IronBridge is an industry-defining, cryptographic key generation platform that employs quantum computers to generate quantum-enhanced cryptographic keys. Using the IronBridge platform, both classical algorithms (e.g., RSA or AES) and post-quantum algorithms (e.g., CRYSTALS-Dilithium and Falcon) can be seeded to provide cryptographic keys that offer superior protection against even the most powerful of adversaries.

TKET is the leading, open-source development toolkit that enables state-of-the-art optimisation and manipulation of quantum circuits for NISQ devices. With platform agnosticism at its core, TKET is integrated with most commercially available quantum hardware platforms (e.g., IBM, Honeywell, Google, IonQ and others) and quantum programming tools (e.g., Cirq, Qiskit and Pennylane). TKET’s user base is growing exponentially, enabling more users across industry and academia to extract value from quantum hardware as it becomes increasingly available online.

Currently in advanced beta testing, EUMEN is an enterprise-grade quantum computational chemistry package and development ecosystem, enabling a new era of molecular and materials simulations. Developed in close collaboration with Fortune 500 partners, EUMEN’s modular workflow enables both computational chemists and quantum algorithm developers to easily mix and match the latest quantum algorithms with advanced subroutines and error mitigation techniques to obtain best-in-class results using quantum platforms. EUMEN is also deployed internally by Cambridge Quantum in our own advanced research programs where we are working with global clients in areas such as new material discovery for carbon sequestration, drug design and discovery and hydrogen storage.

Our goal is to realise definitive and unequivocal quantum computational advantage as soon as possible – this motivates everything we do. Although we are ultimately interested in all quantum algorithms, at present, we have identified three problems which show promise for early quantum advantage, including Monte Carlo estimation, optimisation and solving Partial Differential Equations (PDEs). In each of these areas we are partnered with industry leaders to tailor best-in-class quantum solutions to problems at the very heart of their business.

In collaboration with our industrial, academic and governmental partners, Cambridge Quantum designs and engineers novel, application-motivated Quantum Machine Learning (QML) algorithms. To date, we have worked with a wealth of the world’s largest organisations to deploy purpose-built QML algorithms across industries such as finance, healthcare, pharma, energy and logistics. Our resource-efficient and cross-platform capabilities enable us to extract the most from quantum devices today, propelling us ever closer to our goal of quantum advantage.

The rapidly emerging field of Quantum Natural Language Processing (QNLP) and its underlying theoretical foundations, has been pioneered by the team at Cambridge Quantum. Our structural approach takes advantage of mathematical analogies between theoretical linguistics and quantum theory to design “quantum native” NLP pipelines. Combined with advances in QML, we have successfully trained quantum computers to perform elementary text classification and question-answering tasks, paving the way for more scalable intelligence systems.

As a proprietary and operational neural network-based algorithm for financial trading, Arrow employs pattern recognition systems that learn profitable trading strategies. It is currently in use by the world’s largest financial institutions and has been engineered with inspiration from our work in parameterised quantum circuit learning models. At present, we are working towards full quantum implementations of Arrow that will further enhance our industry-leading performance.