Quantum computing is gaining significant business and commercial potential, according to a new report by researchers at the MIT Initiative on the Digital Economy. The “Quantum Index Report 2025” provides a comprehensive assessment of the state of quantum technologies, aiming to make them more accessible to entrepreneurs, investors, teachers, and business decision-makers. The report highlights the increasing interest in quantum computing, with the US leading the field with over 40 quantum processing units (QPUs). The report also notes that quantum technology patents have soared, with corporations and universities leading innovation efforts. Venture capital funding for quantum technology reached a new high point in 2024, with quantum computing firms receiving the most funding ($1.6 billion) followed by quantum software companies at $621 million. Businesses are also buzzing about quantum computing, with the frequency of mentions each quarter increasing from 2022 to 2024. The demand for quantum skills has nearly tripled since 2018, prompting universities to establish quantum hubs and programs connecting business leaders with researchers. The report highlights the rapid progress and developments across various areas, indicating a broad and deep development in the field.
MIT says quantum computing is surging in USA with over 40 quantum processing units offered, a 5x increase in patents, and $2.2 billion in venture investment in 2024
Quantum computing is gaining significant business and commercial potential, according to a new report by researchers at the MIT Initiative on the Digital Economy. The “Quantum Index Report 2025” provides a comprehensive assessment of the state of quantum technologies, aiming to make them more accessible to entrepreneurs, investors, teachers, and business decision-makers. The report highlights the increasing interest in quantum computing, with the US leading the field with over 40 quantum processing units (QPUs). The report also notes that quantum technology patents have soared, with corporations and universities leading innovation efforts. Venture capital funding for quantum technology reached a new high point in 2024, with quantum computing firms receiving the most funding ($1.6 billion) followed by quantum software companies at $621 million. Businesses are also buzzing about quantum computing, with the frequency of mentions each quarter increasing from 2022 to 2024. The demand for quantum skills has nearly tripled since 2018, prompting universities to establish quantum hubs and programs connecting business leaders with researchers. The report highlights the rapid progress and developments across various areas, indicating a broad and deep development in the field.
Quantum-as-a-Service provides businesses cloud-based, pay-as-you-go access to quantum computing; eliminating multimillion-dollar infrastructure costs and lowering expertise barriers for modeling and optimization
Current quantum computers are hugely expensive to own and difficult to maintain. This is where quantum-as-a-service comes in. Thanks to QaaS, businesses wanting to experiment with it or even start putting it to operational use don’t need to spend millions of dollars on hardware and a dedicated facility to operate it from. Instead, QaaS providers let businesses or research organizations access their quantum computers through the cloud, using a pay-as-you-go model to minimize initial overheads. QaaS is an increasingly attractive option for many businesses and organizations that want to experiment with quantum computing without incurring huge initial outlays. For instance, financial services companies are using it to model risks and understand the seemingly chaotic behavior of markets, to help them make better investment decisions. While multinational banks like JP Morgan and HSBC have money to invest in IT infrastructure, the quantum skills shortage still makes it difficult to find workers with the technical knowledge to maintain it. In short, QaaS hugely reduces (in principle) the financial cost, the logistical requirements, and the skills overhead of running quantum computing projects, potentially making it available to a much wider user base. If your business model involves modelling of complex systems, real-world environments or optimization challenges (such as finding the most efficient route between a large number of destinations), then quantum is certainly worth exploring.
Quantinuum launches a Python-based quantum programming language, an emulation platform for Helios hardware with measurement-dependent control flow and a CUDA‑QX error correction platform
Integrated quantum computing company Quantinuum Ltd. unveiled new open-source software tools designed to accelerate software development for quantum computing with a more intuitive programming language. The company also announced a new open-source emulator for its Helios quantum computing hardware, Selene, built to model entangled quantum behavior realistically. The new language is called Guppy. The company based the programming language on Python, a widely recognized syntax by developers, which provides an accessible entry point for programming quantum computers. Guppy enables programmers to use code logic, allowing them to create dynamic software with “if/then” statements and “for” loop structures. This higher-level language permits the development of applications that can adjust the path when states change and the code itself is more readable. The new language gives developers access to advanced quantum protocols, including magic state distillation and injection, quantum teleportation and other measurement-based routines. All of them can be executed through a real-time control system. Additionally, users can use Nvidia Corp.’s new CUDA-QX capabilities for error correction, all without needing to write extra code. Quantinuum refers to Selene as a “digital sister” for its Helios quantum computer hardware, providing developers access to an open-source emulator. Selene provides advanced runtime behavior unique to Helios, including measurement-dependent control flow and hybrid quantum-classical logic capabilities. It can also run Guppy programs out of the box, allowing developers to get going immediately. All of this is brought together with the company’s all-in-one quantum computing platform, Nexus, which serves as the middle layer that connects all parts of the stack.
Modular quantum chips enable fault‑tolerant computing with surface code as inter‑chip links run 10 times noisier yet still correct errors accelerating secure finance workloads
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.
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 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.
Quantum Motion delivers the industry’s first full-stack silicon CMOS quantum computer; using 300 mm wafers and three 19‑inch racks, can scale to millions of qubits
Quantum Motion has delivered the industry’s first full-stack quantum computer to be built using a standard silicon CMOS chip fabrication process – the same transistor technology used in conventional computers. Deployed at the UK National Quantum Computing Centre (NQCC), this is the first full-stack quantum computer to use mass manufacturable 300mm silicon CMOS wafer technology and the first silicon spin‑qubit computer installed under the NQCC’s Quantum Computing Testbed Programme. The system integrates the company’s Quantum Processing Unit (QPU) with a user interface and control stack compatible with industry standard software frameworks, such as Qiskit and Cirq, making it a full-stack solution. The system has a data-centre-friendly footprint of just three 19” server racks, housing the dilution refrigerator and integrated control electronics. Auxiliary equipment is designed to sit separately, enabling it to fit in standard data-centre environments and supporting upgrades to much larger QPUs without any change to the system footprint. Unlike other quantum computing approaches, Quantum Motion’s architecture leverages high-volume industrial chipmaking to produce qubits, using industry standard 300 mm processes from commercial chip foundries. Quantum Motion’s architecture, control stack and manufacturing approach is designed to scale to host millions of qubits, enabling fault tolerant, utility-scale and commercially viable quantum computing. Quantum Motion’s QPU is based on a scalable tile architecture which integrates all the needed compute, readout, and control elements into a dense array that can be repeatedly printed onto a chip, enabling future expansion to millions of qubits per QPU. This design enables systems to be easily upgraded by installing future generation QPUs. The system also represents a breakthrough in AI machine‑learning tuning, enabling more efficient operation and automated algorithms for control and calibration.
As the quantum industry moves from the lab to manufacturing, quantum error correction is the key to building robust and scalable fault-tolerant machines
As the quantum industry moves from the lab to the fab, five major trends are driving the next wave of technology and business models: Quantum error correction: The industry’s focus has shifted to quantum error correction as the key to building robust and scalable fault-tolerant machines. With this shift, we’re seeing increased interest in companies focused on error correction capabilities, including Riverlane Ltd., Q-CTRL Pty. Ltd. and Qedma Ltd. There is also significant innovation being applied to encoding physical qubits into logical qubits using not just the classic surface code, but also novel alternatives such as quantum low-density parity check codes, which protect quantum information against noise and decoherence. The middle of the stack: This evolution allows companies to focus on what they do best and buy components and capabilities as needed, such as control systems from Quantum Machines (Q.M Technologies Ltd.) and quantum software development from firms such as Classiq Technologies Ltd. and Algorithmiq Oy. Scale-out architectures: This strategy involves linking multiple QPUs to work together as one distributed machine, which could even enable different types of qubits to collaborate on a single problem. Startups such as Alice & Bob SAS and Qolab Inc. are driving advances in qubit and architecture design and fabrication. Input-output and cryogenics: Alternatives that reduce both the number of cables and their thermal load are emerging, such as improved density (Delft Circuits B.V.), cryogenic qubit control capabilities (Diraq Pty Ltd.’s cryo-CMOS) and alternative approaches such as Qphox B.V.’s optical fiber. Mergers and acquisitions: This trend is playing out at a scale that few investors, including us, anticipated. IonQ Inc. has made several bold acquisitions in 2025, including computing technology with Oxford Ionics Ltd., interconnects and memories with Lightsynq Technologies Inc., communications with ID Quantique SA and Qubitekk Inc., and even space with Capella Space Corp.