Quantum Compiling by Deep Reinforcement Learning
- URL: http://arxiv.org/abs/2105.15048v1
- Date: Mon, 31 May 2021 15:32:15 GMT
- Title: Quantum Compiling by Deep Reinforcement Learning
- Authors: Lorenzo Moro, Matteo G. A. Paris, Marcello Restelli, Enrico Prati
- Abstract summary: The architecture of circuital quantum computers requires layers devoted to compiling high-level quantum algorithms into lower-level circuits of quantum gates.
The general problem of quantum compiling is to approximate any unitary transformation that describes the quantum computation, as a sequence of elements selected from a finite base of universal quantum gates.
We exploit the deep reinforcement learning method as an alternative strategy, which has a significantly different trade-off between search time and exploitation time.
- Score: 30.189226681406392
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The architecture of circuital quantum computers requires computing layers
devoted to compiling high-level quantum algorithms into lower-level circuits of
quantum gates. The general problem of quantum compiling is to approximate any
unitary transformation that describes the quantum computation, as a sequence of
elements selected from a finite base of universal quantum gates. The existence
of an approximating sequence of one qubit quantum gates is guaranteed by the
Solovay-Kitaev theorem, which implies sub-optimal algorithms to establish it
explicitly. Since a unitary transformation may require significantly different
gate sequences, depending on the base considered, such a problem is of great
complexity and does not admit an efficient approximating algorithm. Therefore,
traditional approaches are time-consuming tasks, unsuitable to be employed
during quantum computation. We exploit the deep reinforcement learning method
as an alternative strategy, which has a significantly different trade-off
between search time and exploitation time. Deep reinforcement learning allows
creating single-qubit operations in real time, after an arbitrary long training
period during which a strategy for creating sequences to approximate unitary
operators is built. The deep reinforcement learning based compiling method
allows for fast computation times, which could in principle be exploited for
real-time quantum compiling.
Related papers
- Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - Hamiltonian Encoding for Quantum Approximate Time Evolution of Kinetic
Energy Operator [2.184775414778289]
The time evolution operator plays a crucial role in the precise computation of chemical experiments on quantum computers.
We have proposed a new encoding method, namely quantum approximate time evolution (QATE) for the quantum implementation of the kinetic energy operator.
arXiv Detail & Related papers (2023-10-05T05:25:38Z) - Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
The anticipated applications of quantum computers span across science and industry.
We present a survey of several potential application areas of quantum algorithms.
We outline the challenges and opportunities in each area in an "end-to-end" fashion.
arXiv Detail & Related papers (2023-10-04T17:53:55Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Parametric Synthesis of Computational Circuits for Complex Quantum
Algorithms [0.0]
The purpose of our quantum synthesizer is enabling users to implement quantum algorithms using higher-level commands.
The proposed approach for implementing quantum algorithms has a potential application in the field of machine learning.
arXiv Detail & Related papers (2022-09-20T06:25:47Z) - Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle.
We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm.
We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
arXiv Detail & Related papers (2022-05-26T20:32:36Z) - Quantum amplitude damping for solving homogeneous linear differential
equations: A noninterferometric algorithm [0.0]
This work proposes a novel approach by using the Quantum Amplitude Damping operation as a resource, in order to construct an efficient quantum algorithm for solving homogeneous LDEs.
We show that such an open quantum system-inspired circuitry allows for constructing the real exponential terms in the solution in a non-interferometric.
arXiv Detail & Related papers (2021-11-10T11:25:32Z) - Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network [64.1951227380212]
We propose that quantum circuits can be modeled as queuing networks.
Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
arXiv Detail & Related papers (2021-06-26T10:55:52Z) - Boundaries of quantum supremacy via random circuit sampling [69.16452769334367]
Google's recent quantum supremacy experiment heralded a transition point where quantum computing performed a computational task, random circuit sampling.
We examine the constraints of the observed quantum runtime advantage in a larger number of qubits and gates.
arXiv Detail & Related papers (2020-05-05T20:11:53Z) - Topological Quantum Compiling with Reinforcement Learning [7.741584909637626]
We introduce an efficient algorithm that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set.
Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.
arXiv Detail & Related papers (2020-04-09T18:00:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.