducting quantum computer with a capacity exceeding 10,000 qubits, with construction set to finish in fiscal 2030. The computer will use 250 logical qubits and utilize Fujitsu’s “STAR architecture,” an early-stage fault-tolerant quantum computing (early-FTQC) architecture. The project, backed by the NEDO, aims to make practical quantum computing possible, particularly in materials science. Fujitsu will contribute to the development of quantum computers towards industrialization through joint research with Japan’s National Institute of Advanced Industrial Science and Technology and RIKEN. The company plans to achieve a 1,000 logical qubit machine by fiscal 2035, considering the possibility of multiple interconnected quantum bit-chips. Fujitsu’s research efforts will focus on developing the following scaling technologies: High-throughput, high-precision qubit manufacturing technology: Improvement of the manufacturing precision of Josephson Junctions, critical components of superconducting qubits which minimize frequency variations. Chip-to-chip interconnect technology: Development of wiring and packaging technologies to enable the interconnection of multiple qubit chips, facilitating the creation of larger quantum processors. High-density packaging and low-cost qubit control: Addressing the challenges associated with cryogenic cooling and control systems, including the development of techniques to reduce component count and heat dissipation. Decoding technology for quantum error correction: Development of algorithms and system designs for decoding measurement data and correcting errors in quantum computations.
IBM’s new decoder algorithm offers a 10X increase in accuracy in the detection and correction of errors in quantum memory using memory tuning to analyze indirect measurements of quantum states
IBM researchers have developed a new decoder algorithm called Relay-BP, which significantly improves the detection and correction of errors in quantum memory. The algorithm, known as Relay-BP, shows a tenfold increase in accuracy over previous leading methods and reduces the computing resources required to implement it. Relay-BP addresses a persistent bottleneck in the quest to build reliable quantum computers and could lead to experimental deployments within the next few years. Quantum computers are sensitive to errors due to their fragile qubits, which can be disturbed by environmental noise or imperfections in control. The decoder works by analyzing syndromes, indirect measurements of quantum states, that provide clues about where something has gone wrong. Relay-BP, built on an improved version of a classical technique called belief propagation (BP), is the most compact, fast, and accurate implementation yet for decoding quantum low-density parity-check (qLDPC) codes. It is designed to overcome trade-offs, being fast enough to keep up with quantum error rates, compact enough to run on field-programmable gate arrays (FPGAs), and flexible enough to adapt to a wide range of qLDPC codes. IBM’s Relay-BP is a quantum error correction algorithm that uses memory tuning, a tool in physics, to improve performance. The algorithm’s success is attributed to the interdisciplinary approach of the team, which combined expertise from firmware engineering, condensed matter physics, software development, and mathematics. IBM credits this cross-functional approach as a cultural strength of its quantum program. Relay-BP currently focuses on decoding for quantum memory, but is still short of full quantum processing. To achieve real-time quantum computation, the decoding must become faster and smaller. IBM plans to begin experimental testing of the decoder in 2026 on Kookaburra, an upcoming system designed to explore fault-tolerant quantum memory. Relay-BP is considered a vital piece of the puzzle, pushing the limits of classical resources to stabilize quantum systems and offering a new tool for researchers looking to bridge the gap between experimental qubits and reliable quantum logic.
D-Wave’s new quantum AI toolkit enables developers to seamlessly integrate quantum computers into modern ML architectures
D-Wave has released a collection of offerings to help developers explore and advance quantum artificial intelligence (AI) and machine learning (ML) innovation, including an open-source quantum AI toolkit and a demo. Available now for download, the quantum AI toolkit enables developers to seamlessly integrate quantum computers into modern ML architectures. Developers can leverage this toolkit to experiment with using D-Wave™ quantum processors to generate simple images. By releasing this new set of tools, D-Wave aims to help organizations accelerate the use of annealing quantum computers in a growing set of AI applications. The quantum AI toolkit, part of D-Wave’s Ocean™ software suite, provides direct integration between D-Wave’s quantum computers and PyTorch, a production-grade ML framework widely used to build and train deep learning models. The toolkit includes a PyTorch neural network module for using a quantum computer to build and train ML models known as a restricted Boltzmann machine (RBM). By integrating with PyTorch, D-Wave’s new toolkit aims to make it easy for developers to experiment with quantum computing to address computational challenges in training AI models. “With this new toolkit and demo, D-Wave is enabling developers to build architectures that integrate our annealing quantum processors into a growing set of ML models,” said Dr. Trevor Lanting, chief development officer at D-Wave.