Collaborating Authors

Technical Perspective: Deciphering Errors to Reduce the Cost of Quantum Computation

Communications of the ACM

Quantum computers may one day upend cryptography, help design new materials and drugs, and accelerate many other computational tasks. A quantum computer's memory is a quantum system, capable of being in a superposition of many different bit strings at once. It can take advantage of quantum interference to run uniquely quantum algorithms which can solve some (but not all) computational problems much faster than a regular classical computer. Experimental efforts to build a quantum computer have taken enormous strides forward in the last decade, leading to today's devices with over 50 quantum bits ("qubits"). Governments and large technology companies such as Google, IBM, and Microsoft, as well as a slew of start-ups, have begun pouring money into the field hoping to be the first with a useful quantum computer.

Deep neural decoders for near term fault-tolerant experiments Machine Learning

Finding efficient decoders for quantum error correcting codes adapted to realistic experimental noise in fault-tolerant devices represents a significant challenge. In this paper we introduce several decoding algorithms complemented by deep neural decoders and apply them to analyze several fault-tolerant error correction protocols such as the surface code as well as Steane and Knill error correction. Our methods require no knowledge of the underlying noise model afflicting the quantum device making them appealing for real-world experiments. Our analysis is based on a full circuit-level noise model. It considers both distance-three and five codes, and is performed near the codes pseudo-threshold regime. Training deep neural decoders in low noise rate regimes appears to be a challenging machine learning endeavour. We provide a detailed description of our neural network architectures and training methodology. We then discuss both the advantages and limitations of deep neural decoders. Lastly, we provide a rigorous analysis of the decoding runtime of trained deep neural decoders and compare our methods with anticipated gate times in future quantum devices. Given the broad applications of our decoding schemes, we believe that the methods presented in this paper could have practical applications for near term fault-tolerant experiments.

Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation Artificial Intelligence

In order to implement large scale quantum computations it is necessary to be able to store and manipulate quantum information in a manner that is robust to the unavoidable errors introduced through interaction of the physical qubits with a noisy environment. The known strategy for achieving such robustness is to encode a single logical qubit into the state of many physical qubits, via a quantum error correcting code, from which it is possible to actively diagnose and correct errors that may occur [1, 2]. While many quantum error correcting codes exist, topological quantum codes [1-8], in which only local operations are required to diagnose and correct errors, are of particular interest as a result of their experimental feasibility [9-15]. In particular, the surface code has emerged as an especially promising candidate for large-scale fault-tolerant quantum computation, due to the combination of its comparatively low overhead and locality requirements, coupled with the availability of convenient strategies for the implementation of all required logical gates [16, 17]. In fact, current road maps towards the realization of robust quantum computing have identified surface code based approaches as the most feasible methodology for achieving this goal [18].

Roadmap for 1000 Qubits Fault-tolerant Quantum Computers - Amit Ray


How many qubits are needed to out-perform conventional computers, how to protect a quantum computer from the effects of decoherence and how to design more than 1000 qubits fault-tolerant large scale quantum computers, these are the three basic questions we want to deal in this article. Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing are discussed. Earlier we have discussed 7 Core Qubit Technologies for Quantum Computing, 7 Key Requirements for Quantum Computing. Spin-orbit Coupling Qubits for Quantum Computing and AI, Quantum Computing Algorithms for Artificial Intelligence, Quantum Computing and Artificial Intelligence, Quantum Computing with Many World Interpretation Scopes and Challenges and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on practical issues related to designing large-scale quantum computers.

Optimizing Quantum Error Correction Codes with Reinforcement Learning Artificial Intelligence

Quantum error correction is widely thought to be the key to fault-tolerant quantum computation. However, determining the most suited encoding for unknown error channels or specific laboratory setups is highly challenging. Here, we present a reinforcement learning framework for optimizing and fault-tolerantly adapting quantum error correction codes. We consider a reinforcement learning agent tasked with modifying a quantum memory until a desired logical error rate is reached. Using efficient simulations of a surface code quantum memory with about 70 physical qubits, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of physical qubits, for various error models of interest. Moreover, we show that agents trained on one task are able to transfer their experience to similar tasks. This ability for transfer learning showcases the inherent strengths of reinforcement learning and the applicability of our approach for optimization both in off-line simulations and on-line under laboratory conditions.