Variational quantum compiling for three-qubit gates design in quantum dots

Abstract

Semiconductor quantum dots offer a promising platform for controlling spin qubits and realizing quantum logic gates, essential for scalable quantum computing. In this work, we utilize a variational quantum compiling algorithm to design efficient three-qubit gates using a time-independent Hamiltonian composed of only physical interaction terms. The resulting gates, including the Toffoli and Fredkin gates, demonstrate high fidelity and robustness against both coherent and incoherent noise sources, including charge and nuclear spin noise. This method is applicable to a wide range of physical systems, such as superconducting qubits and trapped ions, paving the way for more resilient and universal quantum computing architectures.

11 pages, 5 figures

References in corpus (59)

• Quantum Computing in the NISQ era and beyond
• Quantum Computing
• Quantum algorithm for solving linear systems of equations
• Variational Quantum Algorithms
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Spins in few-electron quantum dots
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum computational chemistry
• Quantum Circuit Learning
• A >99.9%-fidelity quantum-dot spin qubit with coherence limited by charge noise
• Scalable Quantum Simulation of Molecular Energies
• A programmable two-qubit quantum processor in silicon
• A simple formula for the average gate fidelity of a quantum dynamical operation
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Realization of Three-Qubit Quantum Error Correction with Superconducting Circuits
• Quantum CNOT Gate for Spins in Silicon
• Computing with spin qubits at the surface code error threshold
• Fast universal quantum control above the fault-tolerance threshold in silicon
• Robust determination of molecular spectra on a quantum processor
• Charge noise and spin noise in a semiconductor quantum device
• Prospects for Spin-Based Quantum Computing
• Chopped random-basis quantum optimization
• Implementation of a Toffoli Gate with Superconducting Circuits
• Fidelity benchmarks for two-qubit gates in silicon
• Quantum-assisted quantum compiling
• Realization of the quantum Toffoli gate with trapped ions
• Exploring entanglement and optimization within the Hamiltonian Variational Ansatz
• Effect of barren plateaus on gradient-free optimization
• Silicon qubit fidelities approaching incoherent noise limits via pulse engineering
• Semiconductor Quantum Computation
• Optimizing Variational Quantum Algorithms using Pontryagin's Minimum Principle
• Universal logic with encoded spin qubits in silicon
• The impact of classical control electronics on qubit fidelity
• Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing
• Deep Reinforcement Learning for Quantum Gate Control
• From pulses to circuits and back again: A quantum optimal control perspective on variational quantum algorithms
• Noise-resistant control for a spin qubit array
• Nonperturbative master equation solution of central spin dephasing dynamics
• Non-adiabatic molecular quantum dynamics with quantum computers
• Universal Quantum Computation through Control of Spin-Orbit Coupling
• Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach
• Quantum gate learning in engineered qubit networks: Toffoli gate with always-on interactions
• Theory of anisotropic exchange in laterally coupled quantum dots
• Two-qubit logic with anisotropic exchange in a fin field-effect transistor
• Implementation of the SMART protocol for global qubit control in silicon
• Digital quantum simulation of molecular dynamics and control
• Fabrication process and failure analysis for robust quantum dots in silicon
• Robust quantum gates using smooth pulses and physics-informed neural networks
• Supervised learning of time-independent Hamiltonians for gate design
• Spin decoherence in a two-qubit CPHASE gate: the critical role of tunneling noise
• Optimizing Counterdiabaticity by Variational Quantum Circuits
• Two-qubit CZ gates robust against charge noise in silicon while compensating for crosstalk using neural network
• Time-Optimal Quantum Driving by Variational Circuit Learning
• Benchmarking hybrid digitized-counterdiabatic quantum optimization
• Neural-network-designed three-qubit gates robust against charge noise and crosstalk in silicon
• Dropout is all you need: robust two-qubit gate with reinforcement learning