Hybrid Programming for Near-Term Quantum Computing Systems

Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. Here, we propose 3 "application-motivated" circuit classes for benchmarking: deep (relevant for state preparation in the variational quantum eigensolver algorithm), shallow (inspired by IQP-type circuits that might be useful for near-term quantum machine learning), and square (inspired by the quantum volume benchmark). We quantify the performance of a quantum computing system in running circuits from these classes using several figures of merit, all of which require exponential classical computing resources and a polynomial number of classical samples (bitstrings) from the system. We study how performance varies with the compilation strategy used and the device on which the circuit is run. Using systems made available by IBM Quantum, we examine their performance, showing that noise-aware compilation strategies may be beneficial, and that device connectivity and noise levels play a crucial role in the performance of the system according to our benchmarks.

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Optimal teleportation via noisy quantum channels without additional qubit resources

npj Quantum Information ◽

10.1038/s41534-021-00426-x ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Dong-Gil Im ◽

Chung-Hyun Lee ◽

Yosep Kim ◽

Hyunchul Nha ◽

M. S. Kim ◽

...

Keyword(s):

Quantum Computing ◽

Quantum Teleportation ◽

Quantum Communication ◽

Quantum Noise ◽

Single Copy ◽

Entangled State ◽

Quantum Network ◽

Quantum Channels ◽

Maximally Entangled State ◽

Near Term

AbstractQuantum teleportation exemplifies how the transmission of quantum information starkly differs from that of classical information and serves as a key protocol for quantum communication and quantum computing. While an ideal teleportation protocol requires noiseless quantum channels to share a pure maximally entangled state, the reality is that shared entanglement is often severely degraded due to various decoherence mechanisms. Although the quantum noise induced by the decoherence is indeed a major obstacle to realizing a near-term quantum network or processor with a limited number of qubits, the methodologies considered thus far to address this issue are resource-intensive. Here, we demonstrate a protocol that allows optimal quantum teleportation via noisy quantum channels without additional qubit resources. By analyzing teleportation in the framework of generalized quantum measurement, we optimize the teleportation protocol for noisy quantum channels. In particular, we experimentally demonstrate that our protocol enables to teleport an unknown qubit even via a single copy of an entangled state under strong decoherence that would otherwise preclude any quantum operation. Our work provides a useful methodology for practically coping with decoherence with a limited number of qubits and paves the way for realizing noisy intermediate-scale quantum computing and quantum communication.

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Quantum Computing and the Architectural, Engineering and Construction (AEC) Industry: Trends, Challenges, Near-Term Practical Opportunities and Future Outlook

10.32591/coas.e-book.002 ◽

2021 ◽

Author(s):

Oliver Barima ◽

Keyword(s):

Quantum Computing ◽

Future Research ◽

Communication Tools ◽

Architectural Engineering ◽

Complex Problems ◽

Potential Impact ◽

Near Term ◽

Communication Methods ◽

Industry Trends ◽

Aec Industry

Digital computing and allied communication tools have been phenomenal, but they are fast reaching their technical limits and also their capacity to solve certain extant complex problems. Quantum computing has now emerged with a touted potential to deal with aspects of the latter complex problems and also significantly transform extant computing and communication methods. Yet, little studies exist in the Architectural, Engineering and Construction (AEC) industry related literature on quantum computing to boost awareness and also explore its prospective impact on the AEC industry. This study discusses the underpinning basic concepts, technologies and trends in the quantum computing domain to build awareness to boost Architecture, Engineering and Construction (AEC) industry capacity on the topic. The research dissects the challenges in this novel area. And also examines the near-term practical opportunities and potential impact of quantum computing on the AEC industry. The study concludes with suggestions for practice and future research.

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What is Quantum Computing and How it Works

10.47363/jmsmr/2020(2)104 ◽

2020 ◽

pp. 1-5

Author(s):

Bahman Zohuri ◽

◽

Farhang Mossavar Rahmani ◽

Keyword(s):

Artificial Intelligence ◽

Quantum Computing ◽

Quantum Computer ◽

Time Frame ◽

Basic Unit ◽

Next Generation ◽

Intel Corporation ◽

Encrypt Data ◽

Near Term ◽

Incremental Step

Companies such as Intel as a pioneer in chip design for computing are pushing the edge of computing from its present Classical Computing generation to the next generation of Quantum Computing. Along the side of Intel corporation, companies such as IBM, Microsoft, and Google are also playing in this domain. The race is on to build the world’s first meaningful quantum computer—one that can deliver the technology’s long-promised ability to help scientists do things like develop miraculous new materials, encrypt data with near-perfect security and accurately predict how Earth’s climate will change. Such a machine is likely more than a decade away, but IBM, Microsoft, Google, Intel, and other tech heavyweights breathlessly tout each tiny, incremental step along the way. Most of these milestones involve packing more quantum bits, or qubits—the basic unit of information in a quantum computer—onto a processor chip ever. But the path to quantum computing involves far more than wrangling subatomic particles. Such computing capabilities are opening a new area into dealing with the massive sheer volume of structured and unstructured data in the form of Big Data, is an excellent augmentation to Artificial Intelligence (AI) and would allow it to thrive to its next generation of Super Artificial Intelligence (SAI) in the near-term time frame.

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Circuit models for the co-simulation of superconducting quantum computing systems

10.23919/date51398.2021.9474086 ◽

2021 ◽

Author(s):

Rohith Acharya ◽

Fahd A. Mohiyaddin ◽

Anton Potocnik ◽

Kristiaan De Greve ◽

Bogdan Govoreanu ◽

...

Keyword(s):

Quantum Computing ◽

Computing Systems

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Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

Communications in Computer and Information Science - Cloud Computing and Services Science ◽

10.1007/978-3-030-72369-9_2 ◽

2021 ◽

pp. 25-58

Author(s):

Johanna Barzen ◽

Frank Leymann ◽

Michael Falkenthal ◽

Daniel Vietz ◽

Benjamin Weder ◽

...

Keyword(s):

Quantum Computing ◽

Near Term

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Reconfigurable FPGA-Based Channelization Using Polyphase Filter Banks for Quantum Computing Systems

Applied Reconfigurable Computing. Architectures, Tools, and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-78890-6_49 ◽

2018 ◽

pp. 615-626

Author(s):

Johannes Pfau ◽

Shalina Percy Delicia Figuli ◽

Steffen Bähr ◽

Jürgen Becker

Keyword(s):

Quantum Computing ◽

Filter Banks ◽

Computing Systems ◽

Polyphase Filter

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Application of Quantum Computing to Biochemical Systems: A Look to the Future

Frontiers in Chemistry ◽

10.3389/fchem.2020.587143 ◽

2020 ◽

Vol 8 ◽

Author(s):

Hai-Ping Cheng ◽

Erik Deumens ◽

James K. Freericks ◽

Chenglong Li ◽

Beverly A. Sanders

Keyword(s):

Quantum Computing ◽

Physical System ◽

Quantum Computer ◽

Quantum Algorithms ◽

Active Regions ◽

Quantum Computers ◽

The Future ◽

Biochemical Systems ◽

Near Term ◽

Different Levels

Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.

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A Full Quantum Eigensolver for Quantum Chemistry Simulations

Research ◽

10.34133/2020/1486935 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Shijie Wei ◽

Hang Li ◽

GuiLu Long

Keyword(s):

Quantum Computing ◽

Quantum Chemistry ◽

Gradient Descent ◽

Materials Science ◽

Hybrid Methods ◽

Quantum Computer ◽

Rapid Development ◽

System Size ◽

Near Term ◽

Full Quantum

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.

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