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.
MIT researchers demonstrate the strongest nonlinear light-matter coupling in a quantum system that could help reach the fault-tolerant quantum computing stage with 10X faster operations and readout
MIT researchers have demonstrated what they believe is the strongest nonlinear light-matter coupling ever achieved in a quantum system. Their experiment is a step toward realizing quantum operations and readout that could be performed in a few nanoseconds. The researchers used a novel superconducting circuit architecture to show nonlinear light-matter coupling that is about an order of magnitude stronger than prior demonstrations, which could enable a quantum processor to run about 10 times faster. “This would really eliminate one of the bottlenecks in quantum computing. Usually, you have to measure the results of your computations in between rounds of error correction. This could accelerate how quickly we can reach the fault-tolerant quantum computing stage and be able to get real-world applications and value out of our quantum computers,” says Yufeng “Bright” Ye, lead author of a paper on this research. The new architecture, based on a superconducting “quarton” coupler, achieved coupling strengths roughly ten times higher than previous designs, potentially allowing quantum processors to run ten times faster. Faster readout and operations are critical to reducing errors in quantum computation, which depend on performing error correction within the limited lifespans of qubits. Researchers demonstrated extremely strong nonlinear light-matter coupling in a quantum circuit. Stronger coupling enables faster readout and operations using qubits, which are the fundamental units of information in quantum computing. (Christine Daniloff, MIT)
Origin Quantum launches Tianji 4.0 to support scalable quantum systems offering standardized workflows capable of being executed by non-specialist engineers
Origin Quantum Computing Technology has released its fourth-generation quantum control system, Tianji 4.0, which supports over 500 qubits and supports China’s continuing efforts toward building scalable, industrial-grade quantum computing infrastructure. Tianji 4.0 introduces improvements across scalability, integration, stability, and automation. It reflects a move from intense hardware tuning to standardized workflows capable of being executed by non-specialist engineers. Tianji 4.0 integrates with four core software systems developed by Origin Quantum. This full-stack integration streamlines the testing and tuning of superconducting qubit chips, which traditionally required input from PhD-level specialists. The result, according to the company, is a more repeatable and scalable approach to engineering, which prepares the system for use in future hundred-qubit quantum devices. Guo Guoping, director of the Anhui Quantum Computing Engineering Research Center and chief scientist at Origin Quantum, emphasized that the launch signifies a transition from prototype-level development to replicable engineering production. This could lay the foundation for mass production of quantum systems that are both higher in qubit count and more reliable in operation, which are essential requirements for practical use in computation-heavy sectors. The functionality offered by Tianji 4.0 suggests a continued focus on hardware-software co-design, system stability under increasing qubit counts, and preparation for industrial deployment, as well as prioritization of higher-throughput and modular quantum platforms within China’s domestic quantum ecosystem.
China’s Origin Quantum releases fourth-generation quantum control system, heads toward mass production, supports over 500 qubits and serves as the central control for superconducting quantum computers
China’s Origin Quantum has launched its fourth-generation quantum control system, a move signaling the country’s increasing push to industrialize and scale quantum computing capabilities. The new system, dubbed Origin Tianji 4.0, supports over 500 qubits and serves as the central control for superconducting quantum computers, according to The Global Times, a media outlet under the Chinese Communist Party (CCP). The system, unveiled this week in Hefei, is positioned as a critical enabler for mass-producing quantum computers with more than 100 qubits. The control system is considered the “neural center” of a quantum computer. It generates, acquires and controls the precise signals that manage quantum chips, which are the computational heart of a quantum system. With the Tianji 4.0 upgrade, Origin Quantum claims major improvements in integration, automation and scalability compared to its previous version, which powered the country’s third-generation superconducting quantum computer, Origin Wukong. The company said Tianji 4.0 is integrated with four of Origin Quantum’s proprietary software platforms, enabling faster testing and adjustment of superconducting chips. These improvements are expected to reduce both the cost and time required to bring quantum machines online.
World’s first silicon-based quantum computer can still integrate seamlessly with HPC computing in data center because of own self-contained, closed-cycle cryo cooling
Equal1 has unveiled the Bell-1, the first quantum device that combines the potential of quantum computing with the convenience of traditional high-performance computing (HPC). The six-qubit machine is rack-mountable and can fit into existing data centers. It doesn’t require specialized infrastructure or additional equipment to operate at a temperature of minus 459.13 degrees Fahrenheit. The Bell-1 uses the latest semiconductor fabrication techniques and purified silicon for high control and long coherence times. The chip, called the UnityQ 6-Qubit Quantum Processing System, uses spin qubits, allowing for higher qubit density and scalability. The Bell-1 also incorporates error correction, control, and readout, taking advantage of existing semiconductor infrastructure for reliability and scalability. The company plans to make more powerful versions with higher qubit counts and is future-proof, allowing early adopters to upgrade existing systems as new models are released.
Quantware and Q-CTRL accelerate deployment of on-premises quantum computers and scaling of QPUs with an autonomous calibration solution with an ability to unlock processors with over 1 million qubits
Quantware announced a collaboration with Q-CTRL to deliver an autonomous calibration solution for its customers. By integrating Q-CTRL’s autonomous calibration solution, Boulder Opal Scale Up, with its cutting-edge QPUs, QuantWare’s customers will be able to achieve push-button tuneup of their on-premises quantum computers – an critical solution for scaling QPUs, especially those powered by QuantWare’s VIO technology, designed to unlock processors with over 1 million qubits. This new partnership will provide QuantWare’s customers with: Accelerated System Development: QuantWare’s customers will be able to drastically accelerate the construction and deployment of their quantum systems towards error correction. Q-CTRL’s autonomous calibration solution streamlines the setup process, reducing test times from days to hours. Maximized QPU Performance: Leveraging Q-CTRL’s Boulder Opal Scale Up solution empowers any user to achieve optimal performance from QuantWare QPUs with minimal effort. This ensures that customers can unlock the full potential of QuantWare’s QPUs, including the new Contralto-A Quantum Error Correction QPU recently launched in early access. Q-CTRL’s Boulder Opal Scale Up solution combines PhD-level human intelligence with AI-driven automation to overcome the quantum industry bottleneck. Built on the company’s track record of delivering peak QPU performance through physics-informed AI, Boulder Opal Scale Up provides an expert-configured and fully autonomous software solution to deliver fast, repeatable, and robust QPU characterization and calibration.