Problems involving combinatorial and continuous optimization are ubiquitous in our modern life. Classic examples include lead compound optimization in the development of medicine, frequency band and/or transmission power optimization in wireless communications, sparse coding for compressed sensing, Boltzmann sampling in machine learning, portfolio optimization in Fin Tech, and many others. Most of those problems belong to the non-deterministic polynomial (NP), NP-complete, and NP-hard classes in complexity theory and require exponential resources as the problem size increases. The QNN exploits quantum parallel searching at below OPO threshold, collective symmetry breaking at OPO threshold, and exponential probability amplification of the solution state at above OPO threshold to address this limitation.

shinichiPost authorQNNcloud

https://qnncloud.com/

The QNNcloud has been developed with financial support from the Cabinet Office of the Government of Japan’s ImPACT program. The Nippon Telegraph and Telephone (NTT) Corporation, National Institute of Informatics (NII), Stanford University, University of Tokyo, Osaka University, Tohoku University, Tokyo University of Science, and PEZY Computing/ExaScaler are participating in this project.

shinichiPost authorWhat is QNNcloud?

Quantum neural networks (QNNs) are novel computing systems that use optical parametric oscillators (OPOs) as quantum neurons and optical homodyne measurement-feedback circuits as quantum synapses. The QNN efficiently searches for a solution for various combinatorial optimization problems by exploiting collective symmetry breaking at the OPO threshold. Users can experience what it is like to conduct real experiments with the QNN and numerical simulations based on the quantum theory of OPO networks. For those who are interested in the principles and quantum features of QNNs, the QNNcloud provides three tools: a white paper that describes quantum theory, quantum simulation capability using the Shoubu supercomputer at Riken, and quantum computation using the QNN at NTT. In the future, the QNNcloud will provide a simulation tool specifically for the development of quantum algorithms for real-world applications.

The QNNcloud is based on a network of 2000 OPOs with programmable all-to-all connections so that users can solve NP-hard Max-Cut problems up to the problem size of N = 2000 on complete graphs without the effort of imbedding a target graph in the hardware. This problem size is far above the limitations of existing quantum computing systems.

shinichiPost authorQNN’s Hardware

In a 1-km long fiber ring cavity, N = 2000 OPO pulses are simultaneously generated by exciting an intra-cavity and periodically poled LiNb03 (PPLN) waveguide device using a pump pulse train with a 1-GHz repetition frequency. A binary variable is represented by the bi-stable 0-phase and π-phase states of each OPO pulse. Each OPO pulse is prepared in a 0-phase and π-phase superposition at below threshold but with either one of the two at well above threshold. Any pair of OPO pulses can be coupled by sequentially measuring the OPO pulse amplitude, computing an appropriate feedback pulse amplitude with a field programmable gate array (FPGA), and injecting the feedback pulse into the target OPO pulse. All-to-all connections for N = 2000 OPO pulses are implemented at every round trip (lasting 5 μsec). If the external pump rate increases to above OPO threshold, the exact solution or approximate solution is obtained as an oscillation phase (0 or π-phase) configuration after 10 to 1000 round trips.external pump rate increases to above OPO threshold, the exact solution or approximate solution is obtained as an oscillation phase (0 or π-phase) configuration after 10 to 1000 round trips.

shinichiPost authorQNN Simulator

The dynamics of the QNN can be theoretically predicted by the quantum master equation taking into account the wave packet reduction induced by measurements. This theoretical model is implemented on the Shoubu supercomputer at Riken. A massive parallel simulation enables it to reproduce the quantum dynamics of the QNN in a reasonably short time.

shinichiPost authorQuantum neural network

https://en.wikipedia.org/wiki/Quantum_neural_network

Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models (sometimes also vice versa), and the other one searching for potential quantum effects in the brain.

shinichiPost authorJapan Unveils Quantum Neural Network

by Tiffany Trader

https://www.hpcwire.com/2017/11/22/japan-unveils-first-quantum-computer-prototype/

The U.S. and China are leading the race toward productive quantum computing, but it’s early enough that ultimate leadership is still something of an open question. The latest geo-region to throw its hat in the quantum computing ring is Japan. The nation will begin offering public access to a prototype quantum device over the internet for free starting Nov. 27 at https://qnncloud.com.

As reported by Japanese news outlets this week, Tokyo-based NTT along with the National Institute of Informatics and the University of Tokyo are working on a quantum computing device that exploits the properties of light. Backed with state investment, the quantum neural network (QNN) prototype is reported to be capable of prolonged operation at room temperature. The system consists of a 1km long optical fiber loop, a special optical amplifier called a PSA, and an FPGA. (See video below for a detailed explanation of how it all works.)

Source: NTT (YouTube)

The implementation, a type of Ising machine, is a departure from superconducting quantum efforts which require exotic and expensive cooling apparatus. NTT’s prototype draws just 1kW, close to an ordinary household appliance.

Unlike efforts from Google and IBM, this won’t be a universal quantum computer. The goal of the QNN is to find solutions to combinatorial optimization problems thousands of times faster than classical computers are able to (this is what Ising machines are theorized to excel at). Potential real-world use cases include easing traffic congestion, optimizing smart phone communications, and drug discovery. Project stakeholders are aiming to commercialize the system by March 2020 and are seeking participation from the community for testing and software development purposes.

“We will seek to further improve the prototype so that the quantum computer can tackle problems with near-infinite combinations that are difficult to solve, even by modern computers at high speed,” said project head Stanford University Professor Emeritus Yoshihisa Yamamoto.

Japan has quietly been building its quantum research portfolio and will kick off a ten-year, 30 billion yen ($267 million) quantum research program in April 2018.

For further background, see the Oct. 2016, Japan Science and Technology Agency (JST) announcement here: http://www.jst.go.jp/pr/announce/20161021/index.html

The Japanese news portal

Nikkeihas photos of the unveiling here.