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Scalable quantum computing can become a reality with error correction, provided that coherent qubits can be constructed in large arrays 1 , 2 . The key premise is that physical errors can remain both small and sufficiently uncorrelated as devices scale, so that logical error rates can be exponentially suppressed. However, impacts from cosmic rays and latent radioactivity violate these assumptions. An impinging particle can ionize the substrate and induce a burst of quasiparticles that destroys qubit coherence throughout the device. High-energy radiation has been identified as a source of error in pilot superconducting quantum devices 3 – 5 , but the effect on large-scale algorithms and error correction remains an open question. Elucidating the physics involved requires operating large numbers of qubits at the same rapid timescales necessary for error correction. Here, we use space- and time-resolved measurements of a large-scale quantum processor to identify bursts of quasiparticles produced by high-energy rays. We track the events from their initial localized impact as they spread, simultaneously and severely limiting the energy coherence of all qubits and causing chip-wide failure. Our results provide direct insights into the impact of these damaging error bursts and highlight the necessity of mitigation to enable quantum computing to scale. 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.

Superconducting quantum bits (qubits) rely on ultra-thin, amorphous oxide tunneling barriers that can have significant inhomogeneities and defects as grown. This can result in relatively large uncertainties and deleterious effects in the circuits, limiting the scalability. Finding a robust solution to the junction reproducibility problem has been a long-standing goal in the field. Here, we demonstrate a transformational technique for controllably tuning the electrical properties of aluminum-oxide tunnel junctions. This is accomplished using a low-voltage, alternating-bias applied individually to the tunnel junctions, with which resistance tuning by more than 70% can be achieved. The data indicates an improvement of coherence and reduction of two-level system defects. Transmission electron microscopy shows that the treated junctions are predominantly amorphous, albeit with a more uniform distribution of alumina coordination across the barrier. This technique is expected to be useful for other devices based on ionic amorphous materials.Amorphous aluminum oxide tunnel junctions are important for cryogenic and room temperature devices. Here, the authors demonstrate the use of alternating-bias-assisted annealing for transforming and tuning transmon qubit junctions, where giant increases in excess of 70% in the room temperature resistance can be achieved.

The challenge underlying superconducting quantum computing is to remove materials bottleneck for highly coherent quantum devices. The nonuniformity and complex structural components in the underlying quantum circuits often lead to local electric field concentration, charge scattering, dissipation and ultimately decoherence. Here we visualize interface dipole heterogeneous distribution of individual Al/AlO x /Al junctions employed in transmon qubits by broadband terahertz scanning near-field microscopy that enables the non-destructive and contactless identification of defective boundaries in nano-junctions at an extremely precise nanoscale level. Our THz nano-imaging tool reveals an asymmetry across the junction in electromagnetic wave-junction coupling response that manifests as hot (high intensity) vs cold (low intensity) spots in the spatial electrical field structures and correlates with defected boundaries from the multi-angle deposition processes in Josephson junction fabrication inside qubit devices. The demonstrated local electromagnetic scattering method offers high sensitivity, allowing for reliable device defect detection in the pursuit of improved quantum circuit fabrication for ultimately optimizing coherence times. To enhance the development of superior quantum circuits, the ability to detect defects accurately is crucial. The authors use broadband terahertz scanning near-field optical microscopy that enables the non-destructive and contactless identification of defective boundaries in nano-junctions employed in transmon qubits at an extremely precise nanoscale level.

The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor 1 . A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits 2 – 7 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2 53 (about 10 16 ). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy 8 – 14 for this specific computational task, heralding a much-anticipated computing paradigm. 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.

Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the planar connectivity graph native to our hardware; however, we also apply the QAOA to the Sherrington–Kirkpatrick model and MaxCut, non-native problems that require extensive compilation to implement. For hardware-native problems, which are classically efficient to solve on average, we obtain an approximation ratio that is independent of problem size and observe that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size. Circuits involving several thousand gates still present an advantage over random guessing but not over some efficient classical algorithms. Our results suggest that it will be challenging to scale near-term implementations of the QAOA for problems on non-native graphs. As these graphs are closer to real-world instances, we suggest more emphasis should be placed on such problems when using the QAOA to benchmark quantum processors.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.

Realizing the potential of quantum computing requires sufficiently low logical error rates 1 . Many applications call for error rates as low as 10 −15 (refs. 2 – 9 ), but state-of-the-art quantum platforms typically have physical error rates near 10 −3 (refs. 10 – 14 ). Quantum error correction 15 – 17 promises to bridge this divide by distributing quantum logical information across many physical qubits in such a way that errors can be detected and corrected. Errors on the encoded logical qubit state can be exponentially suppressed as the number of physical qubits grows, provided that the physical error rates are below a certain threshold and stable over the course of a computation. Here we implement one-dimensional repetition codes embedded in a two-dimensional grid of superconducting qubits that demonstrate exponential suppression of bit-flip or phase-flip errors, reducing logical error per round more than 100-fold when increasing the number of qubits from 5 to 21. Crucially, this error suppression is stable over 50 rounds of error correction. We also introduce a method for analysing error correlations with high precision, allowing us to characterize error locality while performing quantum error correction. Finally, we perform error detection with a small logical qubit using the 2D surface code on the same device 18 , 19 and show that the results from both one- and two-dimensional codes agree with numerical simulations that use a simple depolarizing error model. These experimental demonstrations provide a foundation for building a scalable fault-tolerant quantum computer with superconducting qubits. Repetition codes running many cycles of quantum error correction achieve exponential suppression of errors with increasing numbers of qubits.

We present a transmon qubit fabrication technique that yields systematic improvements in T 1 relaxation times. We encapsulate the surface of niobium and prevent the formation of its lossy surface oxide. By maintaining the same superconducting metal and only varying the surface, this comparative investigation examining different capping materials, such as tantalum, aluminum, titanium nitride, and gold, as well as substrates across different qubit foundries demonstrates the detrimental impact that niobium oxides have on coherence times of superconducting qubits, compared to native oxides of tantalum, aluminum or titanium nitride. Our surface-encapsulated niobium qubit devices exhibit T 1 relaxation times 2–5 times longer than baseline qubit devices with native niobium oxides. When capping niobium with tantalum, we obtain median qubit lifetimes above 300 μs, with maximum values up to 600 μs. Our comparative structural and chemical analysis provides insight into why amorphous niobium oxides may induce higher losses compared to other amorphous oxides.

Josephson junctions supply the nonlinear inductance element in superconducting qubits. In the widely used transmon configuration, where the junction is shunted by a large capacitor, the low charging energy minimizes the sensitivity of the qubit to charge noise while maintaining the necessary anharmonicity to qubit states. We report here low-frequency transport measurements on small standalone junctions and identically fabricated capacitively-shunted junctions that show two distinct features normally attributed to small capacitance junctions near zero bias: reduced switching currents and prominent finite resistance associated with phase diffusion in the current-voltage characteristic. Our transport data reveals the existence of phase fluctuations in transmons arising from intrinsic junction capacitance.

A lensless holographic in-line point source microscope was envisioned more than half a century ago, but its realization with electron waves has come short due to not only difficulties inherent in Fresnel-type reconstruction methods, but also to the lack of an adequate (spatially and temporally coherent) point source. With the recent creation of ultrasharp nanotips, which can field emit electrons from a single atom at their apex, an extremely coherent electron source is available that provides a great boost to the holographic method. The spatial coherence of such nanotips is a few Å, while their temporal coherence is characterized by a value of energy dispersion (FWHM) as low as 0.1 eV. In this work we ascertain the use of such a microscope in the imaging of nanoscale structures and interfaces. The method is suitable for two- and three- dimensional imaging of solid nanoparticles, thin crystals, and surfaces, but also for biological entities. We show how improvements in the reconstruction method can be made by applying the rigorous Fresnel-Kirchhoff diffraction theory adapted to Electron Optics. Sub-nanometer resolution is achievable for beam energy between 100–200 eV.