Efficient quantum process tomography to enable scalable optical quantum computing

Optical quantum computers are attracting increasing attention as a next-generation computing technology with high speed and scalability. However, accurately characterizing complex optical processes, in which multiple optical modes interact to generate quantum entanglement, has been considered an extremely difficult task.

A KAIST research team has overcome this limitation by developing a highly efficient technique that allows comprehensive characterization of complex multi-mode quantum operations in experiments. This technology, capable of analyzing large-scale operations with less data, represents an important step toward scalable quantum computing and quantum communication technologies.

A research team led by Professor Young-Sik Ra from the Department of Physics has developed a multi-mode quantum process tomography technique capable of efficiently identifying the characteristics of second-order nonlinear optical quantum processes essential for optical quantum computing.

The study is published in Natural photonics.

Efficient CT scan technology for quantum computers

Tomography is a technique, similar to a medical scanner, which reconstructs an invisible internal structure from various measurements. Similarly, quantum computing requires a method that reconstructs the inner workings of quantum operations using various measurement data.

To outperform classical computers, a quantum computer must be able to manipulate a large number of quantum units (qubits or qumodes) at the same time. However, as the number of qubits or quantum optical modes (qumodes) increases, the resources required for tomography increase exponentially, making existing technologies incapable of analyzing systems with even five or more optical modes.

With the newly developed technique, the research team is now able to clearly determine what is actually happening inside an optical quantum computer, such as during a scanner.

Presentation of a new mathematical framework based on amplification and noise matrices

Inside a quantum computer, several optical modes interact in a very complex and entangled way. The research team introduced a new mathematical framework that precisely describes second-order multi-mode nonlinear optical quantum processes.

This method analyzes how input states change during a given operation using two key components: the “amplification matrix,” which describes how average light fields are transformed, and the “noise matrix,” which captures noise or loss introduced by environmental interactions.

Together, these components create a “quantum state map” that allows precise and simultaneous observation of the ideal quantum evolution of light (unitary changes) and the inevitable noise (non-unitary changes) present in real devices. This leads to a much more realistic characterization of how an optical quantum computer actually works.

Reduce required measurement data and expand analysis to 16 modes

To determine how a quantum operation works, the research team entered several types of quantum states and observed how the results evolved. They then applied a statistical method called maximum likelihood estimation to reconstruct the inner workings that most accurately explains the collected data while satisfying the necessary physical conditions.

Using this approach, the research team significantly reduced the amount of measurement data required. While existing methods quickly become impractical – requiring huge data sets even for systems with little more than a few modes and typically limiting analysis to around five modes – the new technique overcomes this bottleneck.

The team successfully achieved the world’s first experimental characterization of a large-scale optical quantum operation involving 16 modes, an unprecedented milestone in the field.

Professor Young-Sik Ra said: “This research significantly increases the efficiency of quantum process tomography, a fundamental technology essential for quantum computing. The acquired technology will go a long way in improving the scalability and reliability of various quantum technologies, including quantum computing, quantum communication and quantum sensing.