Discover how Professor Tommaso Calarco drives the future of the quantum computer through groundbreaking quantum optimal control research and strategic leadership. From co-authoring the Quantum Manifesto to shaping the €1 billion European Quantum Flagship, his work advances scalable quantum computers, quantum simulation, quantum sensing, and quantum communication by reducing qubit errors, improving control fidelity, and securing Europe’s place in the global quantum computing r

Discover how Professor Andrew Dzurak, CEO of Diraq, is transforming the race to build the top quantum computer using silicon CMOS spin qubits. From the world’s first silicon-based qubits to near-99% fidelity two-qubit gates, his breakthroughs align quantum research with industrial chipmaking. Learn how CMOS scalability, cryogenic integration, and wafer-level innovation are powering the next era of quantum computing.

Robert (Bob) Sutor is a leading voice in quantum computing, bridging deep theory with real-world clarity. Author of Dancing with Qubits – Second Edition and Dancing with Python, his work spans quantum algorithms, NISQ systems, machine learning, symbolic computation, AXIOM, MathML, OpenMath, and industry strategy. Through teaching, advisory roles, and reports, Sutor shapes the quantum ecosystem, guiding startups, enterprises, and global adoption.

Peter Chapman has been instrumental in transforming IonQ into one of the world’s leading quantum computing companies, taking it from research to enterprise adoption. Under his leadership, IonQ went public, doubled revenues, and expanded into quantum networking with acquisitions like Qubitekk and ID Quantique. Through strategic partnerships with AstraZeneca, Ansys, and SK Telecom, Chapman positioned IonQ at the forefront of the quantum revolution.

The Quantum 100 Report 2025 highlights leaders shaping the future of quantum technologies. From hardware to applications, policy to communication, quantum is moving from theory to enterprise impact across healthcare, finance, climate, and security. Pitchworks is building quantum initiatives to connect innovators, enterprises, and investors, fostering a global quantum community that accelerates adoption and ensures this revolution translates into real-world impact.

Generative AI is revolutionizing Drug-Target Interaction (DTI) prediction by automating drug design, improving binding accuracy, and accelerating discovery timelines. It replaces manual, trial-and-error methods with predictive, end-to-end pipelines. While challenges like interpretability and data quality persist, real-world case studies show its promise. GenAI is set to reshape pharma by making drug discovery faster, smarter, and more scalable.

Explore how Generative AI is reshaping molecular simulations in drug discovery—from accelerating virtual screening to designing novel drug compounds and predicting protein structures. With advanced tools like AlphaFold and DiffDock, and platforms like Kwapio, biotech teams can now reduce costs, increase speed, and bring AI-native clarity to in silico testing. Powered by Pitchworks VC Studio, this shift is driving faster, smarter, and more scalable drug development.

Gen AI is revolutionizing Molecular Property Prediction (MPP) by enabling accurate, scalable insights from SMILES, graphs, and 3D structures. Platforms like Pitchworks and Kwapio combine AI with agile project management—integrating tasks, data, and models into a seamless workflow. This boosts speed, reproducibility, and collaboration, turning MPP into a reliable engine for drug discovery and formulation.

This blog explores how Gen AI is transforming R&D workflows by improving ADMET predictions, reducing risk, and enhancing decision-making. It highlights Kwapio, an AI-powered platform developed by Pitchworks Gen AI Labs and GCC, which integrates predictive models with project management. The result: faster, more accurate drug development and better collaboration across biotech, CROs, and pharma teams.

Generative AI is transforming drug optimization by accelerating analog design, improving ADMET predictions, and automating synthesis planning. Traditionally taking 12–24 months, this phase can now be shortened by up to 50%, with cost savings of 30–40%. From analog generation to compound prioritization, Gen AI enables faster, smarter decisions—empowering teams and reshaping the future of drug development.