Insider Brief
- Kipu Quantum and IonQ have jointly solved the most complex protein folding problem ever executed on a quantum computer, successfully modeling a 3D structure of up to 12 amino acids using IonQ’s Forte hardware and Kipu’s BF-DCQO algorithm.
- The project also set records for solving dense quantum problems, including all-to-all connected QUBO and HUBO instances with up to 36 qubits, demonstrating optimal performance and highlighting the power of trapped-ion qubit connectivity.
- The achievement represents a major step toward near-term quantum advantage in biology, drug discovery, and materials science, and the collaboration now extends to early access of IonQ’s 64- and 256-qubit systems for industrial-scale problem solving.
PRESS RELEASE — Kipu Quantum, a leader in application and hardware-specific quantum computing solutions, and IonQ (NYSE: IONQ), a leading commercial quantum computing and networking company, proudly announced a record achievement: the successful solution of the most complex protein folding problem ever executed on a quantum computer.
This joint effort is the largest quantum computation of its kind to-date and demonstrates the synergy between Kipu’s innovative algorithmic framework and IonQ’s state-of-the-art hardware.
In their latest study, the teams solved:
- The largest protein folding problem solved and executed on quantum hardware, comprising a 3D use case of up to 12 amino acids–– an industry record on its own and a promising path towards commercial use of quantum computing for drug discovery.
- All-to-all connected spin-glass problems (QUBO) and MAX-4-SAT problems (HUBO) using up to 36 qubits, obtaining optimal solutions in all instances — an industry record for dense digital quantum computing problems executed on quantum hardware.
All instances were executed on IonQ’s Forte generation systems using Kipu Quantum’s flagship algorithm- BF-DCQO (Bias-Field Digitized Counterdiabatic Quantum Optimization). The results advance the frontier of near-term quantum computing and have the potential to make a large impact on biology and drug development.
BF-DCQO provides a non-variational, iterative method that is both accurate and resource-efficient. This algorithm can achieve better solutions with fewer quantum operations in each subsequent iteration. This is especially critical for protein folding, where several long-range interactions are present, making the all-to-all connectivity of IonQ’s trapped ion systems an important asset.
“Connectivity between qubits in quantum computing impacts efficiency and accuracy. Having all-to-all connectivity means faster time to solution, with higher quality results, and is a unique characteristic of trapped-ion systems. Combining that with Kipu’s unique quantum algorithms results in unparalleled performance with minimal resources, a sine qua non path to quantum advantage with IonQ’s next-generation system,“ said Prof. Enrique Solano, Co-CEO and Co-Founder of Kipu Quantum. ”This collaboration is not only breaking performance records, but is also positioning us to actively pursue quantum advantage using trapped-ion technologies with IonQ for a wide class of industry use cases.”
”Our collaboration with Kipu Quantum has delivered breakthroughs in both speed and quality that sets a new standard for what’s possible in quantum computing today”, said Ariel Braunstein, SVP of Product at IonQ. “This collaboration demonstrates the value of every part of IonQ’s quantum computing stack – from the quality of our qubits and how they are connected, to our compiler and operating system to how error mitigation techniques are applied. Kipu’s capabilities complement IonQ’s cutting-edge systems perfectly and this collaboration is only the first step in our mutual pursuit of near-term commercial value for customers across multiple industries.”
IonQ and Kipu Quantum have extended their collaboration with early access to IonQ’s upcoming 64-qubit and 256-qubit chips, unlocking the potential to address even larger, industrially relevant challenges. Both companies are exploring additional use cases capable of delivering quantum-advantage in the near term across drug discovery, logistics, and material design.
Read the full study here: https://arxiv.org/pdf/2506.07866
