Commercialize quantum technologies in five years

Molecular and Supramolecular Materials: From Light-Harvesting to Quantum Information Science and Technology

The past two decades have witnessed immense advances in quantum information technology (QIT), benefited by advances in physics, chemistry, biology, and materials science and engineering. It is intriguing to consider whether these diverse molecular and supramolecular structures and materials, partially inspired by quantum effects as observed in sophisticated biological systems such as light-harvesting complexes in photosynthesis and the magnetic compass of migratory birds, might play a role in future QIT. If so, how? Herein, we review materials and specify the relationship between structures and quantum properties, and we identify the challenges and limitations that have restricted the intersection of QIT and chemical materials. Examples are broken down into two categories: materials for quantum sensing where nonclassical function is observed on the molecular scale and systems where nonclassical phenomena are present due to intermolecular interactions. We discuss challenges for materials chemistry and make comparisons to related systems found in nature. We conclude that if chemical materials become relevant for QIT, they will enable quite new kinds of properties and functions.

A universal programmable Gaussian boson sampler for drug discovery

Gaussian boson sampling (GBS) has the potential to solve complex graph problems, such as clique finding, which is relevant to drug discovery tasks. However, realizing the full benefits of quantum enhancements requires large-scale quantum hardware with universal programmability. Here we have developed a time-bin-encoded GBS photonic quantum processor that is universal, programmable and software-scalable. Our processor features freely adjustable squeezing parameters and can implement arbitrary unitary operations with a programmable interferometer. Leveraging our processor, we successfully executed clique finding on a 32-node graph, achieving approximately twice the success probability compared to classical sampling. As proof of concept, we implemented a versatile quantum drug discovery platform using this GBS processor, enabling molecular docking and RNA-folding prediction tasks. Our work achieves GBS circuitry with its universal and programmable architecture, advancing GBS toward use in real-world applications.

Single-Molecule Identification of Nucleotides Using a Quantum Computer

Genomic information is essential for human health. Due to its large volume, genomic information can be potentially computed using quantum computers, which are rapidly developing. Genome analysis using quantum computers can accelerate the development of personalized medicine, innovative drugs, and novel diagnostics based on genomic information. However, genomic analysis, including nucleotide identification, has not yet been performed using quantum computers. Here, we demonstrate single-molecule identification of nucleotides using a quantum computer. We have designed a quantum gate that explains the single-molecule conductance of adenosine electronically bonded between electrodes. The quantum circuit consists of a reverse and an encoding quantum gate that can strongly distinguish adenosine among the four nucleotides. Our results are the first step toward the realization of genome analysis using quantum computers.

PIC methods in astrophysics: simulations of relativistic jets and kinetic physics in astrophysical systems

The Particle-In-Cell (PIC) method has been developed by Oscar Buneman, Charles Birdsall, Roger W. Hockney, and John Dawson in the 1950s and, with the advances of computing power, has been further developed for several fields such as astrophysical, magnetospheric as well as solar plasmas and recently also for atmospheric and laser-plasma physics. Currently more than 15 semi-public PIC codes are available which we discuss in this review. Its applications have grown extensively with increasing computing power available on high performance computing facilities around the world. These systems allow the study of various topics of astrophysical plasmas, such as magnetic reconnection, pulsars and black hole magnetosphere, non-relativistic and relativistic shocks, relativistic jets, and laser-plasma physics. We review a plethora of astrophysical phenomena such as relativistic jets, instabilities, magnetic reconnection, pulsars, as well as PIC simulations of laser-plasma physics (until 2021) emphasizing the physics involved in the simulations. Finally, we give an outlook of the future simulations of jets associated to neutron stars, black holes and their merging and discuss the future of PIC simulations in the light of petascale and exascale computing.

Toward the institutionalization of quantum computing in pharmaceutical research

Innovative pharmaceutical companies have started to explore quantum computing (QC). In this article, we provide a collective industry perspective from QC domain leaders at leading pharmaceutical companies. There are immediate nonfinancial benefits in engaging with QC, some likely financial returns in the short term in drug development, manufacturing, and supply chain, and potentially large scientific benefits in drug discovery long term. We discuss the required activities for institutionalizing QC: how to create an understanding of QC among researchers and management, which and how to deploy external resources, and how to identify the problems to be addressed with QC. If (and once) deployable, QC will likely have a similar trajectory to that of computer-aided drug design (CADD) and artificial intelligence (AI) during the 1990s and 2010s, respectively.

Approximate optimization, sampling, and spin-glass droplet discovery with tensor networks

We devise a deterministic algorithm to efficiently sample high-quality solutions of certain spin-glass systems that encode hard optimization problems. We employ tensor networks to represent the Gibbs distribution of all possible configurations. Using approximate tensor-network contractions, we are able to efficiently map the low-energy spectrum of some quasi-two-dimensional Hamiltonians. We exploit the local nature of the problems to compute spin-glass droplets geometries, which provides a new form of compression of the low-energy spectrum. It naturally extends to sampling, which otherwise, for exact contraction, is #P-complete. In particular, for one of the hardest known problem-classes devised on chimera graphs known as deceptive cluster loops and for up to 2048 spins, we find on the order of 10^{10} degenerate ground states in a single run of our algorithm, computing better solutions than have been reported on some hard instances. Our gradient-free approach could provide new insight into the structure of disordered spin-glass complexes, with ramifications both for machine learning and noisy intermediate-scale quantum devices.

Quantum computing at the frontiers of biological sciences

Computing plays a critical role in the biological sciences but faces increasing challenges of scale and complexity. Quantum computing, a computational paradigm exploiting the unique properties of quantum mechanical analogs of classical bits, seeks to address many of these challenges. We discuss the potential for quantum computing to aid in the merging of insights across different areas of biological sciences.

Quantum computing's potential for drug discovery: Early stage industry dynamics

Quantum computing (QC) is expected to revolutionize drug research by performing tasks classical supercomputers are not capable of. However, practically useful quantum computation is not yet a reality, and thus it is still unclear when and whether QC will be capable of solving real-world issues in drug discovery. By identifying the QC-related activities of pharmaceutical companies, startups, and academia in the field of drug discovery and development, we show that QC has gained traction across all of these stakeholder groups, that there is focus on developing utilities related to lead optimization and compound screening, and that there is a need for collaboration in the highly dynamic QC ecosystem.

Quantum machine learning and quantum biomimetics: A perspective

Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems. Inside quantum machine learning, quantum reinforcement learning aims at developing ‘intelligent’ quantum agents that may interact with the outer world and adapt to it, with the strategy of achieving some final goal. Another paradigm inside quantum machine learning is that of quantum autoencoders, which may allow one for employing fewer resources in a quantum device via a training process. Moreover, the field of quantum biomimetics aims at establishing analogies between biological and quantum systems, to look for previously inadvertent connections that may enable useful applications. Two recent examples are the concepts of quantum artificial life, as well as of quantum memristors. In this Perspective, we give an overview of these topics, describing the related research carried out by the scientific community.

A Full Quantum Eigensolver for Quantum Chemistry Simulations

Quantum simulation of quantum chemistry is one of the most compelling applications of quantum computing. It is of particular importance in areas ranging from materials science, biochemistry, and condensed matter physics. Here, we propose a full quantum eigensolver (FQE) algorithm to calculate the molecular ground energies and electronic structures using quantum gradient descent. Compared to existing classical-quantum hybrid methods such as variational quantum eigensolver (VQE), our method removes the classical optimizer and performs all the calculations on a quantum computer with faster convergence. The gradient descent iteration depth has a favorable complexity that is logarithmically dependent on the system size and inverse of the precision. Moreover, the FQE can be further simplified by exploiting a perturbation theory for the calculations of intermediate matrix elements and obtaining results with a precision that satisfies the requirement of chemistry application. The full quantum eigensolver can be implemented on a near-term quantum computer. With the rapid development of quantum computing hardware, the FQE provides an efficient and powerful tool to solve quantum chemistry problems.

Towards understanding two-level-systems in amorphous solids: insights from quantum circuits

Amorphous solids show surprisingly universal behaviour at low temperatures. The prevailing wisdom is that this can be explained by the existence of two-state defects within the material. The so-called standard tunneling model has become the established framework to explain these results, yet it still leaves the central question essentially unanswered-what are these two-level defects (TLS)? This question has recently taken on a new urgency with the rise of superconducting circuits in quantum computing, circuit quantum electrodynamics, magnetometry, electrometry and metrology. Superconducting circuits made from aluminium or niobium are fundamentally limited by losses due to TLS within the amorphous oxide layers encasing them. On the other hand, these circuits also provide a novel and effective method for studying the very defects which limit their operation. We can now go beyond ensemble measurements and probe individual defects-observing the quantum nature of their dynamics and studying their formation, their behaviour as a function of applied field, strain, temperature and other properties. This article reviews the plethora of recent experimental results in this area and discusses the various theoretical models which have been used to describe the observations. In doing so, it summarises the current approaches to solving this fundamentally important problem in solid-state physics.

Aging and eye tracking: in the quest for objective biomarkers

Recent applications of eye tracking for diagnosis, prognosis and follow-up of therapy in age-related neurological or psychological deficits have been reviewed. The review is focused on active aging, neurodegeneration and cognitive impairments. The potential impacts and current limitations of using characterizing features of eye movements and pupillary responses (oculometrics) as objective biomarkers in the context of aging are discussed. A closer look into the findings, especially with respect to cognitive impairments, suggests that eye tracking is an invaluable technique to study hidden aspects of aging that have not been revealed using any other noninvasive tool. Future research should involve a wider variety of oculometrics, in addition to saccadic metrics and pupillary responses, including nonlinear and combinatorial features as well as blink- and fixation-related metrics to develop biomarkers to trace age-related irregularities associated with cognitive and neural deficits.

Quantum supremacy using a programmable superconducting processor

The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor¹. 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 2⁵³ (about 10¹⁶). 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.

Remote Blind State Preparation with Weak Coherent Pulses in the Field

Quantum computing has seen tremendous progress in past years. Due to implementation complexity and cost, the future path of quantum computation is strongly believed to delegate computational tasks to powerful quantum servers on the cloud. Universal blind quantum computing (UBQC) provides the protocol for the secure delegation of arbitrary quantum computations, and it has received significant attention. However, a great challenge in UBQC is how to transmit a quantum state over a long distance securely and reliably. Here, we solve this challenge by proposing a resource-efficient remote blind qubit preparation (RBQP) protocol, with weak coherent pulses for the client to produce, using a compact and low-cost laser. We experimentally verify a key step of RBQP-quantum nondemolition measurement-in the field test over 100 km of fiber. Our experiment uses a quantum teleportation setup in the telecom wavelength and generates 1000 secure qubits with an average fidelity of (86.9±1.5)%, which exceeds the quantum no-cloning fidelity of equatorial qubit states. The results prove the feasibility of UBQC over long distances, and thus serves as a key milestone towards secure cloud quantum computing.

Quantum optical frequency up-conversion for polarisation entangled qubits: towards interconnected quantum information devices

Realising a global quantum network requires combining individual strengths of different quantum systems to perform universal tasks, notably using flying and stationary qubits. However, transferring coherently quantum information between different systems is challenging as they usually feature different properties, notably in terms of operation wavelength and wavepacket. To circumvent this problem for quantum photonics systems, we demonstrate a polarisation-preserving quantum frequency conversion device in which telecom wavelength photons are converted to the near infrared, at which a variety of quantum memories operate. Our device is essentially free of noise, which we demonstrate through near perfect single photon state transfer tomography and observation of high-fidelity entanglement after conversion. In addition, our guided-wave setup is robust, compact, and easily adaptable to other wavelengths. This approach therefore represents a major building block towards advantageously connecting quantum information systems based on light and matter.

Quantum Chemistry in the Age of Quantum Computing

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.

Ultrastrong coupling probed by Coherent Population Transfer

Light-matter interaction, and the understanding of the fundamental physics behind, is the scenario of emerging quantum technologies. Solid state devices allow the exploration of new regimes where ultrastrong coupling strengths are comparable to subsystem energies, and new exotic phenomena like quantum phase transitions and ground-state entanglement occur. While experiments so far provided only spectroscopic evidence of ultrastrong coupling, we propose a new dynamical protocol for detecting virtual photon pairs in the dressed eigenstates. This is the fingerprint of the violated conservation of the number of excitations, which heralds the symmetry broken by ultrastrong coupling. We show that in flux-based superconducting architectures this photon production channel can be coherently amplified by Stimulated Raman Adiabatic Passage, providing a unique tool for an unambiguous dynamical detection of ultrastrong coupling in present day hardware. This protocol could be a benchmark for control of the dynamics of ultrastrong coupling architectures, in view of applications to quantum information and microwave quantum photonics.

From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz

The next few years will be exciting as prototype universal quantum processors emerge, enabling the implementation of a wider variety of algorithms. Of particular interest are quantum heuristics, which require experimentation on quantum hardware for their evaluation and which have the potential to significantly expand the breadth of applications for which quantum computers have an established advantage. A leading candidate is Farhi et al.’s quantum approximate optimization algorithm, which alternates between applying a cost function based Hamiltonian and a mixing Hamiltonian. Here, we extend this framework to allow alternation between more general families of operators. The essence of this extension, the quantum alternating operator ansatz, is the consideration of general parameterized families of unitaries rather than only those corresponding to the time evolution under a fixed local Hamiltonian for a time specified by the parameter. This ansatz supports the representation of a larger, and potentially more useful, set of states than the original formulation, with potential long-term impact on a broad array of application areas. For cases that call for mixing only within a desired subspace, refocusing on unitaries rather than Hamiltonians enables more efficiently implementable mixers than was possible in the original framework. Such mixers are particularly useful for optimization problems with hard constraints that must always be satisfied, defining a feasible subspace, and soft constraints whose violation we wish to minimize. More efficient implementation enables earlier experimental exploration of an alternating operator approach, in the spirit of the quantum approximate optimization algorithm, to a wide variety of approximate optimization, exact optimization, and sampling problems. In addition to introducing the quantum alternating operator ansatz, we lay out design criteria for mixing operators, detail mappings for eight problems, and provide a compendium with brief descriptions of mappings for a diverse array of problems.

Control not quantum gate on the states of photon orbital angular momentum

The possibility of effectively creating quantum gates based on orbital angular momentum photon qubits using a Kerr nonlinear medium in a cavity is studied. It is shown how a quantum C-NOT gate can be produced with such qubits through four-wave mixing process. A theory of this gate operation is constructed using an input–output formalism. Parametric matching conditions are obtained for effective gate operation.

Decentralized Global Optimization Based on a Growth Transform Dynamical System Model

Conservation principles, such as conservation of charge, energy, or mass, provide a natural way to couple and constrain spatially separated variables. In this paper, we propose a dynamical system model that exploits these constraints for solving nonconvex and discrete global optimization problems. Unlike the traditional simulated annealing or quantum annealing-based global optimization techniques, the proposed method optimizes a target objective function by continuously evolving a driver functional over a conservation manifold, using a generalized variant of growth transformations. As a result, the driver functional asymptotically converges toward a Dirac-delta function that is centered at the global optimum of the target objective function. In this paper, we provide an outline of the proof of convergence for the dynamical system model and investigate different properties of the model using a benchmark nonlinear optimization problem. Also, we demonstrate how a discrete variant of the proposed dynamical system can be used for implementing decentralized optimization algorithms, where an ensemble of spatially separated entities (for example, biological cells or simple computational units) can collectively implement specific functions, such as winner-take-all and ranking, by exchanging signals only with its immediate substrate or environment. The proposed dynamical system model could potentially be used to implement continuous-time optimizers, annealers, and neural networks.

A Comparison of the Bravyi-Kitaev and Jordan-Wigner Transformations for the Quantum Simulation of Quantum Chemistry

The ability to perform classically intractable electronic structure calculations is often cited as one of the principal applications of quantum computing. A great deal of theoretical algorithmic development has been performed in support of this goal. Most techniques require a scheme for mapping electronic states and operations to states of and operations upon qubits. The two most commonly used techniques for this are the Jordan-Wigner transformation and the Bravyi-Kitaev transformation. However, comparisons of these schemes have previously been limited to individual small molecules. In this paper, we discuss resource implications for the use of the Bravyi-Kitaev mapping scheme, specifically with regard to the number of quantum gates required for simulation. We consider both small systems, which may be simulatable on near-future quantum devices, and systems sufficiently large for classical simulation to be intractable. We use 86 molecular systems to demonstrate that the use of the Bravyi-Kitaev transformation is typically at least approximately as efficient as the canonical Jordan-Wigner transformation and results in substantially reduced gate count estimates when performing limited circuit optimizations.

Bravyi-Kitaev Superfast simulation of electronic structure on a quantum computer

Present quantum computers often work with distinguishable qubits as their computational units. In order to simulate indistinguishable fermionic particles, it is first required to map the fermionic state to the state of the qubits. The Bravyi-Kitaev Superfast (BKSF) algorithm can be used to accomplish this mapping. The BKSF mapping has connections to quantum error correction and opens the door to new ways of understanding fermionic simulation in a topological context. Here, we present the first detailed exposition of the BKSF algorithm for molecular simulation. We provide the BKSF transformed qubit operators and report on our implementation of the BKSF fermion-to-qubits transform in OpenFermion. In this initial study of a hydrogen molecule we have compared BKSF, Jordan-Wigner, and Bravyi-Kitaev transforms under the Trotter approximation. The gate count to implement BKSF is lower than Jordan-Wigner but higher than Bravyi-Kitaev. We considered different orderings of the exponentiated terms and found lower Trotter errors than the previously reported for Jordan-Wigner and Bravyi-Kitaev algorithms. These results open the door to the further study of the BKSF algorithm for quantum simulation.

Surface loop-gap resonators for electron spin resonance at W-band

Electron spin resonance (ESR) is a spectroscopic method used to detect paramagnetic materials, reveal their structure, and also image their position in a sample. ESR makes use of a large static magnetic field to split the energy levels of the electron magnetic moment of the paramagnetic species. A strong microwave magnetic field is applied to excite the spins, and subsequently the ESR system detects their faint microwave signal response. The sensitivity of an ESR system is greatly influenced by the magnitude of the static field and the properties of the microwave resonator used to detect the spin signal. In general terms, the higher the static field (microwave frequency) and the smaller the resonator, the more sensitive the system will be. Previous work aimed at high-sensitivity ESR was focused on the development and testing of very small resonators operating at moderate magnetic fields in the range of ∼0.1-1.2 T (maximum frequency of ∼35 GHz). Here, we describe the design, construction, and testing of recently developed miniature surface loop-gap resonators used in ESR and operating at a much higher frequency of ∼95 GHz (W-band, corresponding to a field of ∼3.4 T). Such resonators can greatly enhance the sensitivity of ESR and also improve the resulting spectral resolution due to the higher static field employed. A detailed description of the resonator's design and coupling mechanism, as well as the supporting probe head, is provided. We also discuss the production method of the resonators and probe head and, in the end, provide preliminary experimental results that show the setup's high spin sensitivity and compare it to theoretical predictions.

Entanglement and Wigner Function Negativity of Multimode Non-Gaussian States

Non-Gaussian operations are essential to exploit the quantum advantages in optical continuous variable quantum information protocols. We focus on mode-selective photon addition and subtraction as experimentally promising processes to create multimode non-Gaussian states. Our approach is based on correlation functions, as is common in quantum statistical mechanics and condensed matter physics, mixed with quantum optics tools. We formulate an analytical expression of the Wigner function after the subtraction or addition of a single photon, for arbitrarily many modes. It is used to demonstrate entanglement properties specific to non-Gaussian states and also leads to a practical and elegant condition for Wigner function negativity. Finally, we analyze the potential of photon addition and subtraction for an experimentally generated multimode Gaussian state.