Google DeepMind and Quantum AI teams introduced a new, AI-powered decoder system for quantum computers that can identify computing errors with unparalleled accuracy. Called AlphaQubit, it’s the result of a collaboration that brings together Google DeepMind’s expertise in machine learning with Google Quantum AI’s proficiency in quantum machines. In the researchers’ tests, AlphaQubit displayed incredible accuracy compared to existing quantum decoders, making 6% fewer errors than tensor network methods in the largest Sycamore experiments. While tensor networks are quite accurate themselves, the problem is that they’re extremely slow. AlphaQubit, on the other hand, can identify errors with more accuracy and at much greater speed, more than fast enough to scale up to handle real-world quantum computing operations. AlphaQubit has some other useful features too. For instance, it has the ability to report “condence levels” on inputs and outputs, which means there’s lots of potential to improve the performance of quantum processors in future. Moreover, when trained on samples of up to 25 rounds of error correction, the system maintained its high performance for up to 100,000 rounds, meaning it can generalize scenarios far exceeding its training data.
Charting the quantum frontiers- three top methods
By using the concepts of quantum mechanics to execute operations at previously unheard-of speeds and efficiency, quantum computing is revolutionizing industries and changing the technological landscape. The potential of quantum computing to address practical issues increases with the emergence of novel quantum dynamics such as quantum advantage and error correction. Numerous quantum computing models are being investigated, each employing a unique strategy to use quantum events for computation. 1) Gate-based quantum computing (circuit model): Quantum gates are used to manipulate qubits in order to carry out operations in a quantum circuit in gate-based quantum computing. These quantum gates enable exponentially faster computations on certain issues by altering the state of qubits based on entanglement and superposition. 2) Quantum annealing: Quantum annealers use quantum tunneling to investigate potential solutions rather than a sequence of gate-based processes. Finding the shortest path in intricate logistical networks or minimizing energy in a system are two optimization tasks that quantum annealing excels at. 3) Topological quantum computing: It is a more speculative technique that aims to create robust quantum gates by braiding anyons, which are quasiparticles, around one another. Topological quantum computing’s primary benefit is its intrinsic resilience to mistakes and noise, which is a key problem in previous quantum models. Measurement-based quantum computing: MBQC simplifies several parts of quantum computation by managing the entanglement and measurements in place of conventional quantum gates.