A space-efficient quantum computer simulator suitable for high-speed FPGA implementation

Frank, Michael P.; Oniciuc, Liviu; Meyer-Baese, Uwe; Chiorescu, Irinel

doi:10.1117/12.817924

Cited by 11 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quantum computers simulators available today can only simulate a small number of circuits, i.e., very limited offered capacity [196]. This is because the simulation of a quantum computer on a classical computer is a computationally hard problem.…”

Section: Challenges In Enabling Quantum and Qml-assisted Communicationsmentioning

confidence: 99%

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

et al. 2019

View full text Add to dashboard Cite

The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users' service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum-and QML-assisted B5G networks are identified and discussed.

show abstract

Section: Challenges In Enabling Quantum and Qml-assisted Communicationsmentioning

confidence: 99%

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Problems with exponential growth of space and time complexity during simulation of larger quantum circuits were also addressed by Franka et al [13]. They designed and conducted empirical complexity measurements of a working software prototype of a quantum computer simulator avoiding excessive space requirements.…”

Section: Existing Workmentioning

confidence: 99%

An FPGA-based real quantum computer emulator

Pilch

Długopolski

2018

J Comput Electron

View full text Add to dashboard Cite

While we cannot efficiently emulate quantum algorithms on classical architectures, we can move the weight of complexity from time to hardware resources. This paper describes a proposition of a universal and scalable quantum computer emulator, in which the FPGA hardware emulates the behavior of a real quantum system, capable of running quantum algorithms while maintaining their natural time complexity. The article also shows the proposed quantum emulator architecture, exposing a standard programming interface, and working results of an implementation of an exemplary quantum algorithm.

show abstract

“…However, the number of quantum bits and quantum gates that can be simulated are still limited. In [3], [4], a space efficient quantum computer simulator is proposed. This is based on the fact that BQP is included in PSPACE, where BQP is the class of decision problems that can be solved by quantum Turing machines in polynomial time with bounded error, and PSPACE is the class of decision problems that can be solved by classical Turing machines in polynomial space.…”

Section: Introductionmentioning

confidence: 99%

A Fast Quantum Computer Simulator Based on Register Reordering

Nakanishi

Matsuyama

Yokoo

2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARY Quantum computer simulators play an important role when we evaluate quantum algorithms. Quantum computation can be regarded as parallel computation in some sense, and thus, it is suitable to implement a simulator on hardware that can process a lot of operations in parallel. In this paper, we propose a hardware quantum computer simulator. The proposed simulator is based on the register reordering method that shifts and swaps registers containing probability amplitudes so that the probability amplitudes of target basis states can be quickly selected. This reduces the number of large multiplexers and improves clock frequency. We implement the simulator on an FPGA. Experiments show that the proposed simulator has scalability in terms of the number of quantum bits, and can simulate quantum algorithms faster than software simulators.

show abstract

A space-efficient quantum computer simulator suitable for high-speed FPGA implementation

Cited by 11 publications

References 12 publications

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

An FPGA-based real quantum computer emulator

A Fast Quantum Computer Simulator Based on Register Reordering

Contact Info

Product

Resources

About