NMRCloudQ: a quantum cloud experience on a nuclear magnetic resonance quantum computer

Xin, Tao; Huang, Shilin; Lu, Sirui; Li, Keren; Luo, Zhiyong; Yin, Zhang‐qi; Li, Jun; Lu, Dawei; Long, Gui Lu; Zeng, Bei

doi:10.1016/j.scib.2017.12.022

Cited by 34 publications

(20 citation statements)

References 33 publications

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“…We have checked each pulse against a conventional full matrix exponential calculation, and the error in the fidelity of the final propagator has always been below 10 −4 . As typical experiments in this field seek a fidelity of around 0.999 [22] this error is effectively negligible, and approximate propagators provide an entirely practical approach to pulse engineering.…”

Section: Discussionmentioning

confidence: 99%

Practical pulse engineering: Gradient ascent without matrix exponentiation

Bhole

Jones

2018

Front. Phys.

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Since 2005 there has been a huge growth in the use of engineered control pulses to perform desired quantum operations in systems such as NMR quantum information processors. These approaches, which build on the original gradient ascent pulse engineering (GRAPE) algorithm, remain computationally intensive because of the need to calculate matrix exponentials for each time step in the control pulse. Here we discuss how the propagators for each time step can be approximated using the Trotter-Suzuki formula, and a further speed up achieved by avoiding unnecessary operations. The resulting procedure can give a substantial speed gain with negligible cost in propagator error, providing a more practical approach to pulse engineering. PACS numbers:Quantum information processors encode information in two-level quantum systems (qubits) and manipulate this through a series of elementary unitary transformations (quantum logic gates) [1,2]. Quantum control seeks to implement some target unitary propagator U in a quantum system with background Hamiltonian H 0 by applying some time-dependent Hamiltonian H 1 (t). The resulting operator can be written aswhere T is the Dyson time-ordering operator. To make progress beyond this formal solution it is usually necessary to replace this continuously varying Hamiltonian by a piecewise constant form, so thatand to write the time-varying portion of the Hamiltonian as a weighted sum of a set of p distinct control fieldsAny particular control pulse can then be described by the corresponding set of amplitudes, a k j , and the time step δt, here taken as fixed. The quality of a control pulse can be measured by its fidelity with the desired operation Uwhere the Hilbert-Schmidt inner product is defined by U |V = tr(U † V ), possibly normalised by the dimension of the operators [2]. The optimal control problem is then to find the set of amplitudes which maximises this fidelity, usually in the presence of practical constraints on the magnitudes of the amplitudes and the total length * Electronic address: jonathan.jones@physics.ox.ac.uk of the sequence. This is computationally challenging as the dimension of the underlying Hilbert space rises exponentially with the number of qubits to be controlled, although this difficulty can in some cases be reduced by using subsystems to simplify the calculations [3]. One recent approach [4] is to use subsystem methods to find approximate control pulses and then optimise these directly using the quantum system itself. Whatever approach is adopted, it is important to perform any computations as efficiently as possible.

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Section: Discussionmentioning

confidence: 99%

Practical pulse engineering: Gradient ascent without matrix exponentiation

Bhole

Jones

2018

Front. Phys.

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“…These developments have been limited by the universal and powerful quantum clusters. Cryptographic verification of quantum cloud computing, fault‐tolerant secure quantum computations, error‐free quantum cryptography mechanisms, cryptography primitives, and key distribution mechanisms in quantum cloud computing environment, and quantum techniques for access control in cloud computing 303–306 . In conclusion, secure and efficient quantum cloud computing environment is required to be studied in‐depth for universal QC at large scale.…”

Section: Future Directionsmentioning

confidence: 99%

Quantum computing: A taxonomy, systematic review and future directions

et al. 2021

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Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm has brought QC much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era—the next logical step being the quantum advantage whereby quantum computers solve a real‐world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. QC is a highly topical and fast‐moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on QC will be invaluable to understand the state‐of‐the‐art of this emerging field and identify open challenges for the QC community to address in the coming years. This article presents a comprehensive review of QC literature and proposes taxonomy of QC. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post‐quantum cryptography, and quantum computer hardware development captures the current state‐of‐the‐art in the respective areas. The article identifies and highlights various open challenges and promising future directions for research and innovation in QC.

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“…This means that, often times, access to these quantum machines is very costly and inconvenient, especially for academics and researchers. In some cases, despite the availability of quantum platforms from IBM, D‐Wave, Rigetti, etc, via the cloud, the access time and hardware availability are highly limited. Due to these limitations, it is necessary to investigate alternative methods of efficiently running quantum algorithms on classical hardware, namely, by simulation and/or emulation methods.…”

Section: Introductionmentioning

confidence: 99%

Scaling reconfigurable emulation of quantum algorithms at high precision and high throughput

El-Araby

Caliga

2019

Quantum Engineering

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Summary The general approach for emulating quantum algorithms on classical platforms has been through representing them as gate‐based quantum circuits. However, direct implementation of quantum circuits significantly increases the hardware resource utilization and system latency of classical emulators. In this paper, we investigate multiple implementation models alternative to conventional emulation approaches, as feasible solutions to the scalability problem in classical emulation of quantum circuits. In the first model, the quantum circuit functionality is reduced to equivalent, arithmetic (multiply‐and‐accumulate) operations and combined with two computation methods, based on lookup and dynamic generation. In the second model, a kernel operation is extracted from the quantum circuit based on its functionality, and the kernel is iterated through all input quantum states. The proposed emulation models provide space and time optimizations, significantly reducing resource utilizations and latencies and improving scalability. We use these models to develop a highly scalable hardware emulator that is based on reconfigurable technology for efficient simulations of full quantum algorithms and circuits. Our hardware implementations support single precision floating point arithmetic for improved accuracy, and contain fully pipelined designs for high throughput. We investigate quantum algorithms such as quantum Fourier transform and quantum wavelet transform and explore different hardware architectures and optimizations. Experimental results and analysis are provided for the architectures in terms of resource consumption and emulation time. The experiments are carried out on a high‐performance reconfigurable computing system and the obtained results show that the proposed emulator is feasible for running and testing a variety of quantum algorithms.

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NMRCloudQ: a quantum cloud experience on a nuclear magnetic resonance quantum computer

Cited by 34 publications

References 33 publications

Practical pulse engineering: Gradient ascent without matrix exponentiation

Practical pulse engineering: Gradient ascent without matrix exponentiation

Quantum computing: A taxonomy, systematic review and future directions

Scaling reconfigurable emulation of quantum algorithms at high precision and high throughput

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