Quantum Computing Advance: IonQ and Kipu Quantum Solve Record Protein Folding Problem

IonQ and Kipu Quantum have jointly achieved a new benchmark in quantum computation by solving the largest known protein folding problem executed on quantum hardware. The collaboration utilised IonQ’s Forte system and Kipu Quantum’s BF-DCQO algorithm to successfully address a 12-amino acid protein folding challenge, alongside solving dense digital quantum computing problems using up to 36 qubits. This work, detailed in a recent study, demonstrates advancements in near-term quantum computing with potential applications in drug discovery and material science, and will be extended through early access to IonQ’s forthcoming 64- and 256-qubit chips. Both companies are actively exploring further use cases across logistics and material design to achieve quantum advantage.

Quantum computing is demonstrating increasing capability in addressing complex computational challenges. IonQ and Kipu Quantum have demonstrated the modelling of protein folding with a 12-qubit system, alongside solving dense digital computing problems – including all-to-all connected spin-glass problems and MAX-SAT problems – up to 36 qubits. This extends beyond biological modelling, showcasing a capacity to address complex combinatorial optimisation challenges with potential applications in logistics, financial modelling, and materials science.

IonQ’s trapped ion architecture, featuring all-to-all qubit connectivity, facilitates direct interaction between qubits, minimising the need for complex routing or SWAP gates that introduce errors and increase computation time. This proves particularly advantageous for algorithms like Kipu Quantum’s BF-DCQO, which relies on efficient exploration of the solution space. Kipu Quantum’s BF-DCQO algorithm prioritises resource efficiency through an iterative, non-variational approach, crucial for tackling problems with numerous interacting variables. This contrasts with variational methods that can become computationally expensive as problem size increases, making BF-DCQO a more scalable solution.

The integration of artificial intelligence with quantum workflows, through Agentic Quantum Computing, aims to further optimise problem-solving by automating algorithm selection and tuning. This combines the strengths of both quantum computing and artificial intelligence, creating a powerful synergy. The PLANQK platform plays a crucial role in facilitating the development and deployment of quantum algorithms, providing a user-friendly interface for accessing and utilising quantum computing resources. This platform enables researchers and developers to easily experiment with different algorithms and hardware configurations, accelerating innovation.

Future development focuses on leveraging larger quantum processors, specifically planned 64- and 256-qubit systems, to address currently intractable industrial problems. These larger systems will unlock the potential to tackle even more complex optimisation tasks, opening up new possibilities in fields like drug discovery, materials science, and financial modelling. Ongoing research and development efforts focus on scaling up qubit counts, improving qubit coherence, and developing new quantum algorithms.

The success of this collaboration demonstrates the growing maturity of quantum computing and its potential to transform various industries. As quantum computers continue to scale up in size and complexity, they are poised to revolutionise fields from drug discovery to materials science to finance. As quantum computers become more powerful and accessible, they are poised to play an increasingly important role in solving some of the world’s most challenging problems.