Google Quantum AI Team Achieves Quantum Error Correction Milestone
Introduction
Quantum computers are devices that use the principles of quantum mechanics to perform computations that are impossible or impractical for classical computers. Quantum computers have the potential to solve some of the most challenging problems in science, engineering, and cryptography, such as simulating quantum systems, optimizing complex functions, and breaking encryption schemes.
However, quantum computers also face a major challenge: quantum errors. Quantum errors are random fluctuations that affect the state of the qubits, which are the basic units of quantum information. Quantum errors can arise from various sources, such as noise, interference, or decoherence. Quantum errors can corrupt the qubits and cause them to lose their quantum properties, such as superposition and entanglement. Quantum errors can also propagate and affect the outcome of the quantum computation, leading to incorrect or unreliable results.
To overcome this challenge, quantum researchers have developed the concept of quantum error correction, which is a technique that can detect and correct quantum errors without disturbing the quantum information. Quantum error correction works by encoding a logical qubit, which is the unit of quantum information that the user wants to manipulate, into multiple physical qubits, which are the actual hardware components that store and process the quantum information. By using a clever scheme of redundancy and entanglement, quantum error correction can protect the logical qubit from errors that affect the physical qubits. Quantum error correction can also allow the logical qubit to perform quantum operations and algorithms with high fidelity and accuracy.
Google’s Logical Qubit Prototype
The Google Quantum AI team has recently achieved a significant milestone in the field of quantum error correction. The team has demonstrated the first prototype of a logical qubit, which is a qubit that is protected from errors by using multiple physical qubits. The team used a superconducting quantum processor with nine qubits to create a logical qubit that can store and process quantum information with high fidelity.
The team’s logical qubit is based on a quantum error correction code called the surface code, which is one of the most promising and scalable codes for building large-scale quantum computers. The surface code works by arranging the physical qubits in a two-dimensional grid and using a combination of measurement and feedback to detect and correct errors that occur on the qubits. The surface code can also encode multiple logical qubits on the same grid and perform logical operations between them.
The team’s logical qubit prototype consists of a 3x3 grid of physical qubits, where the center qubit is the logical qubit and the surrounding eight qubits are the error correction qubits. The team used microwave pulses to initialize, manipulate, and read out the state of the logical qubit. The team also used a custom-designed quantum error correction controller to perform the error correction cycle, which involves measuring the error correction qubits and applying corrective pulses to the logical qubit based on the measurement outcomes.
The team’s logical qubit prototype achieved a fidelity of 99.2%, which means that the logical qubit can preserve its quantum state with a very low probability of error. The team also showed that the logical qubit can perform single-qubit and two-qubit logical operations with high fidelity, such as the Hadamard gate, the phase gate, and the controlled-NOT gate. The team also demonstrated that the logical qubit can run a simple quantum algorithm called the Bernstein-Vazirani algorithm, which can solve a hidden bit string problem faster than any classical algorithm.
Scaling Up the Logical Qubit
The team’s logical qubit prototype is not only a proof-of-principle demonstration, but also a scalable platform that can be extended to larger and more complex quantum systems. The team showed that the logical qubit can be connected to other logical qubits on the same grid and form a logical register, which is a collection of logical qubits that can store and process quantum information. The team also showed that the logical register can perform logical operations between different logical qubits, such as the logical swap gate, which can exchange the quantum states of two logical qubits.
The team’s logical qubit prototype can also be integrated with other quantum technologies, such as quantum memory, quantum communication, and quantum error correction. The team showed that the logical qubit can be transferred to another physical qubit on the same grid or on a different grid, which can enable quantum memory and quantum communication. The team also showed that the logical qubit can be encoded with a higher level of quantum error correction, which can further improve the fidelity and robustness of the logical qubit.
The team’s logical qubit prototype is a significant step towards the development of a large-scale useful quantum computer. The team estimates that a quantum computer with 1000 logical qubits and a logical gate fidelity of 99.9% can perform quantum computations that are beyond the reach of any classical computer. The team also expects that the logical qubit prototype can be scaled up to such a quantum computer within the next decade.
Conclusion
The Google Quantum AI team has made an important breakthrough in the field of quantum error correction. The team has demonstrated the first prototype of a logical qubit, which is a qubit that is protected from errors by using multiple physical qubits. The team used a superconducting quantum processor with nine qubits to create a logical qubit that can store and process quantum information with high fidelity. The team also showed that the logical qubit can be scaled up to larger systems and perform complex quantum algorithms. This breakthrough is the first demonstration of quantum error correction, which is essential for building reliable and powerful quantum computers.
