A new scalable framework, compilation-based quantum process tomography (CQPT), characterizes quantum processes by using a trainable compiler that inverts the unknown process and returns the output toward the original input.

CQPT starts with a known input quantum state, applies a trainable process, and is optimized to maximize the final output’s return to the original input, effectively reconstructing intermediate quantum processes with minimal measurement outcomes per input state.

Researchers from Tohoku University, NAIST, and the University of Information Technology in Vietnam introduced CQPT to characterize quantum devices more efficiently than traditional QPT.

CQPT has two complementary formulations: a Kraus-operator framework for unitary or near-unitary operations and a Choi-matrix framework for general noisy processes, broadening applicability to realistic devices.

CQPT is designed to be scalable and practical for larger quantum systems where traditional quantum process tomography becomes prohibitively costly.

Dr. Le Bin Ho notes CQPT’s potential as a practical alternative to full QPT, especially for larger quantum systems.

The work includes theoretical analysis and extensive numerical simulations demonstrating accurate reconstruction with reduced measurement overhead.

Optimization in CQPT can be performed with only a single measurement outcome per input state, significantly reducing data requirements.

The article emphasizes the importance of efficient quantum process characterization for the future of quantum computing and sensing, highlighting scalable tomography as systems grow.

The study was conducted by teams from Tohoku University, NAIST, and Vietnam National University (Ho Chi Minh City) and published in Advanced Quantum Technologies on February 26, 2026, under the title Advancing Quantum Process Tomography through Quantum Compilation.

Future work aims at experimental implementation and improving robustness against hardware imperfections to bridge theory and practical hardware diagnostics.