Optical Processor Achieves Comparable Process Tomography to Leading Quantum Platforms

Variational quantum process tomography, a technique for characterising quantum states, typically relies on complex digital quantum computers. However, researchers Vladlen Galetsky, Paul Kohl, and Janis Nötzel, all from the Emmy-Noether Group at the Technical University of Munich, demonstrate a promising alternative using classical optics to mimic quantum behaviour. Their work establishes the first benchmark comparing the performance of this optical approach against several leading digital quantum platforms, including IBM’s and QuTech’s superconducting processors, and Quandela’s photonic processor. The results reveal that the optical system achieves higher fidelity and faster convergence than its superconducting counterparts, particularly as the complexity of the quantum circuits increases, suggesting that optical processors represent a viable and competitive technology for near-term quantum algorithm development and a range of variational quantum applications.

Quantum computing promises revolutionary advances in fields like medicine, materials science, and artificial intelligence. However, building practical quantum computers presents significant challenges, particularly in maintaining the delicate quantum state of information and scaling up the number of qubits. Photonic processors, which use light to carry and manipulate quantum information, are emerging as a compelling alternative, offering potential advantages in both coherence and scalability and the possibility of room-temperature operation and integration with existing telecommunications infrastructure.

Researchers are now rigorously benchmarking these optical processors against established technologies to assess their capabilities. A recent study focused on variational quantum process tomography – a technique for characterising quantum devices – to compare a locally developed optical processor with leading superconducting systems from IBM and QuTech, as well as a quantum optical processor from Quandela. The team evaluated performance using metrics like process fidelity – a measure of how accurately the processor performs a quantum operation – convergence speed, and processing time.

The results demonstrate a clear advantage for optical processors, particularly at higher circuit depths. While superconducting systems suffered from increasing errors due to the loss of quantum information, the optical processors maintained higher fidelity and faster convergence, achieving fidelities of 0.8 after nine iterations. This suggests that photons’ inherent resilience to environmental noise allows for more complex computations before errors accumulate.

The team also investigated how a classical optical setup, using a technique called ‘one-hot encoding’ to mimic quantum behavior, performed compared to a true quantum optical processor. Their analysis revealed comparable performance, indicating that this classical approach is a viable pathway for exploring and developing quantum algorithms. The benchmarking framework and experimental results demonstrate that photonic processors are strong contenders for near-term quantum algorithm deployment, particularly in hybrid variational contexts.

Further investigation revealed that thermal noise in the optical components dominated over other imperfections, such as misalignment and dark counts from single-photon sources. This research represents a crucial step towards realizing the potential of photonic quantum computing and may accelerate the development of practical quantum technologies for applications including quantum machine learning and optimization. The inclusion of the Quandela processor allowed for a direct comparison between different optical quantum computing approaches, suggesting that optical quantum computing is a viable alternative to superconducting quantum computing and may offer advantages in terms of scalability and performance.