Evidence for the utility of quantum computing before fault tolerance.

Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. However, the greatest impediment to realizing its full potential is noise that is inherent to these systems. The widely accepted solution to this challenge is the implementation of fault-tolerant quantum circuits, which is out of reach for current processors. Here we report experiments on a noisy 127-qubit processor and demonstrate the measurement of accurate expectation values for circuit volumes at a scale beyond brute-force classical computation. We argue that this represents evidence for the utility of quantum computing in a pre-fault-tolerant era. These experimental results are enabled by advances in the coherence and calibration of a superconducting processor at this scale and the ability to characterize1 and controllably manipulate noise across such a large device. We establish the accuracy of the measured expectation values by comparing them with the output of exactly verifiable circuits. In the regime of strong entanglement, the quantum computer provides correct results for which leading classical approximations such as pure-state-based 1D (matrix product states, MPS) and 2D (isometric tensor network states, isoTNS) tensor network methods2,3 break down. These experiments demonstrate a foundational tool for the realization of near-term quantum applications4,5.

Error mitigation extends the computational reach of a noisy quantum processor.

Quantum computation, a paradigm of computing that is completely different from classical methods, benefits from theoretically proved speed-ups for certain problems and can be used to study the properties of quantum systems1. Yet, because of the inherently fragile nature of the physical computing elements (qubits), achieving quantum advantages over classical computation requires extremely low error rates for qubit operations, as well as substantial physical qubits, to realize fault tolerance via quantum error correction2,3. However, recent theoretical work4,5 has shown that the accuracy of computation (based on expectation values of quantum observables) can be enhanced through an extrapolation of results from a collection of experiments of varying noise. Here we demonstrate this error mitigation protocol on a superconducting quantum processor, enhancing its computational capability, with no additional hardware modifications. We apply the protocol to mitigate errors in canonical single- and two-qubit experiments and then extend its application to the variational optimization6-8 of Hamiltonians for quantum chemistry and magnetism9. We effectively demonstrate that the suppression of incoherent errors helps to achieve an otherwise inaccessible level of accuracy in the variational solutions using our noisy processor. These results demonstrate that error mitigation techniques will enable substantial improvements in the capabilities of near-term quantum computing hardware.

Supervised learning with quantum-enhanced feature spaces.

Machine learning and quantum computing are two technologies that each have the potential to alter how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous in pattern recognition, with support vector machines (SVMs) being the best known method for classification problems. However, there are limitations to the successful solution to such classification problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element in the computational speed-ups enabled by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here we propose and experimentally implement two quantum algorithms on a superconducting processor. A key component in both methods is the use of the quantum state space as feature space. The use of a quantum-enhanced feature space that is only efficiently accessible on a quantum computer provides a possible path to quantum advantage. The algorithms solve a problem of supervised learning: the construction of a classifier. One method, the quantum variational classifier, uses a variational quantum circuit1,2 to classify the data in a way similar to the method of conventional SVMs. The other method, a quantum kernel estimator, estimates the kernel function on the quantum computer and optimizes a classical SVM. The two methods provide tools for exploring the applications of noisy intermediate-scale quantum computers3 to machine learning.

Antibody Drug Conjugates in Urological Cancers: A Review of the Current Landscape.

PURPOSE OF REVIEW: Our review delves into the progress across urological malignancies and discusses ongoing challenges and future directions in antibody-drug conjugate (ADC) development, emphasising their transformative potential in cancer care. RECENT FINDINGS: ADCs have advanced from hematologic to solid tumours, notably in breast cancer, and are now pivotal in metastatic urological cancers as both monotherapies and in combination regimens, underscored by the FDA's approval of enfortumab vedotin and sacituzumab govitecan for metastatic urothelial cancer. Progress in metastatic prostate cancer, particularly with ADCs targeting PSMA and STEAP1, is noteworthy, although renal cell cancer presents ongoing challenges. There is a continual search for agents in the metastatic, relapsed testicular cancer landscape. ADCs have emerged as a pivotal innovation in oncology, blending targeted antibody therapy with potent cytotoxic drugs, significantly advancing treatment options for urological malignancies.

Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets.

Quantum computers can be used to address electronic-structure problems and problems in materials science and condensed matter physics that can be formulated as interacting fermionic problems, problems which stretch the limits of existing high-performance computers. Finding exact solutions to such problems numerically has a computational cost that scales exponentially with the size of the system, and Monte Carlo methods are unsuitable owing to the fermionic sign problem. These limitations of classical computational methods have made solving even few-atom electronic-structure problems interesting for implementation using medium-sized quantum computers. Yet experimental implementations have so far been restricted to molecules involving only hydrogen and helium. Here we demonstrate the experimental optimization of Hamiltonian problems with up to six qubits and more than one hundred Pauli terms, determining the ground-state energy for molecules of increasing size, up to BeH2. We achieve this result by using a variational quantum eigenvalue solver (eigensolver) with efficiently prepared trial states that are tailored specifically to the interactions that are available in our quantum processor, combined with a compact encoding of fermionic Hamiltonians and a robust stochastic optimization routine. We demonstrate the flexibility of our approach by applying it to a problem of quantum magnetism, an antiferromagnetic Heisenberg model in an external magnetic field. In all cases, we find agreement between our experiments and numerical simulations using a model of the device with noise. Our results help to elucidate the requirements for scaling the method to larger systems and for bridging the gap between key problems in high-performance computing and their implementation on quantum hardware.

Surface-sensitive two-dimensional magneto-fingerprint in mesoscopic Bi2Se3 channels.

Periodic Aharonov­Bohm and Altshuler­Aronov­Spivak oscillations have traditionally been observed in lateral transport through patterned mesoscopic loops of diffusive conductors. However, our studies of perpendicular-to-plane magnetotransport in straight-channel, diffusive devices of epitaxial Bi2Se3 surprisingly reveal signatures of Aharonov­Bohm orbits, periodic conductance fluctuation magneto-fingerprints, even though the devices are not explicitly patterned into loops. We show that the length scale of these orbits corresponds to the typical perimeter of triangular terraces found on the surface of these thin film devices, strongly suggesting that the periodic magneto-fingerprint arises from coherent scattering of electron waves from the step-edges. Our interpretation is bolstered by control measurements in devices without such surface morphology that only show a conventional, aperiodic magneto-fingerprint. These results show that lithographically patterned Bi2Se3 devices provide a novel class of mesoscopic physical systems for systematic studies of coherent surface sensitive transport.

The increasing indications of FDG-PET/CT in the staging and management of Invasive Bladder Cancer.

The management of locally advanced muscle invasive bladder cancer (MIBC) often necessitates neo-adjuvant chemotherapy (NAC) to eliminate any micro-metastatic disease prior to definitive radical cystectomy (RC) and pelvic lymph node dissection (PLND). The most common imaging techniques traditionally used during this process are computerised tomography (CT) and magnetic resonance imaging (MRI), both of which lack a high sensitivity for nodal staging. In this paper, we attempt to review the evolving indications of F-fluoro-2-deoxy-D-glucose positron emission tomography/computerised tomography (FDG-PET/CT) imaging, in the pre-clinical and post-treatment staging of bladder cancer, with a focus on its ability to evaluate response to NAC. We concluded that use of FDG-PET/CT allows for improved nodal staging and metastatic disease detection, compared to traditional imaging modalities. This enabled earlier detection of tumour response to NAC and/or residual disease, impacting factors such as duration of chemotherapy, with its associated adverse effects, and timing of surgical intervention. However, further studies are required to reliably assess its impact on both overall and disease-free survival.

Large discrete jumps observed in the transition between Chern states in a ferromagnetic topological insulator.

A striking prediction in topological insulators is the appearance of the quantized Hall resistance when the surface states are magnetized. The surface Dirac states become gapped everywhere on the surface, but chiral edge states remain on the edges. In an applied current, the edge states produce a quantized Hall resistance that equals the Chern number C = ±1 (in natural units), even in zero magnetic field. This quantum anomalous Hall effect was observed by Chang et al. With reversal of the magnetic field, the system is trapped in a metastable state because of magnetic anisotropy. We investigate how the system escapes the metastable state at low temperatures (10 to 200 mK). When the dissipation (measured by the longitudinal resistance) is ultralow, we find that the system escapes by making a few very rapid transitions, as detected by large jumps in the Hall and longitudinal resistances. Using the field at which the initial jump occurs to estimate the escape rate, we find that raising the temperature strongly suppresses the rate. From a detailed map of the resistance versus gate voltage and temperature, we show that dissipation strongly affects the escape rate. We compare the observations with dissipative quantum tunneling predictions. In the ultralow dissipation regime, two temperature scales (T 1 ~ 70 mK and T 2 ~ 145 mK) exist, between which jumps can be observed. The jumps display a spatial correlation that extends over a large fraction of the sample.

Giant anisotropic magnetoresistance in a quantum anomalous Hall insulator.

When a three-dimensional ferromagnetic topological insulator thin film is magnetized out-of-plane, conduction ideally occurs through dissipationless, one-dimensional (1D) chiral states that are characterized by a quantized, zero-field Hall conductance. The recent realization of this phenomenon, the quantum anomalous Hall effect, provides a conceptually new platform for studies of 1D transport, distinct from the traditionally studied quantum Hall effects that arise from Landau level formation. An important question arises in this context: how do these 1D edge states evolve as the magnetization is changed from out-of-plane to in-plane? We examine this question by studying the field-tilt-driven crossover from predominantly edge-state transport to diffusive transport in Crx(Bi,Sb)(2-x)Te3 thin films. This crossover manifests itself in a giant, electrically tunable anisotropic magnetoresistance that we explain by employing a Landauer-Büttiker formalism. Our methodology provides a powerful means of quantifying dissipative effects in temperature and chemical potential regimes far from perfect quantization.

Visualization of superparamagnetic dynamics in magnetic topological insulators.

Quantized Hall conductance is a generic feature of two-dimensional electronic systems with broken time reversal symmetry. In the quantum anomalous Hall state recently discovered in magnetic topological insulators, time reversal symmetry is believed to be broken by long-range ferromagnetic order, with quantized resistance observed even at zero external magnetic field. We use scanning nanoSQUID (nano-superconducting quantum interference device) magnetic imaging to provide a direct visualization of the dynamics of the quantum phase transition between the two anomalous Hall plateaus in a Cr-doped (Bi,Sb)2Te3 thin film. Contrary to naive expectations based on macroscopic magnetometry, our measurements reveal a superparamagnetic state formed by weakly interacting magnetic domains with a characteristic size of a few tens of nanometers. The magnetic phase transition occurs through random reversals of these local moments, which drive the electronic Hall plateau transition. Surprisingly, we find that the electronic system can, in turn, drive the dynamics of the magnetic system, revealing a subtle interplay between the two coupled quantum phase transitions.