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Quantum annealing is the quantum computational equivalent of the classical approach to solving optimization problems known as simulated annealing. Boixo et al.report experimental evidence for the realization of quantum annealing processes that are unexpectedly robust against noise and imperfections.

Quantum tunnelling may be advantageous for quantum annealing, but multiqubit tunnelling has not yet been observed or characterized theoretically. Here, the authors demonstrate that 8-qubit tunnelling plays a role in a D-Wave Two device through a nonperturbative theory and experimental data.

Gradient-based hybrid quantum-classical algorithms are often initialised with random, unstructured guesses. Here, the authors show that this approach will fail in the long run, due to the exponentially-small probability of finding a large enough gradient along any direction.

Quantum annealing is expected to solve certain optimization problems more efficiently, but there are still open questions regarding the functioning of devices such as D-Wave One. A numerical and experimental investigation of its performance shows evidence for quantum annealing with 108 qubits.

Typical quantum error correcting codes assign fixed roles to the underlying physical qubits. Now the performance benefits of alternative, dynamic error correction schemes have been demonstrated on a superconducting quantum processor.

A recurrent, transformer-based neural network, called AlphaQubit, learns high-accuracy error decoding to suppress the errors that occur in quantum systems, opening the prospect of using neural-network decoders for real quantum hardware.

Experimental measurements of high-order out-of-time-order correlators on a superconducting quantum processor show that these correlators remain highly sensitive to the quantum many-body dynamics in quantum computers at long timescales.

As a benchmark for the development of a future quantum computer, sampling from random quantum circuits is suggested as a task that will lead to quantum supremacy—a calculation that cannot be carried out classically.

A universal pseudo-cooling method based on a Maxwell-demon-like swapping sequence is proposed. A controlled Hamiltonian gate is used to identify lower energy states of the system and to drive the system to those states. An experimental implementation using a quantum optical network exhibits a fidelity higher than 0.978.

Masoud Mohseni, Peter Read, Hartmut Neven and colleagues at Google's Quantum AI Laboratory set out investment opportunities on the road to the ultimate quantum machines.

Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one.

A study establishes a scalable approach to engineer and characterize a many-body-localized discrete time crystal phase on a superconducting quantum processor.

It is hoped that quantum computers may be faster than classical ones at solving optimization problems. Here the authors implement a quantum optimization algorithm over 23 qubits but find more limited performance when an optimization problem structure does not match the underlying hardware.

Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.

Two below-threshold surface code memories on superconducting processors markedly reduce logical error rates, achieving high efficiency and real-time decoding, indicating potential for practical large-scale fault-tolerant quantum algorithms.

A study demonstrating increasing error suppression with larger surface code logical qubits, implemented on a superconducting quantum processor.

Repetition codes running many cycles of quantum error correction achieve exponential suppression of errors with increasing numbers of qubits.