NIST and the University of Colorado Boulder have launched CURBy, a publicly available random number generator based on quantum nonlocality, offering verifiable, truly random numbers. At the heart of this service is the NIST-run Bell test, which provides truly random results. This randomness acts as a kind of raw material that the rest of the researchers’ setup “refines” into random numbers published by the beacon. The Bell test measures pairs of “entangled” photons whose properties are correlated even when separated by vast distances. When researchers measure an individual particle, the outcome is random, but the properties of the pair are more correlated than classical physics allows, enabling researchers to verify the randomness. Einstein called this quantum nonlocality “spooky action at a distance.” This is the first random number generator service to use quantum nonlocality as a source of its numbers, and the most transparent source of random numbers to date. That’s because the results are certifiable and traceable to a greater extent than ever before. CURBy uses entangled photons in a Bell test to generate certifiable randomness, achieving a 99.7% success rate in its first 40 days and producing 512-bit outputs per run. A novel blockchain-based system called the Twine protocol ensures transparency and security by allowing users to trace and verify each step of the randomness generation process. CURBy can be used anywhere an independent, public source of random numbers would be useful, such as selecting jury candidates, making[A1] [A2] a random selection for an audit, or assigning resources through a public lottery.
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.
JPMorgan Chase and Infleqtion reduce the hardware overhead in QC by 100X to as few as 20 physical qubits through use of error-correction techniques
JPMorgan Chase and quantum technology company Infleqtion have released an open-source software library to reduce the hardware requirements for practical quantum computing applications. The new qLDPC library introduces error-correction techniques that reduce the number of physical qubits needed to create reliable logical qubits by a factor of 10 to 100x. This development addresses one of quantum computing’s key challenges, the substantial hardware overhead in qubit numbers typically required for fault tolerance. “This library makes it possible to bring that number down by 100x – down to as few as 20 physical qubits per logical qubit,” Pranav Gokhale, general manager of computing at Infleqtion, told. Depending on the implementation, the new library reduces the requirement to between 15 and 150 qubits. The tools are specifically designed for Infleqtion’s neutral atom-based quantum computing hardware, which offers customizable qubit layouts, enabling more efficient error-correcting codes. The library has been released as open-source software, an uncommon approach for a financial institution partnership. For JPMorgan Chase, the development could enable new applications in financial optimization, risk analysis and fraud detection by making quantum computing more practical. The reduction in required physical qubits makes quantum approaches to complex financial problems more viable. The qLDPC library is now available for developers, researchers and hardware partners to explore methods for improving error correction and optimizing quantum workloads across various platforms. According to Gokhale, the open-source software approach, combined with finding talent in unexpected places, is helping bridge the workforce gap by making quantum computing more accessible.
New quantum algorithm solves hard optimization problems up to 80 times faster than classical solvers like CPLEX and simulated annealing by doing away with error correction and using low-energy states that suppress unwanted transitions
A new study by Kipu Quantum and IBM demonstrates that a tailored quantum algorithm running on IBM’s 156-qubit processors can solve certain hard optimization problems faster than classical solvers like CPLEX and simulated annealing. The quantum system used a technique called bias-field digitized counterdiabatic quantum optimization (BF-DCQO). The method builds on known quantum strategies by evolving a quantum system under special guiding fields that help it stay on track toward low-energy (i.e., optimal) states. It achieved comparable or better solutions in seconds, while classical methods required tens of seconds or more. CPLEX took 30 to about 50 seconds to match that same solution quality, even with 10 CPU threads running in parallel, according to the study. The researchers further confirmed this advantage across a suite of 250 randomly generated hard instances, using distributions specifically selected to challenge classical algorithms. BF-DCQO delivered results up to 80 times faster than CPLEX in some tests and over three times faster than simulated annealing in others. At the heart of the BF-DCQO algorithm is an adaptation of counterdiabatic driving, a physics-inspired strategy where an extra term is added to the Hamiltonian — the system’s energy function — to suppress unwanted transitions. This helps the quantum system evolve faster and more accurately toward its lowest energy configuration. Because this process doesn’t rely on error correction, it is well suited to today’s NISQ devices. And because the algorithm uses only shallow circuits with mostly native operations like single-qubit rotations and two- or three-body interactions, it can fit within the short coherence windows of real hardware.
First successful demonstration of quantum error correction of qudits for quantum computers used a reinforcement learning algorithm to optimize
A Yale University study published in Nature has demonstrated the first-ever experimental quantum error correction for higher-dimensional quantum units using qudits, a quantum system that holds quantum information and can exist in more than two states. The researchers used a reinforcement learning algorithm to optimize the systems as ternary and quaternary quantum memories. The experiment pushed past the break-even point for error correction, showcasing a more practical and hardware-efficient method for quantum error correction by harnessing the power of a larger Hilbert space. The increased photon loss and dephasing rates of GKP qudit states can lead to a modest reduction in the lifetime of the quantum information encoded in logical qudits, but in return, it provides access to more logical quantum states in a single physical system. The findings demonstrate the promise of realizing robust and scalable quantum computers and could lead to breakthroughs in cryptography, materials science, and drug discovery.
Quantum Machines cuts calibration time from hours to minutes by combining open-source framework, modular architecture, reusable components, combining them into complex workflows and instantly sharing protocols with ecosystem
Quantum Machines announced the release of Qualibrate (which the company spells QUAlibrate), an open-source framework for calibrating quantum computers. It cuts quantum computer calibration time from hours to minutes. By addressing one of quantum computing’s most critical scaling bottlenecks, Quantum Machines‘ new framework enables fast, modular calibration and fosters a global ecosystem for sharing and advancing calibration protocols. By creating an open ecosystem, Qualibrate enables researchers and companies worldwide to build upon each other’s advances, accelerating the path to practical quantum computers. To properly initialize and maintain a quantum computer’s performance, calibration must be performed not just once, but frequently during operation to compensate for system drift. Qualibrate enables researchers and quantum engineers to create reusable calibration components, combine them into complex workflows, and execute calibrations through an intuitive interface. The platform abstracts away hardware complexities, allowing teams to focus on quantum system logic rather than low-level details. Qualibrate’s open-source nature and modular architecture mean that when researchers develop new calibration protocols, these innovations can be immediately shared, validated, and built upon by the broader quantum computing community. Companies can also develop proprietary solutions on top of Qualibrate that leverage advanced approaches like quantum system simulation and deep learning algorithms. This creates an ecosystem where fundamental calibration advances can be shared openly and enables specialized tools that push the boundaries of performance. Along with the framework, Quantum Machines is releasing its first calibration graph for superconducting quantum computers, providing a complete calibration solution that can be immediately deployed and customized.