Optimisation of Pulse Waveforms for Qubit Gates using Deep Learning

• URL: http://arxiv.org/abs/2408.02376v1
• Date: Mon, 5 Aug 2024 11:02:59 GMT
• Title: Optimisation of Pulse Waveforms for Qubit Gates using Deep Learning
• Authors: Zachary Fillingham, Hossein Nevisi, Shirin Dora,
• Abstract summary: We propose a novel method using Deep Neural Networks (DNNs) to optimise the parameters of pulse waveforms used for manipulating qubit states. High fidelity quantum simulations are crucial for scaling up current quantum computers.
• Score: 0.5325390073522079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel method using Deep Neural Networks (DNNs) to optimise the parameters of pulse waveforms used for manipulating qubit states, resulting in high fidelity implementation of qubit gates. High fidelity quantum simulations are crucial for scaling up current quantum computers. The proposed approach uses DNNs to model the functional relationship between amplitudes of pulse waveforms used in scheduling and the corresponding fidelities. The DNNs are trained using a dataset of amplitude and corresponding fidelities obtained through quantum simulations in Qiskit. A two-stage approach is used with the trained DNNs to obtain amplitudes that yield the highest fidelity. The proposed method is evaluated by estimating the amplitude for pulse scheduling of single (Hadamard and Pauli-X) and two qubit gates (CNOT). The results clearly indicate that the method can achieve high fidelity implementations of single-qubit gates with fidelities of 0.999976 and 0.999923 for Hadamard and Pauli-X gates, respectively. For the CNOT gate, the best fidelity obtained is 0.695313. This can be attributed to the effects of entanglement and the need for the phase parameter to be accounted for within the predictive model.

Related papers

• Development of Neural Network-Based Optimal Control Pulse Generator for Quantum Logic Gates Using the GRAPE Algorithm in NMR Quantum Computer [0.0]
  We introduce a neural network to generate the optimal control pulses for general single-qubit quantum logic gates. By utilizing a neural network, we can efficiently implement any single-qubit quantum logic gates within a reasonable time scale.
  arXiv  Detail & Related papers  (2024-12-08T08:31:55Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• A generic and robust quantum agent inspired by deep meta-reinforcement learning [4.881040823544883]
  We develop a new training algorithm inspired by the deep meta-reinforcement learning (deep meta-RL) The trained neural network is adaptive and robust. Our algorithm can also automatically adjust the number of pulses required to generate the target gate.
  arXiv  Detail & Related papers  (2024-06-11T13:04:30Z)
• Reinforcement learning pulses for transmon qubit entangling gates [0.0]
  We utilize a continuous-control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits. We demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates.
  arXiv  Detail & Related papers  (2023-11-07T03:19:19Z)
• Quantum state preparation of gravitational waves [0.0]
  We show a quantum circuit capable of efficiently encoding analytical approximations to gravitational wave signal waveforms. gate cost of the proposed method is considered and compared to a state preparation routine for arbitrary amplitudes. We demonstrate through a quantum simulation, that is limited to 28 qubits, the encoding of a second post-Newtonian inspiral waveform with a fidelity compared to the desired state of 0.995.
  arXiv  Detail & Related papers  (2023-06-19T17:17:59Z)
• Hamiltonian Switching Control of Noisy Bipartite Qubit Systems [7.094462708097975]
  We develop a Hamiltonian switching ansatz for bipartite control inspired by the Quantum Approximate Optimization Algorithm (QAOA) We demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols.
  arXiv  Detail & Related papers  (2023-04-11T20:12:57Z)
• Variational waveguide QED simulators [58.720142291102135]
  Waveguide QED simulators are made by quantum emitters interacting with one-dimensional photonic band-gap materials. Here, we demonstrate how these interactions can be a resource to develop more efficient variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-02-03T18:55:08Z)
• Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks [68.8204255655161]
  We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
  arXiv  Detail & Related papers  (2022-06-29T14:34:00Z)
• Extensible circuit-QED architecture via amplitude- and frequency-variable microwaves [52.77024349608834]
  We introduce a circuit-QED architecture combining fixed-frequency qubits and microwave-driven couplers. Drive parameters appear as tunable knobs enabling selective two-qubit coupling and coherent-error suppression.
  arXiv  Detail & Related papers  (2022-04-17T22:49:56Z)
• Accurate methods for the analysis of strong-drive effects in parametric gates [94.70553167084388]
  We show how to efficiently extract gate parameters using exact numerics and a perturbative analytical approach. We identify optimal regimes of operation for different types of gates including $i$SWAP, controlled-Z, and CNOT.
  arXiv  Detail & Related papers  (2021-07-06T02:02:54Z)
• Composably secure data processing for Gaussian-modulated continuous variable quantum key distribution [58.720142291102135]
  Continuous-variable quantum key distribution (QKD) employs the quadratures of a bosonic mode to establish a secret key between two remote parties. We consider a protocol with homodyne detection in the general setting of composable finite-size security. In particular, we analyze the high signal-to-noise regime which requires the use of high-rate (non-binary) low-density parity check codes.
  arXiv  Detail & Related papers  (2021-03-30T18:02:55Z)

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.