Alice & Bob to Integrate Cat Qubits in Datacenters of the Future, Accelerated by NVIDIA Technology

Alice & Bob, a quantum hardware manufacturer and leading QPU designer, announced it is working to accelerate the integration of quantum technology into industry by introducing cat qubits into the datacenters of the future.

Quantum-Enhanced Generative AI Generates Viable Cancer Drug Candidates

Zapata Computing, Inc., the Industrial Generative AI company, announced that its scientists, in collaboration with Insilico Medicine, the University of Toronto, and St. Jude Children’s Research Hospital have demonstrated the first instance of a generative model running on quantum hardware outperforming state-of-the-art classical models in generating viable cancer drug candidates. The research points to a promising future of hybrid quantum generative AI for drug discovery using today’s quantum devices.

Algorithmiq Demonstrates Path to Quantum Utility with IBM

Algorithmiq, a scaleup developing quantum algorithms to solve the most complex problems in life sciences, has successfully run one of the largest scale error mitigation experiments to date on IBM’s hardware. This achievement positions them, with IBM, as front runners to reach quantum utility for real world use cases. The experiment was run with Algorithmiq’s proprietary error mitigation algorithms on the IBM Nazca, the 127 qubit Eagle processor, using 50 active qubits x 98 layers of CNOTS and thus a total of 2402 CNOTS gates. This significant milestone for the field is the result of a collaboration between the two teams, who joined forces back in 2022 to pave the way towards achieving first useful quantum advantage for chemistry.

Research Highlights: Unveiling the First Fully Integrated and Complete Quantum Monte Carlo Integration Engine

Quantinuum, a leading integrated quantum computing company has published full details of their complete Quantum Monte Carlo Integration (QMCI) engine. QMCI applies to problems that have no analytic solution, such as pricing financial derivatives or simulating the results of high-energy particle physics experiments and promises computational advances across business, energy, supply chain logistics and other sectors.

The Countless Worth of Fine-Tuning Foundation Models

In this contributed article, editorial consultant Jelani Harper points out that whether it’s some iteration of GPT, LLaMA, or any other foundation model, the enterprise merit obtained from successfully employing these constructs revolves around the ability to fine-tune them. LoRA and other Parameter-Efficient Fine-Tuning approaches produce this result in a manner that’s cost-effective and efficient—particularly when accessed through no-code cloud frameworks supplying GPUs and access to the models.

Classiq Research Shows Huge Quantum Computing Momentum, Market Potential

New research from Classiq, a leader in quantum software, indicates that companies understand the tremendous economic opportunities that quantum computing represents and across sectors are hiring or planning to hire quantum talent. The research also reveals that organizations are investigating quantum applications in their various sectors – and patenting their own quantum software intellectual property – in a big way, with businesses in the financial sector reporting the highest engagement and investment.

New Quantum Computing Research Shows Promising Path to Commercialization

Agnostiq, Inc., the quantum computing SaaS startup, announced its latest benchmark research which analyzed the state of quantum computing hardware to determine its current and future practicality as a mainstream solution. The findings show that quantum computing hardware has improved over time and that application-specific benchmarks can serve as a more practical yardstick for comparing the capabilities of alternative types of quantum hardware.

Cambridge Quantum Algorithm Solves Optimization Problems Significantly Faster, Outperforming Existing Quantum Methods

In a development that is likely to set a new industry standard, scientists at Cambridge Quantum (CQ) have developed a new algorithm for solving combinatorial optimization problems that are widespread in business and industry, such as traveling salesman, vehicle routing or job shop scheduling, using near-term quantum computers.

Quantum Machine Learning – An Introduction to QGANs

In this contributed article, data scientists from Sigmoid discuss quantum machine learning and provide an introduction to QGANs. Quantum GANs which use a quantum generator or discriminator or both is an algorithm of similar architecture developed to run on Quantum systems. The quantum advantage of various algorithms is impeded by the assumption that data can be loaded to quantum states. However this can be achieved for specific but not generic data.

Cambridge Quantum Computing Pioneers Quantum Machine Learning Methods for Reasoning

Scientists at Cambridge Quantum Computing (CQC) have developed methods and demonstrated that quantum machines can learn to infer hidden information from very general probabilistic reasoning models. These methods could improve a broad range of applications, where reasoning in complex systems and quantifying uncertainty are crucial. Examples include medical diagnosis, fault-detection in mission-critical machines, or financial forecasting for investment management.