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Over the past few years, the capabilities of quantum computers have reached the stage where they can be used to pursue research with widespread technological impact. Through their research, the Q4Q team at the University of Southern California, University of North Texas, and Central Michigan University, explores how software and algorithms designed for the latest quantum computing technologies can be adapted to suit the needs of applied sciences. In a collaborative project, the Q4Q team sets out a roadmap for bringing accessible, user-friendly quantum computing into fields ranging from materials science, to pharmaceutical drug development.

Since it first emerged in the 1980s, the field of quantum computing has promised to transform the ways in which we process information. The technology is centered on the fact that quantum particles such as electrons exist in superpositions of states. Quantum mechanics also dictates that particles will only collapse into one single measurable state when observed by a user. By harnessing these unique properties, physicists discovered that batches of quantum particles can act as more advanced counterparts to conventional binary bits which only exist in one of two possible states (on or off) at a given time.

On classical computers, we write and process information in a binary form. Namely, the basic unit of information is a bit, which takes on the logical binary values 0 or 1. Similarly, quantum bits (also known as qubits) are the native information carriers on quantum computers. Much like bits, we read binary outcomes of qubits, that is 0 or 1 for each qubit.

However, in a stark contrast to bits, we can encode information on a qubit in the form of a superposition of logical values of 0 and 1. This means that we can encode much more information in a qubit than in a bit. In addition, when we have a collection of qubits, the principle of superposition leads to computational states that can encode correlations among the qubits, which are stronger than any type of correlations achieved within a collection of bits. Superposition and strong quantum correlations are, arguably, the foundations on which quantum computers rely on to provide faster processing speeds than their classical counterparts.

To realize computations, qubit states can be used in quantum logic gates, which perform operations on qubits, thus transforming the input state according to a programmed algorithm. This is a paradigm for quantum computation, analogous to conventional computers. In 1998, both qubits and quantum logic gates were realized experimentally for the first time bringing the previously-theoretical concept of quantum computing into the real world.

From this basis, researchers then began to develop new software and algorithms, specially designed for operations using qubits. At the time, however, the widespread adoption of these techniques in everyday applications still seemed a long way off. The heart of the issue lay in the errors that are inevitably introduced to quantum systems by their surrounding environments. If uncorrected, these errors can cause qubits to lose their quantum information, rendering computations completely useless. Many studies at the time aimed to develop ways to correct these errors, but the processes they came up with were invariably costly and time-consuming.

Unfortunately, the risk of introducing errors to quantum computations increases drastically as more qubits are added to a system. For over a decade after the initial experimental realization of qubits and quantum logic gates, this meant that quantum computers showed little promise in rivalling the capabilities of their conventional counterparts.

In addition, quantum computing was largely limited to specialized research labs, meaning that many research groups that could have benefited from the technology were unable to access it.

While error correction remains a hurdle, the technology has since moved beyond specialized research labs, becoming accessible to more users. This occurred for the first time in 2011, when the first quantum annealer was commercialized. With this event, feasible routes emerged towards reliable quantum processors containing thousands of qubits capable of useful computations.

Quantum annealing is an advanced technique for obtaining optimal solutions to complex mathematical problems. It is a quantum computation paradigm alternative to operating on qubits with quantum logic gates.

The availability of commercial quantum annealers spurned a new surge in interest for quantum computing, with consequent technological progress, especially fueled by industrial capitals. In 2016, this culminated in the development of a new cloud system based on quantum logic gates, which enabled owners and users of quantum computers around the world to pool their resources together, expanding the use of the devices outside of specialized research labs. Before long, the widespread use of quantum software and algorithms for specific research scenarios began to look increasingly realistic.

At the time, however, the technology still required high levels of expertise to operate. Without specific knowledge of the quantum processes involved, researchers in fields such as biology, chemistry, materials science, and drug development could not make full use of them. Further progress would be needed before the advantages of quantum computing could be widely applied outside the field of quantum mechanics itself.

Now, the Q4Q team aims to build on these previous advances using user-friendly quantum algorithms and software packages to realize quantum simulations of physical systems. Where the deeply complex properties of these systems are incredibly difficult to recreate within conventional computers, there is now hope that this could be achieved using large systems of qubits.

To recreate the technologies that could realistically become widely available in the near future, the teams experiments will incorporate noisy intermediate-scale quantum (NISQ) devices which contain relatively large numbers of qubits, and by themselves are prone to environmental errors.

In their projects, the Q4Q team identifies three particular aspects of molecules and solid materials that could be better explored through the techniques they aim to develop. The first of these concerns the band structures of solids which describe the range of energy levels that electrons can occupy within a solid, as well as the energies they are forbidden from possessing.

Secondly, they aim to describe the vibrations and electronic properties of individual molecules each of which can heavily influence their physical properties. Finally, the researchers will explore how certain aspects of quantum annealing can be exploited to realize machine-learning algorithms which automatically improve through their experience of processing data.

As they apply these techniques, the Q4Q team predicts that their findings will lead to a better knowledge of the quantum properties of both molecules and solid materials. In particular, they hope to provide better descriptions of periodic solids, whose constituent atoms are arranged in reliably repeating patterns.

Previously, researchers struggled to reproduce the wavefunctions of interacting quantum particles within these materials, which relate to the probability of finding the particles in particular positions when observed by a user. Through their techniques, the Q4Q team aims to reduce the number of qubits required to capture these wavefunctions, leading to more realistic quantum simulations of the solid materials.

Elsewhere, the Q4Q team will account for the often deeply complex quantum properties of individual molecules made up of large groups of atoms. During chemical reactions, any changes taking place within these molecules will be strongly driven by quantum processes, which are still poorly understood. By developing plugins to existing quantum software, the team hopes to accurately recreate this quantum chemistry in simulated reactions.

If they are successful in reaching these goals, the results of their work could open up many new avenues of research within a diverse array of fields especially where the effects of quantum mechanics have not yet been widely considered. In particular, they will also contribute to identifying bottlenecks of current quantum processing units, which will aid the design of better quantum computers.

Perhaps most generally, the Q4Q team hopes that their techniques will enable researchers to better understand how matter responds to external perturbations, such as lasers and other light sources.

Elsewhere, widely accessible quantum software could become immensely useful in the design of new pharmaceutical drugs, as well as new fertilizers. By ascertaining how reactions between organic and biological molecules unfold within simulations, researchers could engineer molecular structures that are specifically tailored to treating certain medical conditions.

The ability to simulate these reactions could also lead to new advances in the field of biology as a whole, where processes involving large, deeply complex molecules including proteins and nucleic acids are critical to the function of every living organism.

Finally, a better knowledge of the vibrational and electronic properties of periodic solids could transform the field of materials physics. By precisely engineering structures to display certain physical properties on macroscopic scales, researchers could tailor new materials with a vast array of desirable characteristics: including durability, advanced interaction with light, and environmental sustainability.

If the impacts of the teams proposed research goals are as transformative as they hope, researchers in many different fields of the technological endeavor could soon be working with quantum technologies.

Such a clear shift away from traditional research practices could in turn create many new jobs with required skillsets including the use of cutting-edge quantum software and algorithms. Therefore, a key element of the teams activity is to develop new strategies for training future generations of researchers. Members of the Q4Q team believe that this will present some of the clearest routes yet towards the widespread application of quantum computing in our everyday lives.

This article was authored by the Q4Q team, consisting of lead investigator Rosa Di Felice, Anna Krylov, Marco Fornari, Marco Buongiorno Nardelli, Itay Hen and Amir Kalev, in Scientia. Learn more about the team, and find the original article here.

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The future of scientific research is quantum - The Next Web

December 6, 2021• Physics 14, 172

Variational quantum algorithms could help researchers improve the performance of optical atomic clocks and of other quantum-metrology schemes.

D. Vasilyev/University of Innsbruck

D. Vasilyev/University of Innsbruck

Since it was first introduced in 1949, Ramsey interferometry has had an exciting history. The method was at the center of a series of beautiful experiments performed by Serge Haroches group that were recognized by the 2012 Nobel Prize in Physics [1, 2]. The prize was given for methods that enable the measurement and manipulation of individual quantum systems. Haroches group used individual atoms to sense the properties of photons inside an optical cavity. Building on these ideas, researchers have reported a new theoretical study that points at a promising way to push the limits of quantum sensing. Raphael Kaubruegger at the University of Innsbruck, Austria, and his colleagues employ so-called variational quantum circuits to optimize the sensitivity of an atomic sensor based on entangled atoms [4]. The result is a sensor that, with surprisingly modest quantum resources, should outperform those based on standard Ramsey interferometry.

We often think of photons as probes to study atoms, but Ramsey interferometry flips the script and uses atoms to study photons. This type of interferometry first puts an atom in a superposition of electronic energy levels and then passes the atom through an optical cavity. As a result, the quantum superposition accumulates a measurable phase shift that depends on the properties of the photons in the cavity. The experiments by Haroches group involved passing atoms through an optical cavity one at a time in order to nondestructively detect the number of photons. More photons in the cavity lead to a larger phase shift in the atomic wave function. In such experiments, each atom can be regarded as an individual entity. In other words, each atom is prepared in an uncorrelated product statea state that can be described independently of every other atoms state.

Kaubruegger and colleagues propose to go a step further by entangling 64 atoms and using them to make an even better sensor for Ramsey interferometry. They demonstrate the effectiveness of their approach by considering an optical atomic clock, in which Ramsey-interferometry measurements of the atomic ensembles phase are used to correct the clocks laser frequency (Fig. 1). Like Haroches group, the researchers manipulate a single quantum system, but one made of 64 atoms. Rather than using atoms in the product state, they propose to prepare these atoms in an entangled state, in which each atoms state cannot be fully described independently of the other atoms. They show that performing Ramsey interferometry using entangled states gives a big boost to the sensitivity of the phase sensor, beating the standard quantum limit that applies when sensing using uncorrelated atoms.

Their proposal harnesses a key innovation to prepare the entangled state. Entangled atomic sensors have been employed before, and a standard approach involves using so-called Greenberger-Horne-Zeilinger (GHZ) states. Kaubruegger and colleagues note that these states are only optimal for sensing under certain assumptions regarding prior knowledge of the phase-shift value. This limitation opened the door for the researchers to improve upon and outperform GHZ states by taking advantage of one of todays hottest concepts in quantum computing: variational quantum circuits. These circuits, which have a set of free parameters, replace the fixed quantum circuits used to implement quantum algorithms such as Shors algorithm for factoring or the Harrow-Hassidim-Lloyd algorithm for solving linear systems. Variational quantum circuits have internal parameters (such as rotation angles about certain Bloch sphere axes) that one optimizes over to perform a given task. Kaubruegger and colleagues propose to use two sets of variational quantum circuits to prepare the entangled state for sensing and to measure the parameter that they want to sense (that is, the optical phase). They call these circuits the entangling and decoding circuits, respectively (Fig. 2).

Achieving good performance with variational quantum circuits is challenging, since the parameters can be hard to optimize and one does not know ahead of time how deep of a circuit one needs, that is, how many quantum gates are required. Kaubruegger and colleagues find that excellent performance can be achieved with shallow circuits composed using the quantum resources inherently available in Ramsey interferometry and atomic-clock platforms. With only a few layers of their quantum circuits, they not only beat the standard quantum limit (which applies to measurements made using uncorrelated atoms) but also get very close to the Heisenberg limitthe ultimate limit for the sensitivity that one can achieve with a quantum system and, therefore, the ultimate limit of a quantum sensor. Here, a layer refers to the building block of the variational quantum circuit: more layers are needed to do a more comprehensive search over the Hilbert space, whereas fewer layers can only search over a smaller subspace. The fact that good performance requires only a few layers suggests that states that are beneficial to quantum metrology are relatively easy to find. This is an exciting possibility that should stimulate more investigation.

This new work is important because it brings together two different communities: the quantum sensing community and the variational quantum algorithm community. While variational quantum algorithms are getting major attention for quantum computing applications, it is rare for them to appear in an atomic experimental setting or in a sensing setting. The beautiful observation that variational algorithms could work in a realistic sensing application should inspire many experimentalists to think about optimizing their setups with variational quantum circuits, regardless of whether they involve atoms, light, spins, or superconductors. We need cross fertilization between quantum experimentalists and quantum computer scientists, and this work gives an inspiring guide for how such cross fertilization can be brought about.

Patrick Coles is a staff scientist at Los Alamos National Laboratory (LANL), New Mexico. He leads the near-term quantum computing research efforts at LANL, focusing on variational quantum algorithms and quantum machine learning. He also co-organizes LANL's quantum computing summer school. He has switched fields many times: He received his master's degree in biochemistry from the University of Cambridge, UK, as a Churchill Scholar and then did his Ph.D. in chemical engineering at the University of California, Berkeley. In contrast, his three postdocs (at Carnegie Mellon University, Pennsylvania; the National University of Singapore; and the University of Waterloo, Canada) were focused on all things quantum, including quantum foundations, quantum optics, quantum information theory, quantum cryptography, and (his current field) quantum computing.

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Pushing the Limits of Quantum Sensing with Variational Quantum Circuits - Physics

Quantum mechanics famously allows objects to be in two places at the same time. The same principle can be applied to information, represented by bits: quantum bits can be both zero and one at the same time. The field of quantum information science seeks to engineer real-world devices that can store and process quantum states of information. It is believed that computers operating according to such principles will be capable of solving problems exponentially faster than existing computers, while quantum networks have provable security guarantees. The same concepts can be applied to making more precise sensors and measurement devices. Constructing such systems is a significant challenge, because quantum effects are typically confined to the atomic scale. However, through careful engineering, several physical platforms have been identified for quantum computing, including superconducting circuits, laser-cooled atoms and ions and electron spins in semiconductors.

Research at Princeton focuses on several aspects of this problem, ranging from fundamental studies of materials and devices to quantum computer architecture and algorithms. Our research groups have close-knit collaborations across several departments including chemistry, computer science and physics and with industry.

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Quantum Engineering | Electrical and Computer Engineering

A new type of quantum computer has proven that it can reign supreme, too.

A photonic quantum computer, which harnesses particles of light, or photons, performed a calculation thats impossible for a conventional computer, researchers in China report online December 3 in Science. That milestone, known as quantum supremacy, has been met only once before, in 2019 by Googles quantum computer (SN: 10/23/19). Googles computer, however, is based on superconducting materials, not photons.

This is the first independent confirmation of Googles claim that you really can achieve quantum supremacy, says theoretical computer scientist Scott Aaronson of the University of Texas at Austin. Thats exciting.

Named Jiuzhang after an ancient Chinese mathematical text, the new quantum computer can perform a calculation in 200 seconds that would take more than half a billion years on the worlds fastest non-quantum, or classical, computer.

My first impression was, wow, says quantum physicist Fabio Sciarrino of Sapienza University of Rome.

Googles device, called Sycamore, is based on tiny quantum bits made of superconducting materials, which conduct energy without resistance. In contrast, Jiuzhang consists of a complex array of optical devices that shuttle photons around. Those devices include light sources, hundreds of beam splitters, dozens of mirrors and 100 photon detectors.

Employing a process called boson sampling, Jiuzhang generates a distribution of numbers that is exceedingly difficult for a classical computer to replicate. Heres how it works: Photons are first sent into a network of channels. There, each photon encounters a series of beam splitters, each of which sends the photon down two paths simultaneously, in whats called a quantum superposition. Paths also merge together, and the repeated splitting and merging causes the photons to interfere with one another according to quantum rules.

Finally, the number of photons in each of the networks output channels is measured at the end. When repeated many times, this process produces a distribution of numbers based on how many photons were found in each output.

If operated with large numbers of photons and many channels, the quantum computer will produce a distribution of numbers that is too complex for a classical computer to calculate. In the new experiment, up to 76 photons traversed a network of 100 channels. For one of the worlds most powerful classical computers, the Chinese supercomputer Sunway TaihuLight, predicting the results that the quantum computer would get for anything beyond about 40 photons was intractable.

While Google was the first to break the quantum supremacy barrier, the milestone is not a single-shot achievement, says study coauthor and quantum physicist Chao-Yang Lu of the University of Science and Technology of China in Hefei. Its a continuous competition between constantly improved quantum hardware and constantly improved classical simulation. After Googles quantum supremacy claim, for example, IBM proposed a type of calculation that might allow a supercomputer to perform the task Googles computer completed, at least theoretically.

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And achieving quantum supremacy doesnt necessarily indicate that the quantum computers are yet very useful, because the calculations are esoteric ones designed to be difficult for classical computers.

The result does boost the profile of photonic quantum computers, which havent always received as much attention as other technologies, says quantum physicist Christian Weedbrook, CEO of Xanadu, a Toronto-based company focused on building photonic quantum computers. Historically, photonics has been the dark horse.

One limitation of Jiuzhang, Weedbrook notes, is that it can perform only a single type of task, namely, boson sampling. In contrast, Googles quantum computer could be programmed to execute a variety of algorithms. But other types of photonic quantum computers, including Xanadus, are programmable.

Demonstrating quantum supremacy with a different type of device reveals how rapidly quantum computing is progressing, Sciarrino says. The fact that now the two different platforms are able to achieve this regime shows that the whole field is advancing in a very mature way.

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Light-based quantum computer Jiuzhang achieves quantum ...

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[52] M.D. Reed, L. DiCarlo, S.E. Nigg, L. Sun, L. Frunzio, S.M. Girvin, and R.J. Schoelkopf, 2012, Realization for three-qubit quantum error correction with superconducting circuits, Nature 482:382-385.

[53] J. Kelly, R. Barends, A.G. Fowler, A. Megrant, E. Jeffrey, T. C. White, D. Sank, et al., 2015, State preservation by repetitive error detection in a superconducting quantum circuit, Nature 519:66-69.

[54] A.D. Crcoles, E. Magesan, S.J. Srinivasan, A.W. Cross, M. Steffen, J.M. Gambetta, and J.M. Chow, 2015, Demonstration of a quantum error detection code using a square lattice of four superconducting qubits, Nature Communications 6:6979.

[55] D. Rist, S. Poletto, M.-Z. Huang, A. Bruno, V. Vesterinen, O.-P. Saira, and L. DiCarlo, 2015, Detecting bit-flip errors in a logical qubit using stabilizer measurements, Nature Communications 6:6983.

[56] N. Ofek, A. Petrenko, R. Heeres, P. Reinhold, Z. Leghtas, B. Vlastakis, Y. Liu, et al., 2016, Extending the lifetime of a quantum bit with error correction in superconducting circuits, Nature 536:441-445.

[57] IBM Q Team, 2018, IBM Q 5 Yorktown Backend Specification V1.1.0, https://ibm.biz/qiskit-yorktown; IBM Q Team, 2018, IBM Q 5 Tenerife backend specification V1.1.0, https://ibm.biz/qiskit-tenerife.

[58] Ibid.

[59] M.W. Johnson, M.H.S. Amin, S. Gildert, T. Lanting, F. Hamze, N. Dickson, R. Harris, et al., 2011, Quantum annealing with manufactured spins, Nature 473:194-198.

[60] D Wave, Technology Information, http://dwavesys.com/resources/publications.

[61] John Martinis, private conversation.

[62] W.D. Oliver and P.B. Welander, 2013, Materials in superconducting qubits, MRS Bulletin 38:816.

[63] D. Rosenberg, D.K. Kim, R. Das, D. Yost, S. Gustavsson, D. Hover, P. Krantz, et al., 2017, 3D integrated superconducting qubits, npj Quantum Information 3:42.

[64] B. Foxen, J.Y. Mutus, E. Lucero, R. Graff, A. Megrant, Y. Chen, C. Quintana, et al., 2017, Qubit Compatible Superconducting Interconnects, arXiv:1708.04270.

[65] J.M. Chow, J.M. Gambetta, A.D. Corcoles, S.T. Merkel, J.A. Smolin, C. Rigetti, S. Poletto, G.A. Keefe, M.B. Rothwell, J.R. Rozen, M.B. Ketchen, and M. Steffen, 2012, Universal quantum gate set approaching fault-tolerant thresholds with superconducing qubits, Physical Review Letters 109:060501.

[66] See, for example, J.W. Silverstone, D. Bonneau, J.L. OBrien, and M.G. Thompson, 2016, Silicon quantum photonics, IEEE Journal of Selected Topics in Quantum Electronics 22:390-402;

T. Rudolph, 2017, Why I am optimistic about the silicon-photonic route to quantum computing?, APL Photonics 2:030901.

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5 Essential Hardware Components of a Quantum Computer ...