New TNQE Method Cuts Quantum Circuit Depth for Efficient Data Encoding
Researchers have developed a new method for encoding classical data into quantum circuits more efficiently. The approach, called TNQE, reduces the need for deep circuits and heavy resource use—a common hurdle in quantum computing. Its latest variant, TNQE-unitary, further improves performance by introducing a unitary-aware constraint that simplifies operator encoding.
Guang Lin, Toshihisa Tanaka, and Qibin Zhao created TNQE as a framework for circuit-efficient quantum data encoding. It breaks down classical inputs into tensor cores and compiles them into shallow circuits using trainable, unitary-aware constraints. This method gives users direct control over circuit depth and qubit allocation.
TNQE includes two strategies: TNQE-full and TNQE-core. The latter supports parallel processing, leading to even shallower circuit structures. Unlike traditional methods, TNQE avoids long-range entangling operations, making it more hardware-friendly.
Testing on the MNIST dataset showed strong results. TNQE-unitary achieved a mean squared error of 0.021 with a circuit depth of just four. In simulations, it successfully encoded high-resolution 256 × 256 images, proving scalability. The circuits matched the shallowness of amplitude encoding but scaled better for larger images.
The framework's design ensures compatibility with existing quantum hardware. Its structured approach reduces complexity while maintaining high reconstruction quality.
TNQE and its TNQE-unitary variant offer a practical solution for efficient quantum data encoding. The method cuts down on circuit depth and resource demands, making it easier to implement on current quantum processors. With successful tests on image datasets, the framework shows promise for real-world quantum computing applications.
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