Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods.

Quantum algorithms for simulating electronic ground states are slower than popular classical mean-field algorithms such as Hartree-Fock and density functional theory but offer higher accuracy. Accordingly, quantum computers have been predominantly regarded as competitors to only the most accurate and costly classical methods for treating electron correlation. However, here we tighten bounds showing that certain first-quantized quantum algorithms enable exact time evolution of electronic systems with exponentially less space and polynomially fewer operations in basis set size than conventional real-time time-dependent Hartree-Fock and density functional theory. Although the need to sample observables in the quantum algorithm reduces the speedup, we show that one can estimate all elements of the k-particle reduced density matrix with a number of samples scaling only polylogarithmically in basis set size. We also introduce a more efficient quantum algorithm for first-quantized mean-field state preparation that is likely cheaper than the cost of time evolution. We conclude that quantum speedup is most pronounced for finite-temperature simulations and suggest several practically important electron dynamics problems with potential quantum advantage.

Nearly Optimal Quantum Algorithm for Estimating Multiple Expectation Values.

Many quantum algorithms involve the evaluation of expectation values. Optimal strategies for estimating a single expectation value are known, requiring a number of state preparations that scales with the target error ϵ as O(1/ϵ). In this Letter, we address the task of estimating the expectation values of M different observables, each to within additive error ϵ, with the same 1/ϵ dependence. We describe an approach that leverages Gilyén et al.'s quantum gradient estimation algorithm to achieve O(sqrt[M]/ϵ) scaling up to logarithmic factors, regardless of the commutation properties of the M observables. We prove that this scaling is worst-case optimal in the high-precision regime if the state preparation is treated as a black box, even when the operators are mutually commuting. We highlight the flexibility of our approach by presenting several generalizations, including a strategy for accelerating the estimation of a collection of dynamic correlation functions.

Unbiasing fermionic quantum Monte Carlo with a quantum computer.

Interacting many-electron problems pose some of the greatest computational challenges in science, with essential applications across many fields. The solutions to these problems will offer accurate predictions of chemical reactivity and kinetics, and other properties of quantum systems1-4. Fermionic quantum Monte Carlo (QMC) methods5,6, which use a statistical sampling of the ground state, are among the most powerful approaches to these problems. Controlling the fermionic sign problem with constraints ensures the efficiency of QMC at the expense of potentially significant biases owing to the limited flexibility of classical computation. Here we propose an approach that combines constrained QMC with quantum computation to reduce such biases. We implement our scheme experimentally using up to 16 qubits to unbias constrained QMC calculations performed on chemical systems with as many as 120 orbitals. These experiments represent the largest chemistry simulations performed with the help of quantum computers, while achieving accuracy that is competitive with state-of-the-art classical methods without burdensome error mitigation. Compared with the popular variational quantum eigensolver7,8, our hybrid quantum-classical computational model offers an alternative path towards achieving a practical quantum advantage for the electronic structure problem without demanding exceedingly accurate preparation and measurement of the ground-state wavefunction.

Mutual Information Scaling for Tensor Network Machine Learning.

Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that the success of a given tensor network ansatz depends in part on how well it can reproduce the underlying entanglement structure of the target state, with different network designs favoring different scaling patterns. We demonstrate here how a related correlation analysis can be applied to tensor network machine learning, and explore whether classical data possess correlation scaling patterns similar to those found in quantum states which might indicate the best network to use for a given dataset. We utilize mutual information as measure of correlations in classical data, and show that it can serve as a lower-bound on the entanglement needed for a probabilistic tensor network classifier. We then develop a logistic regression algorithm to estimate the mutual information between bipartitions of data features, and verify its accuracy on a set of Gaussian distributions designed to mimic different correlation patterns. Using this algorithm, we characterize the scaling patterns in the MNIST and Tiny Images datasets, and find clear evidence of boundary-law scaling in the latter. This quantum-inspired classical analysis offers insight into the design of tensor networks which are best suited for specific learning tasks.

Time-crystalline eigenstate order on a quantum processor.

Quantum many-body systems display rich phase structure in their low-temperature equilibrium states1. However, much of nature is not in thermal equilibrium. Remarkably, it was recently predicted that out-of-equilibrium systems can exhibit novel dynamical phases2-8 that may otherwise be forbidden by equilibrium thermodynamics, a paradigmatic example being the discrete time crystal (DTC)7,9-15. Concretely, dynamical phases can be defined in periodically driven many-body-localized (MBL) systems via the concept of eigenstate order7,16,17. In eigenstate-ordered MBL 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, or from regimes in which the dynamics of a few select states can mask typical behaviour. Here we implement tunable controlled-phase (CPHASE) gates on an array of superconducting qubits to experimentally observe an MBL-DTC and demonstrate its characteristic spatiotemporal response for generic initial states7,9,10. Our work employs a time-reversal protocol to quantify the impact of external decoherence, and leverages quantum typicality to circumvent the exponential cost of densely sampling the eigenspectrum. Furthermore, we locate the phase transition out of the DTC with an experimental finite-size analysis. These results establish a scalable approach to studying non-equilibrium phases of matter on quantum processors.

Information scrambling in quantum circuits.

Interactions in quantum systems can spread initially localized quantum information into the exponentially many degrees of freedom of the entire system. Understanding this process, known as quantum scrambling, is key to resolving several open questions 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 operator spreading and operator entanglement and experimentally observe their respective signatures. We show that whereas operator spreading is captured by an efficient classical model, operator entanglement in idealized circuits 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.

What the foundations of quantum computer science teach us about chemistry.

With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in a chemical simulation with any computer. Some of these results even impact our understanding of chemistry in the real world. In this Perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on the one hand, this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution, in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics such as the ergodic hypothesis. However, we argue that this Perspective is not defeatist but rather helps shed light on the success of existing chemical models such as transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results, showing that data-augmented models are a more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can also extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.

A dual-therapy approach for the treatment of biofilm-mediated Salmonella gallbladder carriage.

Asymptomatic carriage of Salmonella Typhi continues to facilitate the transmission of typhoid fever, resulting in 14 million new infections and 136,000 fatalities each year. Asymptomatic chronic carriage of S. Typhi is facilitated by the formation of biofilms on gallstones that protect the bacteria from environmental insults and immune system clearance. Here, we identified two unique small molecules capable of both inhibiting Salmonella biofilm growth and disrupting pre-formed biofilm structures without affecting bacterial viability. In a mouse model of chronic gallbladder Salmonella carriage, treatment with either compound reduced bacterial burden in the gallbladder by 1-2 logs resulting in bacterial dissemination to peripheral organs that was associated with increased mortality. Co-administration of either compound with ciprofloxacin not only enhanced compound efficacy in the gallbladder by a further 1-1.5 logs for a total of 3-4.5 log reduction, but also prevented bacterial dissemination to peripheral organs. These data suggest a dual-therapy approach targeting both biofilm and planktonic populations can be further developed as a safe and efficient treatment of biofilm-mediated chronic S. Typhi infections.

Meridianin D analogues possess antibiofilm activity against Mycobacterium smegmatis.

The formation of bacterial biofilms significantly decreases the efficacy of antibiotic treatments. Herein, we've investigated the antibiofilm properties of the natural product meridianin D and a library of analogues against Mycobacterium smegmatis. As a result, we discovered several analogues that both inhibit and disperse M. smegmatis biofilms.

Generalized Unitary Coupled Cluster Wave functions for Quantum Computation.

We introduce a unitary coupled-cluster (UCC) ansatz termed k-UpCCGSD that is based on a family of sparse generalized doubles operators, which provides an affordable and systematically improvable unitary coupled-cluster wave function suitable for implementation on a near-term quantum computer. k-UpCCGSD employs k products of the exponential of pair coupled-cluster double excitation operators (pCCD), together with generalized single excitation operators. We compare its performance in both efficiency of implementation and accuracy with that of the generalized UCC ansatz employing the full generalized single and double excitation operators (UCCGSD), as well as with the standard ansatz employing only single and double excitations (UCCSD). k-UpCCGSD is found to show the best scaling for quantum computing applications, requiring a circuit depth of [Formula: see text], compared with [Formula: see text] for UCCGSD, and [Formula: see text] for UCCSD, where N is the number of spin orbitals and η is the number of electrons. We analyzed the accuracy of these three ansätze by making classical benchmark calculations on the ground state and the first excited state of H4 (STO-3G, 6-31G), H2O (STO-3G), and N2 (STO-3G), making additional comparisons to conventional coupled cluster methods. The results for ground states show that k-UpCCGSD offers a good trade-off between accuracy and cost, achieving chemical accuracy for lower cost of implementation on quantum computers than both UCCGSD and UCCSD. UCCGSD is also found to be more accurate than UCCSD but at a greater cost for implementation. Excited states are calculated with an orthogonally constrained variational quantum eigensolver approach. This is seen to generally yield less accurate energies than for the corresponding ground states. We demonstrate that using a specialized multideterminantal reference state constructed from classical linear response calculations allows these excited state energetics to be improved.

An interprofessional approach to prevent recurrent obstetric anal sphincter injury (OASIS): A case report.

Successful outcomes in this case are consistent with the American College of Obstetricians and Gynecologist (ACOG) guidelines for preventing OASIS. The interprofessional birth care team (IBCT) model exemplified by this case focuses on best practice in promoting a family's preferences for physiologic birth and preventing recurrent OASIS.

2-Aminobenzimidazoles as antibiofilm agents against Salmonella enterica serovar Typhimurium.

Serovars within the species Salmonella enterica are some of the most common food and water-borne pathogens worldwide. Some S. enterica serovars have shown a remarkable ability to persist both inside and outside the human body. Salmonella enterica serovar Typhi can cause chronic, asymptomatic infection of the human gallbladder. This organism's ability to survive inside the gallbladder centers around its ability to form biofilms on gallstone surfaces. Currently, chronic carriage of S. Typhi is treated by invasive methods, which are not well suited to areas where Salmonella carriage is prevalent. Herein, we report 2-aminobenzimidazoles that inhibit S. enterica serovar Typhimurium (a surrogate for S. Typhi) biofilm formation in low micromolar concentrations. Modifications to the head, tail, and linker regions of the original hit compound elucidated new, more effective analogues that inhibit S. Typhimurium biofilm formation while being non-toxic to planktonic bacterial growth.

Meridianin D Analogues Display Antibiofilm Activity against MRSA and Increase Colistin Efficacy in Gram-Negative Bacteria.

In the last 30 years, development of new classes of antibiotics has slowed, increasing the necessity for new options to treat multidrug resistant bacterial infections. Development of antibiotic adjuvants that increase the effectiveness of currently available antibiotics is a promising alternative approach to classical antibiotic development. Reports of the ability of the natural product meridianin D to modulate bacterial behavior have been rare. Herein, we describe the ability of meridianin D to inhibit biofilm formation of methicillin-resistant Staphylococcus aureus (MRSA) and to increase the potency of colistin against colistin-resistant and sensitive Gram-negative bacteria. Analogues were identified that are capable of inhibiting and dispersing MRSA biofilms and lowering the colistin MIC to below the CLSI breakpoint against Acinetobacter baumannii, Klebsiella pneumoniae, and Escherichia coli.

1,2,4-Triazolidine-3-thiones as Narrow Spectrum Antibiotics against Multidrug-Resistant Acinetobacter baumannii.

With only two new classes of antibiotics developed in the last 40 years, novel antibiotics are desperately needed to combat the growing problem of multidrug-resistant and extensively drug resistant bacteria, particularly Gram-negative bacteria. Described in this letter is the synthesis and antibiotic activity of 1,2,4-triazolidine-3-thiones as narrow spectrum antibiotics. Optimization of the 1,2,4-triazolidine-3-thione scaffold identified a small molecule with potent antibiotic activity against multiple strains of multidrug-resistant and extensively drug-resistant Acinetobacter baumannii. This small molecule also shows single dose, in vivo activity in a Galleria mellonella infection model with A. baumannii and represents a promising start in the development of a class of drugs that can target this bacterial pathogen.

1,2,4-Triazolidine-3-thiones Have Specific Activity against Acinetobacter baumannii among Common Nosocomial Pathogens.

Acinetobacter baumannii are Gram-negative bacilli that pose a constant threat to susceptible patients because of increased resistance to multiple antibiotics and persistence in the hospital environment. After genome analysis, we discovered that A. baumannii harbors genes that share homology to an enzymatic pathway that elongates long-chain fatty acids (LCFA) in fungi. Previously, 1,2,4-triazolidine-3-thiones (T-3-Ts) were shown to inhibit hyphae production in fungi, and this same LCFA elongation pathway was implicated as the possible target. Therefore, we investigated if T-3-Ts also have activity against multidrug-resistant A. baumannii. Surprisingly, all of the clinical isolates of A. baumannii that were tested have susceptibility to ECC145 and ECC188 with MIC90 values of 8.0 µg/mL. In contrast, reference strains and clinical isolates of other common nosocomial bacteria that lack the LCFA pathway also lacked susceptibility. Time-kill experiments revealed that both ECC145 and ECC188 have a bacteriostatic effect against A. baumannii. Mass spectrometry analysis suggested that exposure to T-3-Ts resulted in less LCFA production. Supplementation of media with either 0.02% w/v oleic or linoleic acid abrogated the bacteriostatic effect of the compounds, which again implicated LCFA elongation as the target. Our results suggest these molecules could be a promising start to further exploit what appears to be an important aspect of A. baumannii membrane function and integrity.