2/28/2023 0 Comments Quantum error gameOne color corresponds to the state zero, the other to one, and a move in a game of quantum Go involves turning pieces over. ![]() ![]() However, there are certain key differences from a conventional game of Go: all the pieces are already distributed around the board, and each of them is white on one side and black on the other. The qubits are distributed across the board like pieces. “You can imagine the elements of a quantum computer as being just like a Go board,” says Marquardt, getting to the core idea behind his project. Quantum error-correction is like a game of Go with strange rules However, the operations that this requires are not only complex but must also leave the quantum information itself intact. This and other peculiarities of the quantum world mean that quantum information needs regular repairs – that is, quantum error correction. The downside is that quantum information is highly sensitive to noise from its environment. This entanglement explains the tremendous processing power of quantum computers when it comes to solving certain complex tasks at which conventional computers are doomed to fail. In a quantum computer’s processor, there are even multiple qubits superimposed as part of a joint state. Unlike conventional digital bits, a qubit can adopt not only the two states zero and one, but also superpositions of both states. The basis for quantum information is the quantum bit, or qubit. To understand what it involves, you need to look at the way quantum computers work. There is even the prospect that, with sufficient training, this approach will outstrip other error-correction strategies. In the paper, the team demonstrates that artificial neural networks with an AlphaGo-inspired architecture are capable of learning – for themselves – how to perform a task that will be essential for the operation of future quantum computers: quantum error correction. The first area of application are quantum computers, as shown by the recent paper, which includes a significant contribution by Thomas Fösel, a doctoral student at the Max Planck Institute in Erlangen. “By doing so, we profit from rapid progress in the area of artificial intelligence.”Īrtificial neural networks could outstrip other error-correction strategies “We take the latest ideas from computer science and apply them to physical systems,” explains Florian Marquardt. Now, the Erlangen-based researchers are using neural networks of this kind to develop error-correction learning for a quantum computer.Īrtificial neural networks are computer programs that mimic the behaviour of interconnected nerve cells (neurons) – in the case of the research in Erlangen, around two thousand artificial neurons are connected with one another. Unlike a human, the program was able to practise hundreds of thousands of games in a short time, eventually surpassing the best human player. ![]() Rather, AlphaGo used artificial neural networks, which can recognize visual patterns and are even capable of learning. Given that a game of Go has more combinations of moves than there are estimated to be atoms in the universe, this required more than just sheer processing power. In 2016, the computer program AlphaGo won four out of five games of Go against the world’s best human player. Florian Marquardt, Director at the Max Planck Institute for the Science of Light, and his team have now presented a quantum error correction system that is capable of learning thanks to artificial intelligence. The plan is to combat this using active protection based on quantum error correction. However, the quantum states are extremely sensitive to constant interference from their environment. Quantum computers could solve complex tasks that are beyond the capabilities of conventional computers. Credit: Max Planck Institute for the Science of Light Neural networks enable learning of error correction strategies for computers based on quantum physics Learning quantum error correction: the image visualizes the activity of artificial neurons in the Erlangen researchers’ neural network while it is solving its task.
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