Realistic Spin Qubit Simulations Enable Hardware Benchmarking and Mitigation of Noise

The development of practical quantum computers requires robust tools for both designing quantum algorithms and accurately simulating their performance on realistic hardware. Benoît Vermersch, Oscar Gravier, and Nathan Miscopein, alongside colleagues from Université Grenoble Alpes and Quobly, address this need with the introduction of SpinPulse, a new open-source Python package. SpinPulse uniquely simulates spin qubit computers at the pulse level, incorporating detailed modelling of non-Markovian noise which is crucial for understanding real-world device limitations. This capability allows researchers to move beyond idealised simulations and test the resilience of quantum circuits against the imperfections inherent in physical qubits, ultimately accelerating progress in hardware development and error mitigation strategies. By bridging the gap between abstract quantum algorithms and the complexities of physical implementation, SpinPulse promises to be a significant asset to the quantum computing community.

SpinPulse uniquely simulates spin qubit computers at the pulse level, incorporating detailed modelling of non-Markovian noise, crucial for understanding real-world device limitations. This capability allows researchers to test the resilience of quantum circuits against imperfections inherent in physical qubits, accelerating progress in hardware development and error mitigation strategies. By bridging the gap between abstract algorithms and physical implementation, SpinPulse promises to be a significant asset to the quantum computing community.

Spin Qubit Simulation with Classical Noise Modelling

The research team developed SpinPulse, an open-source Python package designed for simulating spin qubit-based computers at the pulse level, addressing a critical need for realistic hardware development tools. This work pioneers a methodology that models the specific physics of spin qubits, crucially incorporating classical non-Markovian noise to accurately represent experimental conditions. A circuit, initially formulated in languages like Pennylane or Cirq, undergoes conversion to the OpenQASM language using the qiskit.qasm3 module before being adapted to the model’s native gate set via a two-stage transpilation process. This initial gate transpilation is achieved using parametrized qiskit transpilation pass managers, followed by a specific decomposition of RZZ gates.

Following gate transpilation, the research introduces pulse transpilation, translating gates into precisely defined pulse sequences. The team engineered a hierarchical system comprising PulseLayer, PulseSequence, and PulseInstruction classes to manage this conversion, decomposing an Instruction Set Architecture (ISA) circuit into layers where each qubit participates in at most one gate. Each layer is assembled into PulseSequence objects detailing single- and two-qubit pulses, with PulseInstruction subclasses governing the Hamiltonian evolution at each time step. The method from_circuit within the PulseCircuit class orchestrates these steps, culminating in a complete pulse-level description of the circuit, visualised using the plot method. Numerical integration forms the final stage of the simulation pipeline, converting the PulseCircuit object back into a gate circuit to calculate the overall evolution operator, accomplished using the to_circuit method, generating a qiskit circuit for simulation with tools like qiskit_aer. This approach achieves a high degree of fidelity by approximating the SW adiabatic transformation, integrating the Heisenberg interactions described in the model, fostering the development of high-fidelity quantum circuits.

Spin Qubit Simulation with Realistic Noise Models

Scientists have developed SpinPulse, an open-source Python package designed for simulating spin qubit-based computers at the pulse level, achieving realistic modelling of quantum systems. The work introduces a method for simulating the specific physics of spin qubits, notably incorporating classical non-Markovian noise to accurately represent experimental conditions. Experiments demonstrate that circuits are first transpiled into a native gate set and then converted into a pulse sequence, which is then numerically integrated within a simulated noisy environment. This approach supports hardware development by enabling detailed simulations of native gates and circuits.

The team measured the performance of optimized concatenated CPMG sequences, finding that maximizing the number of 2nX pulses within a finite time window results in a negligible effective idle duration, simplifying pulse-level description. Furthermore, the research accounts for fluctuations in coupling constants within the Heisenberg Hamiltonian, modelling these as noisy fields with a coherence time, T∗J, analogous to qubit frequency shifts. Tests prove that gate exchange noise leads to a gate RZZ(θ + ∆θ), where ∆θ is calculated as the integral of the noisy field over time, approximately equaling the integral of εi,j(t′). Measurements confirm the relationship between gate-exchange noise and a dimensionless contrast quantity, C(t), allowing for parametrization of time traces εi,j(t) using the coherence time T∗J.

The study establishes a modular API realizing simulation steps, enabling users to independently execute and visualize each stage of the process. Hardware specifications, including the number of qubits and coupling constants, are incorporated into a HardwareSpecs class instance. The team achieved gate transpilation, converting circuits written in Qiskit language into the native gate set of the model, employing parametrized Qiskit transpilation pass managers. This breakthrough delivers a comprehensive framework for pulse-level simulations, paving the way for devising high-fidelity circuits and improved mitigation strategies for quantum computing.

Realistic Spin Qubit Simulation with Non-Markovian Noise

SpinPulse is a new open-source Python package designed for simulating spin qubit-based computers at the pulse level, offering a detailed model of spin qubit physics including classical, non-Markovian noise. The package operates by first converting a quantum circuit into a native gate set, then translating these gates into a time-dependent Hamiltonian pulse sequence incorporating simulated noise. This approach allows for realistic simulations of quantum computations, supporting the development of hardware and improved control strategies. By modelling non-Markovian noise, SpinPulse distinguishes itself from existing pulse-level simulators and provides a more accurate representation of spin qubit behaviour.

The package integrates with the quimb tensor-network library, enabling large-scale simulations and workflows encompassing transpilation, pulse-level compilation, hardware benchmarking, and noise mitigation. The authors acknowledge limitations inherent in the model’s simplification of complex physical systems, but highlight the modular design of SpinPulse, facilitating future expansion and refinement of the model. Future work may focus on extending the package’s capabilities through the modular implementation of additional features and refinements to the underlying physical model. The researchers anticipate that SpinPulse will serve as a valuable tool for the quantum computing community, aiding in the design of high-fidelity circuits and the development of effective noise mitigation techniques, representing a significant step towards bridging the gap between theoretical quantum computation and practical hardware implementation for spin qubits.