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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.

In many-body localized (MBL) systems, entanglement propagates throughout the system despite the absence of transport. Early experiments have relied on population measurements to indirectly probe these entanglement dynamics. However, because the entanglement results from phase relationships between localized orbitals, it is more naturally probed with phase sensitive algorithms and measurement. In this thesis, we use an array of nearest neighbor coupled superconducting qubits to introduce phase sensitive protocols to the experimental study of MBL systems. We establish that system is MBL by demonstrating disorder induced ergodicity breaking and the presence of effective nonlocal interactions.We then use density matrix reconstructions to observe the hallmark slow growth of entanglement and provide a site-resolved spatial and temporal map of the developing entanglement. We also inspect the capacity of the MBL phase to preserve quantum correlations by observing the decay of distillable entanglement when Bell pair embedded in an MBL environment and dephased by remote excitation. In superconducting quantum processors, such as that used in the MBL study above, dissipation leads to computational errors and must be minimized. To that end, we also describe coherence engineering experiments in terms of the low power internal quality factor Qi of coplanar waveguide (CPW) resonators, a figure of merit characterizing dissipation in the quantum computing regime. We investigate titanium nitride as a superconducting base metal for quantum circuits. By optimizing the deposition conditions, we achieve a record low-power Qi in CPW resonators. We also characterize the dielectric loss due to flux trapping hole arrays. Since flux traps are commonly used to prevent magnetic vortex formation and dielectric loss is a limiting dissipation mechanism, it is important to estimate the contribution of flux traps to the dielectric dissipation budget. We find that for reasonable hole patterns the dielectric loss can be small while preventing vortex formation.

Quantum many-body systems display rich phase structure in their low-temperature equilibrium states. However, much of nature is not in thermal equilibrium. Remarkably, it was recently predicted that out-of-equilibrium systems can exhibit novel dynamical phases that may otherwise be forbidden by equilibrium thermodynamics, a paradigmatic example being the discrete time crystal (DTC). Concretely, dynamical phases can be defined in periodically driven many-body localized systems via the concept of eigenstate order. In eigenstate-ordered phases, the entire many-body spectrum exhibits quantum correlations and long-range order, with characteristic signatures in late-time dynamics from all initial states. It is, however, challenging to experimentally distinguish such stable phases from transient phenomena, wherein few select states can mask typical behavior. Here we implement a continuous family of tunable CPHASE gates on an array of superconducting qubits to experimentally observe an eigenstate-ordered DTC. We demonstrate the characteristic spatiotemporal response of a DTC for generic initial states. Our work employs a time-reversal protocol that discriminates external decoherence from intrinsic thermalization, and leverages quantum typicality to circumvent the exponential cost of densely sampling the eigenspectrum. In addition, we locate the phase transition out of the DTC with an experimental finite-size analysis. These results establish a scalable approach to study non-equilibrium phases of matter on current quantum processors.

Realizing the potential of quantum computing will require achieving sufficiently low logical error rates. Many applications call for error rates in the \\(10^-15\\) regime, but state-of-the-art quantum platforms typically have physical error rates near \\(10^-3\\). Quantum error correction (QEC) promises to bridge this divide by distributing quantum logical information across many physical qubits so that errors can be detected and corrected. Logical errors are then exponentially suppressed as the number of physical qubits grows, provided that the physical error rates are below a certain threshold. QEC also requires that the errors are local and that performance is maintained over many rounds of error correction, two major outstanding experimental challenges. Here, we implement 1D repetition codes embedded in a 2D grid of superconducting qubits which demonstrate exponential suppression of bit or phase-flip errors, reducing logical error per round by more than \\(100\\) 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 analyzing error correlations with high precision, and characterize the locality of errors in a device performing QEC for the first time. Finally, we perform error detection using a small 2D surface code logical qubit on the same device, and show that the results from both 1D and 2D codes agree with numerical simulations using a simple depolarizing error model. These findings demonstrate that superconducting qubits are on a viable path towards fault tolerant quantum computing.

Interaction in quantum systems can spread initially localized quantum information into the many degrees of freedom of the entire system. Understanding this process, known as quantum scrambling, is the key to resolving various conundrums in physics. Here, by measuring the time-dependent evolution and fluctuation of out-of-time-order correlators, we experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We engineer quantum circuits that distinguish the two mechanisms associated with quantum scrambling, operator spreading and operator entanglement, and experimentally observe their respective signatures. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate. These results open the path to studying complex and practically relevant physical observables with near-term quantum processors.

As the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations of fermionic systems remain one of the most promising directions. Here, we perform a series of quantum simulations of chemistry the largest of which involved a dozen qubits, 78 two-qubit gates, and 114 one-qubit gates. We model the binding energy of \\( H_6\\), \\( H_8\\), \\( H_10\\) and \\( H_12\\) chains as well as the isomerization of diazene. We also demonstrate error-mitigation strategies based on \\(N\\)-representability which dramatically improve the effective fidelity of our experiments. Our parameterized ansatz circuits realize the Givens rotation approach to non-interacting fermion evolution, which we variationally optimize to prepare the Hartree-Fock wavefunction. This ubiquitous algorithmic primitive corresponds to a rotation of the orbital basis and is required by many proposals for correlated simulations of molecules and Hubbard models. Because non-interacting fermion evolutions are classically tractable to simulate, yet still generate highly entangled states over the computational basis, we use these experiments to benchmark the performance of our hardware while establishing a foundation for scaling up more complex correlated quantum simulations of chemistry.

Future quantum computing systems will require cryogenic integrated circuits to control and measure millions of qubits. In this paper, we report the design and characterization of a prototype cryogenic CMOS integrated circuit that has been optimized for the control of transmon qubits. The circuit has been integrated into a quantum measurement setup and its performance has been validated through multiple quantum control experiments.

Pancreatic ductal adenocarcinoma (PDAC) has a heterogeneous tumor microenvironment (TME) comprised of myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages, neutrophils, regulatory T cells, and myofibroblasts. The precise mechanisms that regulate the composition of the TME and how they contribute to radiotherapy (RT) response remain poorly understood. In this study, we analyze changes in immune cell populations and circulating chemokines in patient samples and animal models of pancreatic cancer to characterize the immune response to radiotherapy. Further, we identify STAT3 as a key mediator of immunosuppression post-RT. We found granulocytic MDSCs (G-MDSCs) and neutrophils to be increased in response to RT in murine and human PDAC samples. We also found that RT-induced STAT3 phosphorylation correlated with increased MDSC infiltration and proliferation. Targeting STAT3 using an anti-sense oligonucleotide in combination with RT circumvented RT-induced MDSC infiltration, enhanced the proportion of effector T cells, and improved response to RT. In addition, STAT3 inhibition contributed to the remodeling of the PDAC extracellular matrix when combined with RT, resulting in decreased collagen deposition and fibrotic tissue formation. Collectively, our data provide evidence that targeting STAT3 in combination with RT can mitigate the pro-tumorigenic effects of RT and improve tumor response.

The efficacy of prospective cancer treatments is routinely estimated by in vitro cell-line proliferation screens. However, it is unclear whether tumor aggressiveness and patient survival are influenced more by the proliferative or the migratory properties of cancer cells. To address this question, we experimentally measured proliferation and migration phenotypes across more than 40 breast cancer cell-lines. Based on the latter, we built and validated individual predictors of breast cancer proliferation and migration levels from the cells’ transcriptomics. We then apply these predictors to estimate the proliferation and migration levels of more than 1000 TCGA breast cancer tumors. Reassuringly, both estimates increase with tumor’s aggressiveness, as qualified by its stage, grade, and subtype. However, predicted tumor migration levels are significantly more strongly associated with patient survival than the proliferation levels. We confirmed these findings by conducting siRNA knock-down experiments on the highly migratory MDA-MB-231 cell lines and deriving gene knock-down based proliferation and migration signatures. We show that cytoskeletal drugs might be more beneficial in patients with high predicted migration levels. Taken together, these results testify to the importance of migration levels in determining patient survival.

Background Distal pancreatectomy with celiac axis resection (DP-CAR) is a treatment option for selected patients with pancreatic cancer involving the celiac axis. A recent multicenter European study reported a 90-day mortality rate of 16%, highlighting the importance of patient selection. The authors constructed a risk score to predict 90-day mortality and assessed oncologic outcomes. Methods This multicenter retrospective cohort study investigated patients undergoing DP-CAR at 20 European centers from 12 countries (model design 2000–2016) and three very-high-volume international centers in the United States and Japan (model validation 2004–2017). The area under receiver operator curve (AUC) and calibration plots were used for validation of the 90-day mortality risk model. Secondary outcomes included resection margin status, adjuvant therapy, and survival. Results For 191 DP-CAR patients, the 90-day mortality rate was 5.5% (95 confidence interval [CI], 2.2–11%) at 5 high-volume (≥ 1 DP-CAR/year) and 18% (95 CI, 9–30%) at 18 low-volume DP-CAR centers ( P  = 0.015). A risk score with age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) score, multivisceral resection, open versus minimally invasive surgery, and low- versus high-volume center performed well in both the design and validation cohorts (AUC, 0.79 vs 0.74; P  = 0.642). For 174 patients with pancreatic ductal adenocarcinoma, the R0 resection rate was 60%, neoadjuvant and adjuvant therapies were applied for respectively 69% and 67% of the patients, and the median overall survival period was 19 months (95 CI, 15–25 months). Conclusions When performed for selected patients at high-volume centers, DP-CAR is associated with acceptable 90-day mortality and overall survival. The authors propose a 90-day mortality risk score to improve patient selection and outcomes, with DP-CAR volume as the dominant predictor.