Keio University, Mitsubishi Chemical Push Toward Practical Quantum Reservoir Computing

Insider Brief

• IBM, Keio University, and Mitsubishi Chemical advanced quantum reservoir computing by using quantum processors to improve machine learning predictions in an experiment.
• The researchers developed a “repeated measurements” method that reduced execution time and increased accuracy compared to traditional quantum reservoir computing techniques.
• The work highlights how IBM’s Quantum Innovation Centers foster industry-academic collaborations to accelerate practical quantum computing applications.

IBM and Keio University researchers have taken a critical step in advancing quantum reservoir computing, potentially speeding up complex machine learning tasks crucial for industries ranging from robotics to financial modeling.

The partnership, which includes Mitsubishi Chemical, aims to leverage quantum computers to improve reservoir computing — a machine learning approach that simplifies training compared to traditional methods such as neural networks. The team’s 2023 experiment, detailed in IBM’s Quantum Research Blog, represents a significant step toward practical quantum computing applications.

In reservoir computing, Reservoir input data are processed through a dynamic system, or “reservoir,” to uncover patterns that can be analyzed with simple models like linear regression. Scientists are eager to explore the technique because it reduces the heavy training demands typical of traditional neural networks.

In reservoir computing, input data are processed through a dynamic system, or “reservoir,” to uncover patterns that can be analyzed with simple models like linear regression. Scientists are eager to explore the technique because it reduces the heavy training demands typical of traditional neural networks. Quantum reservoir computing applies the same idea but uses quantum processors as the reservoir and, because quantum computers can theoretically handle large, intricate data more efficiently, this potentially offers even greater speed and efficiency.

“Quantum computers are naturally well-suited to high-dimensional data processing, and may ultimately prove more computationally powerful than classical reservoirs,” the IBM team writes in the post.

Predicting Robot Movements

IBM’s collaboration with Keio University, a private research university located in Tokyo, and Mitsubishi Chemical, also based in Tokyo, demonstrated the advantage of quantum reservoir computing by predicting movements of a “soft robot,” a flexible machine powered by air pressure.

The Keio University-led team converted robotic movement data into quantum states, processed these states through IBM’s quantum processors, and utilized linear regression on the output. This innovative approach, termed “repeated measurements,” involved additional qubits to streamline data collection. Instead of repeatedly resetting and running the quantum circuits for each data point, the researchers simultaneously measured additional qubits, significantly reducing execution time and boosting accuracy.

Their tests, conducted on IBM Quantum processors with up to 120 qubits, demonstrated measurable improvements, according to the post. The repeated measurement technique notably outperformed traditional methods, offering faster and more precise results. IBM noted these findings could soon surpass classical computational capabilities, marking a milestone in quantum computing’s practical applications.

Challenges Remain

Despite promising initial results, IBM emphasized that significant work remains before quantum reservoir computing routinely tackles real-world challenges. The researchers anticipate future exploration into applications beyond robotics, including financial risk modeling — a complex, non-linear problem ideal for quantum computing solutions.

The authors write: “Much more work will be needed in the field of RC and QRC before these methods are able to yield useful results to practical problems. However, the researchers say that, even today, their utility-scale experiments may already be beyond classical simulation methods.”

Future research directions might include the exploration of quantum reservoir computing for hard nonlinear problems, such as financial risk modeling.

IBM’s Quantum Innovation Centers (QICs), like Keio University’s, are instrumental in progressing such cutting-edge research. Since Keio became one of the initial IBM Quantum Hubs in 2017, over 40 such centers have emerged globally. These hubs connect academic expertise with industry needs, fostering a dynamic international quantum computing community.