Archive for the ‘Quantum Computer’ Category

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

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 Engineering | Electrical and Computer Engineering

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

Light-based quantum computer Jiuzhang achieves quantum …

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 ...

5 Essential Hardware Components of a Quantum Computer …

[47] R. Barends, J. Kelly, A. Megrant, A. Veitia, D. Sank, E. Jeffrey, T.C. White, et al., 2014, Superconducting quantum circuits at the surface code threshold for fault tolerance, Nature 508(7497):500.

[48] L. DiCarlo, J.M. Chow, J.M. Gambetta, L.S. Bishop, B.R. Johnson, D.I. Schuster, J. Majer, A. Blais, L. Frunzio, S.M. Girvin, and R.J. Schoelkopf, 2009, Demonstration of two-qubit algorithms with a superconducting quantum processor, Nature 460:240-244.

[49] E. Lucero, R. Barends, Y. Chen, J. Kelly, M. Mariantoni, A. Megrant, P. OMalley, et al., 2012, Computing prime factors with a Josephson phase qubit quantum processor, Nature Physics 8:719-723.

[50] P.J.J. OMalley, R. Babbush, I.D. Kivlichan, J. Romero, J.R. McClean, R. Barends, J. Kelly, et al., 2016, Scalable quantum simulation of molecular energies, Physical Review X 6:031007.

[51] N.K. Langford, R. Sagastizabal, M. Kounalakis, C. Dickel, A. Bruno, F. Luthi, D.J. Thoen, A. Endo, and L. DiCarlo, 2017, Experimentally simulating the dynamics of quantum light and matter at deep-strong coupling, Nature Communications 8:1715.

[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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US blocks export of quantum computing tech to Chinese organizations – CNET

An ion chamber houses the brains of a Honeywell quantum computer.

The Commerce Department on Wednesdaybarred US firms from exporting quantum computing technology to eight Chinese companies and labs to try to keep the country from decrypting sensitive US communications and developing new military technology.

"Global trade and commerce should support peace, prosperity, and good-paying jobs, not national security risks," Commerce Secretary Gina Raimondo said in a statement.

Though still technologically immature, quantum computers eventually could crack conventional encryption. The US government also is leading an active program to develop post-quantum cryptography, but communications that are intercepted today could be exposed if quantum computers become powerful enough.

Quantum computers take advantage of the physics of the ultrasmall to perform a radically different type of computation than conventional computer chips in today's phones, laptops and supercomputers. But today they work only at small scales, are prone to errors that derail calculations and are finicky enough to require ultracold conditions.

The department also pointed to quantum computing military risks involving "counter-stealth and counter-submarine applications." It detailed in theFederal Registerthe Chinese organizations added to its entities list involving export controls.

Another market where quantum computers also have potential is simulating molecular structures that could lead to new materials. Military technology has benefited immensely from materials science in the past, so quantum computing could lead to new breakthroughs.

To capitalize on these breakthroughs, many US companies are investing billions of dollars in developing quantum computers. That includes Google, IBM, Microsoft, Honeywell, IonQ, Rigetti, D-Wave and Intel. Google Chief Executive Sundar Pichai said in November thatChinese researchers are tied with Google in the race to develop quantum computing technology.

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US blocks export of quantum computing tech to Chinese organizations - CNET