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arXiv:2605.03056

Exchange-Only Silicon Based Spin Qubits: Charge Noise, PINN Optimised Pulse Sequences,and Gate-Level Fidelity

Rajdeep Rameshchandra Dwivedi, Amitoj Singh Miglani, Vishvendra Singh Poonia

quant-phSubmitted May 4, 2026• Announced May 6, 2026• 0 citations
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Abstract

Exchange-only (EO) spin qubits in silicon realise all-electrical qubit control through pairwise Heisenberg exchange interactions, making them attractive for scalable quantum computation. Their principal vulnerability is charge noise, which couples multiplicatively to the exchange coupling and degrades gate fidelity. We present a \emph{two-stage} Physics-Informed Neural Network (PINN) framework for per-gate pulse optimisation. In \textbf{Stage~I} (iterations~1--100) the PINN maximises the noise-averaged gate fidelity toward a threshold of $\Fth=0.99$; the pulse duration is held fixed at its nominal hardware value. Once the threshold is crossed, \textbf{Stage~II} (iterations~101--250) progressively compresses the total pulse time while maintaining $F\geq\Fth$ via continuous fine-tuning of the pulse-shape parameters. The cost function is a Monte-Carlo ensemble mean-squared error (MSE) averaged over $N_{\rm real}=2000$ quasi-static Gaussian noise realisations drawn fresh at every iteration. We benchmark the framework on the single-qubit gate set $\{X,Y,Z,H\}$ and the two-qubit set $\{X,Y,Z,H,\mathrm{CX}\}$ at noise levels $\sigmaJ/J\in\{1\%,5\%,10\%\}$. All single-qubit gates cross $\Fth$ within the first 100 iterations across all noise levels; Stage~II then reduces pulse durations by 20--40\% from their nominal values. The two-qubit gates follow the same two-phase behaviour, with the CX gate compressing from its nominal \SI{31}{\nano\second} to $\approx\SI{22}{\nano\second}$ at 1\% noise.

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Exchange-only (EO) spin qubits in silicon realise all-electrical qubit control through pairwise Heisenberg exchange interactions, making them attractive for scalable quantum computation. Their principal vulnerability is charge noise, which couples multiplicatively to the exchange coupling and degrades gate fidelity. We present a \emph{two-stage} Physics-Informed Neural Network (PINN) framework for per-gate pulse optimisation. In \textbf{StageI} (iterations1--100) the PINN maximises the noise-averaged gate fidelity toward a threshold of \Fth=0.99\Fth=0.99\Fth=0.99; the pulse duration is held fixed at its nominal hardware value. Once the threshold is crossed, \textbf{StageII} (iterations101--250) progressively compresses the total pulse time while maintaining F≥\FthF\geq\FthF≥\Fth via continuous fine-tuning of the pulse-shape parameters. The cost function is a Monte-Carlo ensemble mean-squared error (MSE) averaged over Nreal=2000N_{\rm real}=2000Nreal​=2000 quasi-static Gaussian noise realisations drawn fresh at every iteration. We benchmark the framework on the single-qubit gate set {X,Y,Z,H}\{X,Y,Z,H\}{X,Y,Z,H} and the two-qubit set {X,Y,Z,H,CX}\{X,Y,Z,H,\mathrm{CX}\}{X,Y,Z,H,CX} at noise levels \sigmaJ/J∈{1%,5%,10%}\sigmaJ/J\in\{1\%,5\%,10\%\}\sigmaJ/J∈{1%,5%,10%}. All single-qubit gates cross \Fth\Fth\Fth within the first 100 iterations across all noise levels; Stage~II then reduces pulse durations by 20--40% from their nominal values. The two-qubit gates follow the same two-phase behaviour, with the CX gate compressing from its nominal \SI{31}{\nano\second} to ≈\SI22\nano\second\approx\SI{22}{\nano\second}≈\SI22\nano\second at 1% noise.
Abstract In conventional exchange-only (EO) spin qubit demonstrations, quantum gates have been implemented using sequences of individually pulsed pairwise exchange interactions with only one exchange coupling active at a time. Alternatively, multiple non-commuting exchange interactions can be pulsed simultaneously, reducing circuit depths and providing protection against leakage. We demonstrate high-fidelity quantum control of an always-on exchange-only (AEON) qubit, operated using simultaneous exchange pulses in a triangular quantum dot (QD) array. We use blind randomized benchmarking to characterize the performance of the full AEON single-qubit Clifford gate set, achieving an average Clifford gate fidelity F C1 = 99.86%. Extensions of this work may enable more efficient EO two-qubit entangling gates as well as the implementation of native i -Toffoli gates in Loss-DiVincenzo single-spin qubits.
Quantum computers offer a route to outperform classical methods in tasks such as molecular simulation, motivating hybrid algorithms like the Variational Quantum Eigensolver (VQE) for near-term devices. Silicon spin qubits are a promising platform for scalable quantum computation, but their performance is limited by hardware imperfections -- most notably charge-noise-induced potential fluctuations and static miscalibration of gate-electrode voltages -- which degrade quantum gate fidelities and, ultimately, algorithmic accuracy. Here we develop a hardware-algorithm co-simulation framework for silicon quantum-dot processors that links 3D electrostatics to effective ggg-factors and exchange couplings, and propagates voltage-level noise through realistic control pulses. Using VQE for H2\mathrm{H}_2H2​ ground-state energy estimation as a circuit-level testbed, we study both static scaling/offset errors on the gate-electrode voltages and stochastic fluctuations modeled as random-telegraph noise with tunable amplitudes and switching times. At the gate level, we show that exchange-based two-qubit gates are roughly an order of magnitude more sensitive to these types of noise than ESR-driven single-qubit rotations. Quantum process tomography and Kraus-operator analysis further distinguish coherent and incoherent contributions and quantify the fraction of error that is, in principle, correctable by a compensating unitary. Embedding these noise models into the VQE circuit, we identify regimes of miscalibration strength and noise switching time compatible with chemically accurate energy estimates, and discuss how statistical post-processing based on the full distribution of noisy energy estimates could further improve accuracy.
Abstract Semiconductor spin qubits demonstrated single-qubit gates with fidelities up to 99.9 % benchmarked in the single-qubit subspace. However, tomographic characterizations reveal non-negligible crosstalk errors in a larger space. Additionally, it was long thought that the two-qubit gate performance is limited by charge noise, which couples to the qubits via the exchange interaction. Here, we show that coherent error sources such as a limited bandwidth of the control signals, diabaticity errors, microwave crosstalk, and non-linear transfer functions can equally limit the fidelity. We report a simple theoretical framework for pulse optimization that relates erroneous dynamics to spectral concentration problems and allows for the reuse of existing signal shaping methods on a larger set of gate operations. We apply this framework to common gate operations for spin qubits and show that simple pulse shaping techniques can significantly improve the performance of these gate operations in the presence of such coherent error sources. The methods presented in the paper were used to demonstrate two-qubit gate fidelities with F > 99.5 % in Xue et al (2022 Nature 601 343). We also find that single and two-qubit gates can be optimized using the same pulse shape. We use analytic derivations and numerical simulations to arrive at predicted gate fidelities greater than 99.9% with duration less than, 4 / ( Δ E z ) where Δ E z is the difference in qubit frequencies.
Abstract Quantum computers offer a route to outperform classical methods in tasks such as molecular simulation, motivating hybrid algorithms like the variational quantum eigensolver (VQE) for near-term devices. Silicon spin qubits are a promising platform for scalable quantum computation, but their performance is limited by hardware imperfections—most notably charge-noise–induced potential fluctuations and static miscalibration of gate-electrode voltages—which degrade quantum gate fidelities and, ultimately, algorithmic accuracy. Here we develop a hardware–algorithm co-simulation framework for silicon quantum-dot processors that links 3D electrostatics to effective g -factors and exchange couplings, and propagates voltage-level noise through realistic control pulses. Using VQE for H 2 ground-state energy estimation as a circuit-level testbed, we study both static scaling/offset errors on the gate-electrode voltages and stochastic fluctuations modeled as random-telegraph noise with tunable amplitudes and switching times. At the gate level, we show that exchange-based two-qubit gates are roughly an order of magnitude more sensitive to these types of noise than ESR-driven single-qubit rotations. Quantum process tomography and Kraus-operator analysis further distinguish coherent and incoherent contributions and quantify the fraction of error that is, in principle, correctable by a compensating unitary. Embedding these noise models into the VQE circuit, we identify regimes of miscalibration strength and noise switching time compatible with chemically accurate energy estimates, and discuss how statistical post-processing based on the full distribution of noisy energy estimates could further improve accuracy.
Abstract Spin qubits in semiconductor quantum dots are a promising platform for quantum computing, however, scaling to large systems is hampered by crosstalk and charge noise. Crosstalk here refers to the unwanted off-resonant rotation of idle qubits during the resonant rotation of the target qubit. For a three-qubit system with crosstalk and charge noise, it is difficult to analytically create gate protocols that produce three-qubit gates, such as the Toffoli gate, directly in a single shot instead of through the composition of two-qubit gates. Therefore, we numerically optimize a physics-informed neural network to produce theoretically robust shaped pulses that generate a Toffoli-equivalent gate. Additionally, robust π 2 X and Controlled-Z gates are also presented in this work to create a universal set of gates robust against charge noise. The robust pulses maintain an infidelity of 10−3 for average quasistatic fluctuations in the voltage of up to a few mV instead of tenths of mV for non-robust pulses.
Singlet-only always-on gapless exchange (SAGE) spin qubits are an alternative type of exchange-only (EO) qubits that encode a single qubit in the spins of four electrons located in four tunnel-coupled quantum dots. While conventional EO qubits are susceptible to local magnetic field gradients caused by local nuclear environments and ggg-factor variations, the SAGE qubit subspace is inherently protected from magnetic-gradient-induced Pauli errors by virtue of the singlet-only encoding, which is invariant under magnetic field gradients, and the always-on exchange couplings, which provide energetic leakage protection. However, the always-on operation simultaneously increases the qubit's sensitivity to charge noise. Here, starting from a Hubbard model describing the underlying electronic structure of the coupled quantum dots, we characterize the performance of SAGE qubits in the presence of 1/f1/f1/f charge noise that induces fluctuations in both the dot chemical potentials and the interdot tunnel couplings. We calculate SAGE idle coherence times and show that realistic CPMG-like pulse sequences can be used to significantly extend SAGE single-qubit coherence times for experimentally relevant charge noise strengths. We likewise study the fidelity of SAGE two-qubit gates in the presence of charge and magnetic noise and again propose a simple refocusing strategy to mitigate the noise, while increased ramp times of the entangling pulse suppress leakage into noncomputational states.