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IBM has enhanced Qiskit Functions, a set of pre-built software tools for utility-scale quantum research. These advances will allow scientists to perform complex chemical and machine learning studies without hardware expertise. The platform has better analytic capabilities, resource management, and multi-testing for faster iteration.

Researchers and business partners are using these functions to improve computing precision and break qubit count records. To encourage adoption, the company is offering extended licensing and free trials to qualified organizations. By making high-performance quantum operations more accessible, these innovations push supercomputing toward quantum.

IBM Announces 2026 Qiskit Functions Expansion

IBM called this the “year of quantum advantage,” and scientists are using quantum technology differently. IBM has announced major Qiskit Functions Catalog modifications to accelerate international research and democratize large-scale quantum experiments. These upgrades, which include high-performance software abstractions, updated tutorials, and automated features, should allow researchers to study quantum techniques without being quantum circuit designers.

The Abstraction Revolution: Circuit Functions vs. Application

This release focuses on expanding IBM and its ecosystem partners' Qiskit Functions Catalog, a carefully curated software services collection. By automating the most complex quantum workflow steps, these capabilities provide a “abstraction layer” for users. The catalog has two main sections to meet research needs.

Application functions are for data scientists and chemists who may not know quantum computing. Users' classical inputs are automatically mapped to quantum circuits by the function to accomplish the task at full system scale. Circuit functions are for quantum experts who work directly with circuits but need advanced error mitigation, suppression, and translation tools to maximize output.

New Tools for Faster Iterations

The 2026 edition introduces advances to reduce research "friction". One of the biggest advances is the ability to run up to four tests at once, allowing researchers to refine their theories faster.

IBM added resource insights and smarter analysis. New workload reports highlight quantum QPU, GPU, and traditional CPU consumption during a job. Researchers can access this data and make informed accuracy-speed trade-offs with one line of code. A typical workload report may suggest that post-processing and hardware optimization took up a lot of CPU time even though a QPU was only active for 159 seconds. This would enable efficiency gains.

IBM also mentioned “coming soon” features including real-time logs with two-qubit reduction statistics to help researchers discover issues and resume experiments.

Creating Industry and Education Records

Record-breaking testing in various fields show these functionalities' practical impact. Yonsei University researchers used the HI-VQE function to scale energy surface curve tests to 44 qubits and 96 CNOT gates, which would have been difficult with manual circuit fabrication. The University of Tokyo expanded many-body scar research to 25 qubits and 480 two-qubit gates using QESEM.

Corporate usage are expanding. Mitsubishi Chemical broke the global record using Quantum Phase Estimation circuits with over 5,000 gates and 52 qubits. Meanwhile, company ColibriTD uses performance management to boost differential equation solver accuracy by 61% and scale to 144 qubits.

Qubit Pharmaceuticals used 2,000 gates and 123 qubits to discover a medicinal medication. Their hydration-site prediction results were comparable to standard computer methods, marking a turning point for quantum-enhanced drug development.

Streamlining “Binary Optimization” Workflow

IBM demonstrated how a single function call can now perform a complex binary optimization procedure that previously required hand-built circuits and hardware expertise. A researcher can load the Q-CTRL Optimization Solver from the catalog with minimum boilerplate code to convert a typical QUBO matrix into a cost function and execute it on IBM Pittsburgh. This move lets researchers focus on quantum advantage in their fields rather than technology complexity.

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