What's going on inside quantum computers?: New method simplifies process tomography - EurekAlert!
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image: Overview of compilation-based quantum process tomography (CQPT). The left panel shows the main idea: an unknown quantum process transforms an input state into an output state, and CQPT uses a trainable "compiler" to learn the process by forcing the final state to return to the original input. The right panels illustrate two implementations of CQPT: a Kraus-based approach for unitary or near-unitary processes, and a Choi-based approach for general noisy processes. view more Credit: ©Le Bin Ho et al. Quantum computers work by applying quantum operations, such as quantum gates, to delicate quantum states. Ideally, quantum computers can solve complex equations at staggeringly fast speeds that vastly outpace regular computers. In real hardware, the operations of quantum computers often deviate from the ideal behavior because of device imperfections and unwanted noise from the environment. To build reliable quantum machines, researchers need a way to accurately determine what a quantum device is actually doing. Quantum process tomography (QPT) is a standard method for this. However, traditional QPT becomes very costly as the system grows, because the number of required measurements and calculations increases rapidly with the number of qubits. To address this challenge, a research team from Tohoku University, the Nara Institute of Science and Technology (NAIST), and the University of Information Technology (Vietnam National University, Ho Chi Minh City) has introduced a new framework called compilation-based quantum process tomography (CQPT). The central idea of CQPT is simple. The method starts with a known input quantum state, applies a trainable process following the unknown process, and then works backwards to evaluate how well the final output returns to the original input. The "return-to-input" model is optimized to reconstruct the underlying quantum processes that make up the steps in-between the input and output. Importantly, the framework is designed so that optimization can conveniently be performed using only a single measurement outcome per input state. The researchers developed two complementary versions of the CQPT: one based on Kraus operators, and one based on the Choi matrix. Together, these two approaches allow CQPT to handle a wide range of quantum operations and noisy processes relevant to modern quantum devices. "Efficient and scalable methods for characterizing quantum processes are important for the future of quantum computing and quantum sensing," Dr.
Le Bin Ho said. "We need such methods to check whether quantum gates and circuits work correctly, identify hardware errors, calibrate devices, and support quantum error correction." Dr. Le believes that CQPT could become a practical alternative to standard quantum process tomography, especially for larger quantum systems where full tomography is no longer realistic due to high costs. The current study demonstrates that CQPT is feasible through sound theoretical analysis and numerical simulations. The framework offers a promising way to make quantum tomography more efficient. Next steps will involve tackling the challenge of implementing it in real experiments. The researchers plan to focus on developing hardware-ready versions of the method and improving its robustness. The findings were published in Advanced Quantum Technologies on February 26, 2026.
Journal Advanced Quantum Technologies DOI 10.1002/qute.202500494 Article Title Advancing Quantum Process Tomography through Quantum Compilation Article Publication Date 26-Feb-2026 Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.