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Abstract Quantum computers are now on the brink of outperforming their classical counterparts. One way to demonstrate the advantage of quantum computation is through quantum random sampling performed on quantum computing devices. However, existing tools for verifying that a quantum device indeed performed the classically intractable sampling task are either impractical or not scalable to the quantum advantage regime. The verification problem thus remains an outstanding challenge. Here, we experimentally demonstrate efficiently verifiable quantum random sampling in the measurement-based model of quantum computation on a trapped-ion quantum processor. We create and sample from random cluster states, which are at the heart of measurement-based computing, up to a size of 4 × 4 qubits. By exploiting the structure of these states, we are able to recycle qubits during the computation to sample from entangled cluster states that are larger than the qubit register. We then efficiently estimate the fidelity to verify the prepared states—in single instances and on average—and compare our results to cross-entropy benchmarking. Finally, we study the effect of experimental noise on the certificates. Our results and techniques provide a feasible path toward a verified demonstration of a quantum advantage.

Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where $\textit{parameterized quantum circuits}$ (PQCs) are used as learning models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via $\textit{random Fourier features}$ (RFF). In this work, we establish necessary and sufficient conditions under which RFF does indeed provide an efficient dequantization of variational quantum machine learning for regression. We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which are necessary for a regression problem to admit a potential quantum advantage via PQC based optimization.

Recently, the dynamics of quantum systems that involve both unitary evolution and quantum measurements have attracted attention due to the exotic phenomenon of measurement-induced phase transitions. The latter refers to a sudden change in a property of a state of $n$ qubits, such as its entanglement entropy, depending on the rate at which individual qubits are measured. At the same time, quantum complexity emerged as a key quantity for the identification of complex behaviour in quantum many-body dynamics. In this work, we investigate the dynamics of the quantum state complexity in monitored random circuits, where $n$ qubits evolve according to a random unitary circuit and are individually measured with a fixed probability at each time step. We find that the evolution of the exact quantum state complexity undergoes a phase transition when changing the measurement rate. Below a critical measurement rate, the complexity grows at least linearly in time until saturating to a value $e^{\Omega(n)}$. Above, the complexity does not exceed $\operatorname{poly}(n)$. In our proof, we make use of percolation theory to find paths along which an exponentially long quantum computation can be run below the critical rate, and to identify events where the state complexity is reset to zero above the critical rate. We lower bound the exact state complexity in the former regime using recently developed techniques from algebraic geometry. Our results combine quantum complexity growth, phase transitions, and computation with measurements to help understand the behavior of monitored random circuits and to make progress towards determining the computational power of measurements in many-body systems.

Molecular glues (MGs) and proteolysis-targeting chimeras (PROTACs) are used to modulate protein–protein interactions (PPIs), via induced proximity between compounds that have little or no affinity for each other naturally. They promote either reversible inhibition or selective degradation of a target protein, including ones deemed undruggable by traditional therapeutics. Though native MS (nMS) is capable of analyzing multiprotein complexes, the behavior of these artificially induced compounds in the gas phase is still not fully understood, and the number of publications over the past few years is still rather limited. Here, we studied two MG-induced complexes between mTORFRB and FKBP12 as well as a PROTAC-induced complex between FKBP51FK1 and the von Hippel-Lindau E3 ligase (VHL). Native MS combined with collision-induced dissociation (CID) provided a way of measuring not only the formation of these complexes but also their dissociation pathways. Both protein complexes seem to eject preferably the centrally located small (compared to the mass of the proteins) ligand upon CID, rather than dissociating a peripheral subunit, as is often observed for naturally occurring protein complexes. In contrast, chemically induced dissociation in solution generated complementary data to CID, by disrupting the PPI surface, which resulted in more diverse MS spectra that preserved the stronger interactions in solution. [ABSTRACT FROM AUTHOR]

We examine the onset and resilience of emergent time periodicity in a few-body all-to-all interacting Lipkin–Meshkov–Glick model, where one of the constituents is locally in contact with a thermal bath. Employing both a collision model framework and a suitable time-continuous description, we show that stable time-periodic behavior can only be exhibited when the bath acts as a purely dissipative channel. We assess the role that the microscopic interactions within the system play, establishing that for the all-to-all model the introduction of temperature leads to a melting of the emergent time periodicity, in contrast to stable long-time behavior which can be maintained for nearest neighbor XXZ type interactions. [ABSTRACT FROM AUTHOR]

Existing challenges in surgical education (See one, do one, teach one) as well as the COVID-19 pandemic make it necessary to develop new ways for surgical training. Therefore, this work describes the implementation of a scalable remote solution called "TeleSTAR" using immersive, interactive and augmented reality elements which enhances surgical training in the operating room. The system uses a full digital surgical microscope in the context of Ear–Nose–Throat surgery. The microscope is equipped with a modular software augmented reality interface consisting an interactive annotation mode to mark anatomical landmarks using a touch device, an experimental intraoperative image-based stereo-spectral algorithm unit to measure anatomical details and highlight tissue characteristics. The new educational tool was evaluated and tested during the broadcast of three live XR-based three-dimensional cochlear implant surgeries. The system was able to scale to five different remote locations in parallel with low latency and offering a separate two-dimensional YouTube stream with a higher latency. In total more than 150 persons were trained including healthcare professionals, biomedical engineers and medical students. [ABSTRACT FROM AUTHOR]

Quantum computers are now on the brink of outperforming their classical counterparts. One way to demonstrate the advantage of quantum computation is through quantum random sampling performed on quantum computing devices. However, existing tools for verifying that a quantum device indeed performed the classically intractable sampling task are either impractical or not scalable to the quantum advantage regime. The verification problem thus remains an outstanding challenge. Here, we experimentally demonstrate efficiently verifiable quantum random sampling in the measurement-based model of quantum computation on a trapped-ion quantum processor. We create and sample from random cluster states, which are at the heart of measurement-based computing, up to a size of 4 × 4 qubits. By exploiting the structure of these states, we are able to recycle qubits during the computation to sample from entangled cluster states that are larger than the qubit register. We then efficiently estimate the fidelity to verify the prepared states—in single instances and on average—and compare our results to cross-entropy benchmarking. Finally, we study the effect of experimental noise on the certificates. Our results and techniques provide a feasible path toward a verified demonstration of a quantum advantage. Demonstrations of quantum advantage relying on sampling hard-to-compute probability distributions are plagued by difficulties in efficiently confirming the correctness of their output, which is known as the verification problem. Here, the authors use a trapped-ion platform to demonstrate efficient verification of quantum random sampling in measurement-based quantum computing. [ABSTRACT FROM AUTHOR]