Rutgers and IBM Achieve Real-Time Control Across 100 Qubits
Researchers from Rutgers and IBM have demonstrated real-time feedback control over 100 qubits. They achieved this by using repeated measurements and resets to bring chaotic behavior under control and into a steerable state.
Rutgers spearheaded the theoretical work, whereas IBM provided the hardware, a 156-qubit Quantum Heron processor, from which the team selected a connected chain of up to 100 qubits. This demonstration addresses the crucial feedback and calibration layer necessary for future error-corrected quantum machines, which have yet to be built.
Details of the Demonstration
The team programmed the processor to alternate between two conflicting tasks. One task scrambled information among neighboring qubits, increasing disorder. The other task checked individual qubits and reset them when necessary, guiding the system towards an orderly, pre-selected state. When resets occurred more frequently than scrambling, control was maintained.
At an approximate even split, the entire system would flip simultaneously, a phenomenon the physicists liken to water freezing into ice. The researchers noted that the agreement with theoretical predictions improved as the systems grew larger, which suggests the effect was not merely a coincidence of a small circuit.
The verified figures include:
- Processor: IBM Quantum Heron, 156 total qubits
- Qubits controlled: A connected chain of up to 100
- Two-qubit operations: Nearly 5,000
- Measurements and resets: Nearly 5,000, executed while the rest of the chip remained active
- Publication: Nature Physics
The researchers describe this as one of the most extensive demonstrations of real-time quantum feedback control to date. Maika Takita of IBM highlighted that the greater challenge wasn’t measuring a qubit, but doing so repeatedly as the rest of the processor continued to operate.
The Importance of Scalable Control
Error correction requires these check-and-reset cycles to be performed thousands of times within a single calculation, and coordinating them across a large chip has been a major hurdle. Control and calibration continue to emerge as critical challenges for quantum scaling.
For example, FirstQFM reported 33% shorter gate times on a 21-qubit QuantWare QPU using machine-learned calibration. Faster and more stable hardware also shortens the window for organizations to transition sensitive data to post-quantum cryptography, a deadline that many national regulators have already established.
For further information on IBM hardware, quantum control, and PQC migration, continue reading QNews at qnews.io.