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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.

Experimental demonstration of quantum speedup that scales with the system size is the goal of near-term quantum computing. Here, the authors demonstrate such scaling advantage for a D-Wave quantum annealer over analogous classical algorithms in simulations of frustrated quantum magnets.

Cosmic rays flying through superconducting quantum devices create bursts of excitations that destroy qubit coherence. Rapid, spatially resolved measurements of qubit error rates make it possible to observe the evolution of the bursts across a chip.

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.

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.

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.

Ensuring high-fidelity quantum gates while increasing the number of qubits poses a great challenge. Here the authors present a scalable strategy for optimizing frequency trajectories as a form of error mitigation on a 68-qubit superconducting quantum processor, demonstrating high single- and two-qubit gate fidelities.

Physical realizations of qubits are often vulnerable to leakage errors, where the system ends up outside the basis used to store quantum information. A leakage removal protocol can suppress the impact of leakage on quantum error-correcting codes.

It is often assumed that systems that can be analyzed accurately via mean-field theory would not be worth looking at using quantum algorithms, given entanglement plays no key role. Here, the authors show instead that a quantum advantage can be expected for simulating the exact time evolution of such electronic systems.

A sparsified SYK model is constructed using learning techniques and the corresponding traversable wormhole dynamics are observed, representing a step towards a program for studying quantum gravity in the laboratory.

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.

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.

Fault-tolerant quantum computation is still far, but there could be ways in which quantum error correction could improve currently available devices. Here, the authors show how to exploit existing quantum codes through only post-processing and random measurements in order to mitigate errors in NISQ devices.

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 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.