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Computational Role of Tunneling in a Programmable Quantum Annealer

Quantum tunneling is a phenomenon in which a quantum state tunnels through energy barriers above the energy of the state itself. Tunneling has been hypothesized as an advantageous physical resource for optimization. Here we present the first experimental evidence of a computational role of multiqubit quantum tunneling in the evolution of a programmable quantum annealer. We developed a theoretical model based on a NIBA Quantum Master Equation to describe the multi-qubit dissipative cotunneling effects under the complex noise characteristics of such quantum devices.We start by considering a computational primitive, the simplest non-convex optimization problem consisting of just one global and one local minimum. The quantum evolutions enable tunneling to the global minimum while the corresponding classical paths are trapped in a false minimum. In our study the non-convex potentials are realized by frustrated networks of qubit clusters with strong intra-cluster coupling. We show that the collective effect of the quantum environment is suppressed in the critical phase during the evolution where quantum tunneling decides the right path to solution. In a later stage dissipation facilitates the multiqubit cotunneling leading to the solution state. The predictions of the model accurately describe the experimental data from the D-WaveII quantum annealer at NASA Ames. In our computational primitive the temperature dependence of the probability of success in the quantum model is opposite to that of the classical paths with thermal hopping. Specially, we provide an analysis of an optimization problem with sixteen qubits,demonstrating eight qubit cotunneling that increases success probabilities. Furthermore, we report results for larger problems with up to 200 qubits that contain the primitive as subproblems.

hard problems↗

A NASA Perspective on Quantum AI, Error Correction, and Beyond

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. We discuss the prospects for quantum computing and AI, highlighting advances in algorithms, both near- and longer-term. Quantum error correction is critical to the realization of any such vision. The talk with touch on some recent exciting advanced in quantum error correction, particularly in dynamical codes. The talk will conclude with an example of how the combination of quantum computing and artificial intelligence can help probe fundamental aspect of quantum physics.

quantum optimization algorithms and sampling↗

Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy- intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware codesign in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing, and describe improved methods for simulating quantum systems of various types on high performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, computational purposes.

quantum computing↗

Quantum Dots Investigated for Solar Cells

The NASA Glenn Research Center has been investigating the synthesis of quantum dots of CdSe and CuInS2 for use in intermediate-bandgap solar cells. Using quantum dots in a solar cell to create an intermediate band will allow the harvesting of a much larger portion of the available solar spectrum. Theoretical studies predict a potential efficiency of 63.2 percent, which is approximately a factor of 2 better than any state-of-the-art devices available today. This technology is also applicable to thin-film devices--where it offers a potential four-fold increase in power-to-weight ratio over the state of the art. Intermediate-bandgap solar cells require that quantum dots be sandwiched in an intrinsic region between the photovoltaic solar cell's ordinary p- and n-type regions (see the preceding figure). The quantum dots form the intermediate band of discrete states that allow sub-bandgap energies to be absorbed. However, when the current is extracted, it is limited by the bandgap, not the individual photon energies. The energy states of the quantum dot can be controlled by controlling the size of the dot. Ironically, the ground-state energy levels are inversely proportional to the size of the quantum dots. We have prepared a variety of quantum dots using the typical organometallic synthesis routes pioneered by Ba Wendi et al., in the early 1990's. The most studied quantum dots prepared by this method have been of CdSe. To produce these dots, researchers inject a syringe of the desired organometallic precursors into heated triocytlphosphine oxide (TOPO) that has been vigorously stirred under an inert atmosphere (see the following figure). The solution immediately begins to change from colorless to yellow, then orange and red/brown, as the quantum dots increase in size. When the desired size is reached, the heat is removed from the flask. Quantum dots of different sizes can be identified by placing them under a "black light" and observing the various color differences in their fluorescence (see the photograph).

Bailey, Sheila G.↗

Dot-in-Well Quantum-Dot Infrared Photodetectors

Dot-in-well (DWELL) quantum-dot infrared photodetectors (QDIPs) [DWELL-QDIPs] are subjects of research as potentially superior alternatives to prior QDIPs. Heretofore, there has not existed a reliable method for fabricating quantum dots (QDs) having precise, repeatable dimensions. This lack has constituted an obstacle to the development of uniform, high-performance, wavelength-tailorable QDIPs and of focal-plane arrays (FPAs) of such QDIPs. However, techniques for fabricating quantum-well infrared photodetectors (QWIPs) having multiple-quantum- well (MQW) structures are now well established. In the present research on DWELL-QDIPs, the arts of fabrication of QDs and QWIPs are combined with a view toward overcoming the deficiencies of prior QDIPs. The longer-term goal is to develop focal-plane arrays of radiationhard, highly uniform arrays of QDIPs that would exhibit high performance at wavelengths from 8 to 15 m when operated at temperatures between 150 and 200 K. Increasing quantum efficiency is the key to the development of competitive QDIP-based FPAs. Quantum efficiency can be increased by increasing the density of QDs and by enhancing infrared absorption in QD-containing material. QDIPs demonstrated thus far have consisted, variously, of InAs islands on GaAs or InAs islands in InGaAs/GaAs wells. These QDIPs have exhibited low quantum efficiencies because the numbers of QD layers (and, hence, the areal densities of QDs) have been small typically five layers in each QDIP. The number of QD layers in such a device must be thus limited to prevent the aggregation of strain in the InAs/InGaAs/GaAs non-lattice- matched material system. The approach being followed in the DWELL-QDIP research is to embed In- GaAs QDs in GaAs/AlGaAs multi-quantum- well (MQW) structures (see figure). This material system can accommodate a large number of QD layers without excessive lattice-mismatch strain and the associated degradation of photodetection properties. Hence, this material system is expected to enable achievement of greater densities of QDs and correspondingly greater quantum efficiencies. The host GaAs/AlGaAs MQW structures are highly compatible with mature fabrication processes that are now used routinely in making QWIP FPAs. The hybrid InGaAs-dot/GaAs/AlGaAs-well system also offers design advantages in that the effects of variability of dot size can be partly compensated by engineering quantum-well sizes, which can be controlled precisely.

Gunapala, Sarath↗

SCB Quantum Computers Using iSWAP and 1-Qubit Rotations

Units of superconducting circuitry that exploit the concept of the single- Cooper-pair box (SCB) have been built and are undergoing testing as prototypes of logic gates that could, in principle, constitute building blocks of clocked quantum computers. These units utilize quantized charge states as the quantum information-bearing degrees of freedom. An SCB is an artificial two-level quantum system that comprises a nanoscale superconducting electrode connected to a reservoir of Cooper-pair charges via a Josephson junction. The logical quantum states of the device, .0. and .1., are implemented physically as a pair of charge-number states that differ by 2e (where e is the charge of an electron). Typically, some 109 Cooper pairs are involved. Transitions between the logical states are accomplished by tunneling of Cooper pairs through the Josephson junction. Although the two-level system contains a macroscopic number of charges, in the superconducting regime, they behave collectively, as a Bose-Einstein condensate, making possible a coherent superposition of the two logical states. This possibility makes the SCB a candidate for the physical implementation of a qubit. A set of quantum logic operations and the gates that implement them is characterized as universal if, in principle, one can form combinations of the operations in the set to implement any desired quantum computation. To be able to design a practical quantum computer, one must first specify how to decompose any valid quantum computation into a sequence of elementary 1- and 2-qubit quantum gates that are universal and that can be realized in hardware that is feasible to fabricate. Traditionally, the set of universal gates has been taken to be the set of all 1-qubit quantum gates in conjunction with the controlled-NOT (CNOT) gate, which is a 2-qubit gate. Also, it has been known for some time that the SWAP gate, which implements square root of the simple 2-qubit exchange interaction, is as computationally universal as is the CNOT operation.

Williams, Colin↗

Optimization Algorithms as Quantum Performance Benchmarks

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) have the potential to demonstrate significant run-time performance benefits over current state-of-the-art solutions. Using existing methods for characterizing classical optimization algorithms, we analyze solution quality obtained by solving Max-Cut problems using a quantum annealing device and gate-model quantum simulators and devices. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through effective visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. The framework is an enhancement to the existing open-source QED-C Application-Oriented Benchmark suite and can connect to the open-source analysis libraries. The suite can be executed on various quantum simulators and quantum hardware systems.

benchmarking↗

Quantum Technologies for UAS (QTech)

Recent advances in small Unmanned Aerial System (sUAS) technologies lower the barriers for use by both private and commercial entities. However, these advances are also likely to lead to greater vehicle densities, a more heterogenous mix of vehicles and equipment and greater levels of vehicle autonomy, which can increase the chance for communications disruptions. For the safe and secure operation of these vehicles, it is essential to have a robust communications network. This work is focused on harnessing the power of quantum technologies to enable this robust communications network by: (1) utilizing quantum optimization algorithms to design robust network with routing redundancy that can respond adaptively to dynamically changing real-time environment and disruptions, (2) utilize quantum optimization algorithms resource allocation for detection, localization, and tracking of mobile communication disruption agents and (3) utilize quantum key distribution (QKD) to execute secure key sharing in anti-jamming protocols for secure radio frequency (RF) communication. Efforts to map these quantum optimization algorithms to commercially available quantum annealers and soon to be available general-purpose gate-model quantum hardware architectures will be reviewed, and plans for testing the solutions to these algorithms through indoor sUAS flight tests will be discussed. Lastly, efforts to miniaturize and practically deploy Quantum Key Distribution (QKD) hardware, which could ultimately be used to securely exchange encryption keys, in sUAS networks will be reviewed.

Quantum Computing↗

Introduction to Quantum Computing

Quantum computing offers the potential to revolutionize high-performance computing by providing a means to solve certain computational problems asymptotically faster than any classical computer. Quantum computing has advanced recently from merely a theoretical possibility to engineered reality, including commercial entities offering early prototype quantum processors, both special-purpose quantum annealers and general-purpose gate-model processors. The media have been showcasing each new development and implicitly conveying the message that quantum-computing ubiquity is nigh. Here, we will respond to this hype and provide an overview of the exciting but still early state of the field. In this tutorial, we introduce participants to the computational models that give quantum computing its immense computational power. We examine the thought processes that programmers need to map problems to quantum computers. And we discuss hardware and algorithmic challenges that must be overcome before quantum computing becomes a component of every software developer's repertoire.

Quantum computing↗

Introduction to Quantum Computing

Quantum computing offers the potential to revolutionize high-performance computing by providing a means to solve certain computational problems asymptotically faster than any classical computer. Quantum computing has advanced recently from merely a theoretical possibility to engineered reality, including commercial entities offering early prototype quantum processors, both special-purpose quantum annealers and general-purpose gate-model processors. The media have been showcasing each new development and implicitly conveying the message that quantum-computing ubiquity is nigh. Here, we will respond to this hype and provide an overview of the exciting but still early state of the field. In this tutorial, we introduce participants to the computational models that give quantum computing its immense computational power. We examine the thought processes that programmers need to map problems to quantum computers. And we discuss hardware and algorithmic challenges that must be overcome before quantum computing becomes a component of every software developer's repertoire. (Update of 2022 slides)

Quantum computing↗

Modeling of THz Lasers Based on Intersubband Transitions in Semiconductor Quantum Wells

In semiconductor quantum well structures, the intersubband energy separation can be adjusted to the terahertz (THz) frequency range by changing the well width and material combinations. The electronic and optical properties of these nanostructures can also be controlled by an applied dc electric field. These unique features lead to a large frequency tunability of the quantum well devices. In the on-going project of modeling of the THz lasers, we investigate the possibility of using optical pumping to generate THz radiation based on intersubband transitions in semiconductor quantum wells. We choose the optical pumping because in the electric current injection it is difficult to realize population inversion in the THz frequency range due to the small intersubband separation (4-40 meV). We considered both small conduction band offset (GaAs/AlGaAs) and large band offset (InGaAs/AlAsSb) quantum well structures. For GaAs/AlGaAs quantum wells, mid-infrared C02 lasers are used as pumping sources. For InGaAs/AlAsSb quantum wells, the resonant intersubband transitions can be excited by the near-infrared diode lasers. For three- and four-subband quantum wells, we solve the pumpfield-induced nonequilibrium distribution function for each subband of the quantum well system from a set of rate equations that include both intrasubband and intersubband relaxation processes. Taking into account the coherent interactions between pump and THz (signal) waves, we calculate the optical gain for the THz field. The gain arising from population inversion and stimulated Raman processes is calculated in a unified manner. A graph shows the calculated THz gain spectra for three-subband GaAs/AlGaAs quantum wells. We see that the coherent pump and signal wave interactions contribute significantly to the gain. The pump intensity dependence of the THz gain is also studied. The calculated results are shown. Because of the optical Stark effect and pump-induced population redistribution, the maximum THz gain saturates at larger pump intensities.

Liu, Ansheng↗

Efficient Multi-Dimensional Simulation of Quantum Confinement Effects in Advanced MOS Devices

We investigate the density-gradient (DG) transport model for efficient multi-dimensional simulation of quantum confinement effects in advanced MOS devices. The formulation of the DG model is described as a quantum correction ot the classical drift-diffusion model. Quantum confinement effects are shown to be significant in sub-100nm MOSFETs. In thin-oxide MOS capacitors, quantum effects may reduce gate capacitance by 25% or more. As a result, the inclusion of quantum effects may reduce gate capacitance by 25% or more. As a result, the inclusion of quantum effects in simulations dramatically improves the match between C-V simulations and measurements for oxide thickness down to 2 nm. Significant quantum corrections also occur in the I-V characteristics of short-channel (30 to 100 nm) n-MOSFETs, with current drive reduced by up to 70%. This effect is shown to result from reduced inversion charge due to quantum confinement of electrons in the channel. Also, subthreshold slope is degraded by 15 to 20 mV/decade with the inclusion of quantum effects via the density-gradient model, and short channel effects (in particular, drain-induced barrier lowering) are noticeably increased.

Biegel, Bryan A.↗

Focal-Plane Arrays of Quantum-Dot Infrared Photodetectors

Focal-plane arrays of semiconductor quantum-dot infrared photodetectors (QDIPs) are being developed as superior alternatives to prior infrared imagers, including imagers based on HgCdTe devices and, especially, those based on quantum-well infrared photodetectors (QWIPs). HgCdTe devices and arrays thereof are difficult to fabricate and operate, and they exhibit large nonunformities and high 1/f (where f signifies frequency) noise. QWIPs are easier to fabricate and operate, can be made nearly uniform, and exhibit lower 1/f noise, but they exhibit larger dark currents, and their quantization only along the growth direction prevents them from absorbing photons at normal incidence, thereby limiting their quantum efficiencies. Like QWIPs, QDIPs offer the advantages of greater ease of operation, greater uniformity, and lower 1/f noise, but without the disadvantages: QDIPs exhibit lower dark currents, and quantum efficiencies of QDIPs are greater because the three-dimensional quantization of QDIPs is favorable to the absorption of photons at normal or oblique incidence. Moreover, QDIPs can be operated at higher temperatures (around 200 K) than are required for operation of QWIPs. The main problem in the development of QDIP imagers is to fabricate quantum dots with the requisite uniformity of size and spacing. A promising approach to be tested soon involves the use of electron-beam lithography to define the locations and sizes of quantum dots. A photoresist-covered GaAs substrate would be exposed to the beam generated by an advanced, high-precision electron beam apparatus. The exposure pattern would consist of spots typically having a diameter of 4 nm and typically spaced 20 nm apart. The exposed photoresist would be developed by either a high-contrast or a low-contrast method. In the high-contrast method, the spots would be etched in such a way as to form steep-wall holes all the way down to the substrate. The holes would be wider than the electron beam spots perhaps as wide as 15 to 20 nm, but may be sufficient to control the growth of the quantum dots. In the low-contrast method, the resist would be etched in such a way as to form dimples, the shapes of which would mimic the electron-beam density profile. Then by use of a transfer etching process that etches the substrate faster than it etches the resist, either the pattern of holes or a pattern comprising the narrow, lowest portions of the dimples would be imparted to the substrate. Having been thus patterned, the substrate would be cleaned. The resulting holes or dimples in the substrate would serve as nucleation sites for the growth of quantum dots of controlled size in the following steps. The substrate would be cleaned, then placed in a molecular-beam-epitaxy (MBE) chamber, where native oxide would be thermally desorbed and the quantum dots would be grown.

Gunapala, Sarath↗

Adding GaAs Monolayers to InAs Quantum-Dot Lasers on (001) InP

In a modification of the basic configuration of InAs quantum-dot semiconductor lasers on (001)lnP substrate, a thin layer (typically 1 to 2 monolayer thick) of GaAs is incorporated into the active region. This modification enhances laser performance: In particular, whereas it has been necessary to cool the unmodified devices to temperatures of about 80 K in order to obtain lasing at long wavelengths, the modified devices can lase at wavelengths of about 1.7 microns or more near room temperature. InAs quantum dots self-assemble, as a consequence of the lattice mismatch, during epitaxial deposition of InAs on ln0.53Ga0.47As/lnP. In the unmodified devices, the quantum dots as thus formed are typically nonuniform in size. Strainenergy relaxation in very large quantum dots can lead to poor laser performance, especially at wavelengths near 2 microns, for which large quantum dots are needed. In the modified devices, the thin layers of GaAs added to the active regions constitute potential-energy barriers that electrons can only penetrate by quantum tunneling and thus reduce the hot carrier effects. Also, the insertion of thin GaAs layer is shown to reduce the degree of nonuniformity of sizes of the quantum dots. In the fabrication of a batch of modified InAs quantum-dot lasers, the thin additional layer of GaAs is deposited as an interfacial layer in an InGaAs quantum well on (001) InP substrate. The device as described thus far is sandwiched between InGaAsPy waveguide layers, then further sandwiched between InP cladding layers, then further sandwiched between heavily Zn-doped (p-type) InGaAs contact layer.

Qiu, Yueming↗

Growing High-Quality InAs Quantum Dots for Infrared Lasers

An improved method of growing high-quality InAs quantum dots embedded in lattice-matched InGaAs quantum wells on InP substrates has been developed. InAs/InGaAs/InP quantum dot semiconductor lasers fabricated by this method are capable of operating at room temperature at wavelengths greater than or equal to 1.8 mm. Previously, InAs quantum dot lasers based on InP substrates have been reported only at low temperature of 77 K at a wavelength of 1.9 micrometers. In the present method, as in the prior method, one utilizes metalorganic vapor phase epitaxy to grow the aforementioned semiconductor structures. The development of the present method was prompted in part by the observation that when InAs quantum dots are deposited on an InGaAs layer, some of the InAs in the InGaAs layer becomes segregated from the layer and contributes to the formation of the InAs quantum dots. As a result, the quantum dots become highly nonuniform; some even exceed a critical thickness, beyond which they relax. In the present method, one covers the InGaAs layer with a thin layer of GaAs before depositing the InAs quantum dots. The purpose and effect of this thin GaAs layer is to suppress the segregation of InAs from the InGaAs layer, thereby enabling the InAs quantum dots to become nearly uniform (see figure). Devices fabricated by this method have shown near-room-temperature performance.

Qiu, Yueming↗

Bit-Serial Adder Based on Quantum Dots

A proposed integrated circuit based on quantum-dot cellular automata (QCA) would function as a bit-serial adder. This circuit would serve as a prototype building block for demonstrating the feasibility of quantum-dots computing and for the further development of increasingly complex and increasingly capable quantum-dots computing circuits. QCA-based bit-serial adders would be especially useful in that they would enable the development of highly parallel and systolic processors for implementing fast Fourier, cosine, Hartley, and wavelet transforms. The proposed circuit would complement the QCA-based circuits described in "Implementing Permutation Matrices by Use of Quantum Dots" (NPO-20801), NASA Tech Briefs, Vol. 25, No. 10 (October 2001), page 42 and "Compact Interconnection Networks Based on Quantum Dots" (NPO-20855), which appears elsewhere in this issue. Those articles described the limitations of very-large-scale-integrated (VLSI) circuitry and the major potential advantage afforded by QCA. To recapitulate: In a VLSI circuit, signal paths that are required not to interact with each other must not cross in the same plane. In contrast, for reasons too complex to describe in the limited space available for this article, suitably designed and operated QCA-based signal paths that are required not to interact with each other can nevertheless be allowed to cross each other in the same plane without adverse effect. In principle, this characteristic could be exploited to design compact, coplanar, simple (relative to VLSI) QCA-based networks to implement complex, advanced interconnection schemes. To enable a meaningful description of the proposed bit-serial adder, it is necessary to further recapitulate the description of a quantum-dot cellular automation from the first-mentioned prior article: A quantum-dot cellular automaton contains four quantum dots positioned at the corners of a square cell. The cell contains two extra mobile electrons that can tunnel (in the quantum-mechanical sense) between neighboring dots within the cell. The Coulomb repulsion between the two electrons tends to make them occupy antipodal dots in the cell. For an isolated cell, there are two energetically equivalent arrangements (denoted polarization states) of the extra electrons. The cell polarization is used to encode binary information. Because the polarization of a nonisolated cell depends on Coulomb-repulsion interactions with neighboring cells, universal logic gates and binary wires could be constructed, in principle, by arraying QCA of suitable design in suitable patterns. Again, for reasons too complex to describe here, in order to ensure accuracy and timeliness of the output of a QCA array, it is necessary to resort to an adiabatic switching scheme in which the QCA array is divided into subarrays, each controlled by a different phase of a multiphase clock signal. In this scheme, each subarray is given time to perform its computation, then its state is frozen by raising its inter-dot potential barriers and its output is fed as the input to the successor subarray. The successor subarray is kept in an unpolarized state so it does not influence the calculation of preceding subarray. Such a clocking scheme is consistent with pipeline computation in the sense that each different subarray can perform a different part of an overall computation. In other words, QCA arrays are inherently suitable for pipeline and, moreover, systolic computations. This sequential or pipeline aspect of QCA would be utilized in the proposed bit-serial adders.

Fijany, Amir↗

Quantum Computation: Entangling with the Future

Commercial applications of quantum computation have become viable due to the rapid progress of the field in the recent years. Efficient quantum algorithms are discovered to cope with the most challenging real-world problems that are too hard for classical computers. Manufactured quantum hardware has reached unprecedented precision and controllability, enabling fault-tolerant quantum computation. Here, I give a brief introduction on what principles in quantum mechanics promise its unparalleled computational power. I will discuss several important quantum algorithms that achieve exponential or polynomial speedup over any classical algorithm. Building a quantum computer is a daunting task, and I will talk about the criteria and various implementations of quantum computers. I conclude the talk with near-future commercial applications of a quantum computer.

Jiang, Zhang↗

Quantum Machine Learning

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Biswas, Rupak↗