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Parallel operation of two exchange-only qubits consisting of six quantum dots arranged linearly is shown to be achievable and maintains qubit control quality compared with sequential operation, with potential for use in scaled quantum computing.

Ruiz and colleagues introduce AlphaTensor-Quantum, a deep reinforcement learning method for optimizing quantum circuits. It outperforms existing methods and is capable of finding the best human-designed solutions for relevant quantum computations in a fully automated way.

An array of optical tweezers trapping 6,100 neutral-atom qubits in 12,000 sites is experimentally realized, demonstrating performance exceeding present technologies and enabling the prospect of large-scale quantum computing and quantum error correction.

Reconfigurable arrays of up to 448 neutral atoms are used to implement and combine the key elements of a universal, fault-tolerant quantum processing architecture and experimentally explore their underlying working mechanisms.

In order to be practical, schemes for characterizing quantum operations should require the simplest possible gate sequences and measurements. Here, the authors show how random gate sequences and native measurements (followed by classical post-processing) are sufficient for estimating several gate set properties.

Colour code on a superconducting qubit quantum processor is demonstrated, reporting above-breakeven performance and logical error scaling with increased code size by a factor of 1.56 moving from distance-3 to distance-5 code.

Hole spin semiconductor qubits suffer from charge noise, but now it has been demonstrated that placing them in an appropriately oriented magnetic field can suppress this noise and improve qubit performance.

Certifying multipartite entanglement can benchmark quantum devices. Here, authors introduce versatile tests that can certify genuine multipartite entanglement and k-inseparability using only few-body measurements, enabling noise-robust benchmarking of large photonic and superconducting graph states.

A programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits is described, in which improvement of algorithmic performance using a variety of error-correction codes is enabled.

Pulse engineering techniques can be used to reduce the average Clifford gate error rates for silicon quantum dot spin qubits down to 0.043%, a factor of three improvement over state-of-the-art silicon devices.

In this alternative approach to quantum computation, the all-electrical operation of two qubits, each encoded in three physical solid-state spin qubits, realizes swap-based universal quantum logic in an extensible physical architecture.

Magic state distillation is achieved with logical qubits on a neutral-atom quantum computer using a dynamically reconfigurable architecture for parallel quantum operations.

Two-qubit logic gates in a silicon-based system are shown (using randomized benchmarking) to have high gate fidelities of operation and are used to generate Bell states, a step towards solid-state quantum computation.

The APOE-ε4 allele is the strongest genetic risk factor for late-onset Alzheimer’s disease, but it is not deterministic. Here, the authors show that common genetic variation changes how APOE-ε4 influences cognition.

A four-qubit processor of three phosphorus nuclear spins and an electron spin in silicon enables the implementation of a three-qubit Grover’s search algorithm with 95% fidelity. The implementation is based on an advanced multi-qubit gate with single-qubit gate fidelities above 99.9% and two-qubit gate fidelities above 99%.

Lithographically defined qubits are shown to support full two-qubit logic at temperatures above one kelvin by using electron spin states in silicon quantum dots.

A key component of quantum error correction is the decoding algorithm, which needs to be accurate but also with a computational overhead that doesn’t lead to backlogs and allows fast logical clock rates. Here, the authors show an FPGA-driven decoder featuring a coarse-grained parallel architecture and on-the-fly error model updates, allowing both high accuracy and real-time operation.

A materials platform using tantalum as a base layer and silicon as the substrate to construct superconducting qubits enables device performance improvements such as millisecond lifetimes and coherence times, as well as high time-averaged quality factors.

Measurements combined with post-processing of their outcomes can be used to prepare ordered quantum states. It has been shown that they can drive a Nishimori phase transition into a disordered state even in the presence of quantum errors.

An electric dipole spin resonance protocol making use of hyperfine interaction enacts high-fidelity initialization of a four-qubit nuclear spin register in silicon. This protocol allows for high-fidelity qubit control and a path towards a register-based quantum computer using the exceptional coherence properties of donors in silicon.

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.

A quantum bit that can be addressed with a gate voltage and has a very high control-fidelity can be realized in an electrically defined silicon quantum dot.

Quantum error mitigation refers to techniques that reduce, rather than correct, errors in quantum computing. Here the authors demonstrate zero-noise extrapolation applied to quantum error correction circuits on superconducting processors, effectively reducing logical errors and advancing early fault-tolerant quantum computing.

In a quantum simulation of a (2+1)D lattice gauge theory using a superconducting quantum processor, the dynamics of strings reveal the transition from deconfined to confined excitations as the effective electric field is increased.

Respiration enhances cerebrospinal fluid flow through mechanical and autonomic pathways. Inhale length and diaphragm motion influence its displacement and net flow, identifying a modifiable, noninvasive mechanism relevant to brain homeostasis.

Digital quantum simulations of Kitaev’s honeycomb model are realized for two-dimensional fermionic systems using a reconfigurable atom-array processor and used to study the Fermi–Hubbard model on a square lattice.

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.

The presence of various noises in the qubit environment is a major limitation on qubit coherence time. Here, the authors demonstrate the use a closed-loop feedback to stabilize frequency noise in a flux-tunable superconducting qubit and suggest this as a scalable approach applicable to other types of noise.

Repeated error correction creates a logical qubit encoded in the hybrid state of a superconducting circuit and a bosonic cavity, which is shown to be fully controllable under a universal single-qubit gate set.

Large-scale quantum computers will manipulate quantum information encoded in error-corrected logical qubits. A complete set of operations has now been realized on a logical qubit with error detection.

Quantum error correction of Gottesman–Kitaev–Preskill code states is realized experimentally in a superconducting quantum device.

The quantum charge-coupled device architecture is demonstrated, with its various elements integrated into a programmable trapped-ion quantum computer and performing simple quantum operations with state-of-the-art levels of error.

By using a five-qubit error-correcting code with a recently discovered flag protocol, a logical qubit that is operated fault-tolerantly is realized based on solid-state spin qubits in diamond.