Five of six Q4Bio finalists used IBM quantum hardware

Five of six finalists in Wellcome Leap’s Q4Bio challenge specifically chose IBM quantum hardware to run large-scale demonstrations of algorithms designed for healthcare applications. Algorithmiq, in collaboration with Cleveland Clinic and IBM, earned the $2 million Q4Bio prize for simulating processes in photodynamic therapy, a cancer treatment. Q4Bio is targeting a timeframe of three to five years for these algorithms to run on near-term quantum computers. Q4Bio Challenge Drives IBM Quantum Hardware Adoption IBM quantum systems were important for five of six teams competing in Wellcome Leap’s Q4Bio challenge, a demonstration of practical necessity alongside potential preference in the nascent field of quantum biology. The hardware enabled execution of circuits approaching 100 qubits, a scale necessary for validating algorithms designed to tackle complex genomic data, and supported continuous refinement crucial for identifying bottlenecks. This reliance on IBM’s technology underscores the current landscape where utility-scale quantum computers are essential for translating theoretical algorithms into demonstrable results. The Q4Bio challenge, launched in 2023 with $40 million in funding, specifically required teams to run large-scale demonstrations on real quantum hardware, targeting algorithms viable within three to five years. Fred Chong, Professor at University of Chicago and Chief Scientist for Quantum Software at Infleqtion, stated that Heron QPUs could meet the challenge criteria of exceeding 50 qubits and 1,000 quantum gates. His team used these capabilities to demonstrate a hybrid quantum-classical approach improving biomarker identification compared to purely classical methods, a result contingent on access to advanced hardware. The winning team’s work used quantum computing to simulate key processes in photodynamic therapy (PDT), a cancer treatment. This framework incorporated novel methods for active space selection, state preparation, measurement, and post-processing, enabling large-scale molecular electronic structure simulations on IBM’s quantum hardware. “This is not a toy demonstration, it involves biologically significant sequences, represented on quantum hardware using data partitioning techniques and tailored depth-reduction we developed specifically for genomic data,” explained a representative from the Quantum Pangenomics project (University of Oxford and Sanger Institute). James McCafferty, Chief Information Officer at the Wellcome Sanger Institute, stated that the team’s ability to encode a whole genome onto a quantum computer represents at least one order of magnitude improvement over any prior efforts to represent DNA on quantum machines. In their workflow, classical systems handle problem formulation, iteration, and analysis, and quantum hardware is invoked for the most computationally challenging subproblems. Encoding a whole genome onto a quantum computer is a world first and represents at least one order of magnitude improvement over any other efforts to represent DNA on quantum machines. James McCafferty, Chief Information Officer at the Wellcome Sanger Institute Algorithmiq’s Hybrid Approach Wins $2 Million Q4Bio Prize By executing circuits with up to 100 qubits, the collaboration demonstrated a scalable path toward quantum advantage in drug discovery and development.

The team’s work leveraged GPUs and quantum processing units working in tandem, an increasingly promising avenue for hybrid workflows that combine the strengths of both classical and quantum computing. IBM’s quantum systems were critical to the project’s success, enabling the execution of circuits at scales approaching 100 qubits and supporting the continuous validation loop needed to identify bottlenecks and ensure robustness. This work, they add, “provides one of the clearest indications to date that quantum computing can begin to impact real, chemically relevant problems, rather than simplified benchmarks.” Q4Bio’s success, according to observers, demonstrates that quantum computing is transitioning from a purely research endeavor to a developmental one, with teams working toward common goals and making meaningful progress on previously intractable problems. “Q4Bio showed that when teams with complementary expertise work toward a common goal, they can make meaningful progress on problems that no single discipline can solve alone.” The challenge’s structure, with its emphasis on real-world applications and scalable algorithms, is proving to be an effective model for accelerating innovation in quantum healthcare. This is not a toy demonstration, it involves biologically significant sequences, represented on quantum hardware using data partitioning techniques and tailored depth-reduction we developed specifically for genomic data. Sergii Strelchuk, associate professor of Computer Science at Oxford University The University of Oxford and Sanger Institute used an IBM Quantum Heron r2 to encode the Hepatitis-D genome. Our work has already identified novel cancer biomarkers for clinical evaluation, and future quantum machines will allow us to discover even more promising biomarkers that we hope will improve treatment outcomes. Fred Chong, Professor at University of Chicago and Chief Scientist for Quantum Software at Infleqtion IBM Quantum Heron Enables Biomarker Discovery & Scalable Algorithms “For our project, IBM hardware provided the number of qubits, gate fidelity, and sampling rate needed to make our experiments viable,” explained Sergii Strelchuk, associate professor of Computer Science at Oxford University, detailing the requirements for simulating processes in photodynamic therapy, a light-activated cancer treatment. Infleqtion’s project, conducted with the University of Chicago and MIT, also relied on IBM Quantum Heron r2 processors for quantum-enhanced biomarker discovery from multimodal cancer data. The success of both teams hinged on hybrid quantum-classical workflows, augmenting classical Density Functional Theory calculations with high-fidelity molecular data generated by quantum algorithms. Strelchuk further emphasized the practical implications of their genomic data encoding, stating, “The fact that the encoded information can be retrieved through our index-reported verification method sends a clear signal: quantum data encoding for genomics is no longer aspirational, it is ready to scale.” This retrieval capability, achieved through data partitioning techniques and tailored depth-reduction, signifies a shift from theoretical possibility to practical implementation. “And full credit goes to [IBM] in helping us achieve this,” added a representative from the Quantum Pangenomics project (University of Oxford and Sanger Institute), acknowledging the crucial role of IBM’s hardware in their success. The convergence of advanced quantum hardware, innovative algorithms, and collaborative research efforts is accelerating the application of quantum computing to real-world healthcare problems, moving beyond theoretical demonstrations toward tangible advancements in disease understanding and treatment. It’s encouraging to see so many research teams implementing QCSC workflows, where classical and quantum resources work together to achieve what neither can alone. Jay Gambetta, director of IBM Research Source: https://www.ibm.com/quantum/blog/q4bio-finalists More like thisQuantum Research NewsUF’s ECE’s Laura Kim gets NIH grant for quantum endometriosis studyQuantum Research NewsQuantum Lutetium clock at CQT sets new world record for timekeepingQuantum AlgorithmsClassiq and partners test quantum solutions for gas networksQuantum Research NewsBlavatnik Awards recognize Columbia’s rising quantum starsStay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals. Tags: