Quantum computers promise revolutionary capabilities, from breaking today's strongest cryptographic systems to simulating complex molecules for advanced materials science. However, as with any emerging technology, securing quantum computing infrastructure is critical. While much of the focus has historically been on algorithmic or theoretical vulnerabilities, side-channel attacks present a new and often overlooked threat.
In this blog post, we’ll explore the landscape of power side-channels in quantum computers, examine several novel attack types uncovered by recent research, and discuss how engineers and researchers can detect and mitigate these risks. We’ll provide beginner to advanced perspectives, real-world examples, and even actionable code samples to reinforce understanding.
Side-channel attacks (SCA) exploit unintended information leaked by a system’s physical implementation rather than weaknesses in the algorithm itself. In classical computing, common side-channels include:
Examples:
In cryptography, side-channel resilience is as important as algorithmic strength.
Quantum computing uses quantum bits (qubits) that exist in superpositions and are manipulated with quantum gates, often realized through precise control pulses (microwave, optical, etc.). Quantum mechanics underpins their operation, but at the hardware level, implementations are vulnerable.
Key Security Differences:
Power side-channels in quantum devices stem from the physical properties of qubit manipulation. Many commercial devices (such as those accessible via IBM Quantum Experience or AWS Braket) reveal some level of control pulse information to users, often for debugging or optimization.
Potential for Side-Channels:
A notable risk: attackers leveraging cloud-based access may not even require physical proximity.
The 2023 research highlighted in this arXiv paper enumerates five power side-channel attacks exploiting control pulse data in cloud quantum computers:
Attack vector:
By carefully observing the duration of control pulses applied to the qubits, an attacker can infer which quantum gates are being used.
Why it works:
X gate vs. Hadamard gate).Implications:
Attack vector:
Different quantum operations may utilize pulses at different frequencies (especially for multi-qubit gates or addressing specific qubits).
Why it works:
Implications:
Attack vector:
Monitoring the amplitude of pulses gives away information about single- vs. multi-qubit interactions, intensity of operations, or error correction.
Why it works:
Implications:
Attack vector:
Due to physical proximity, pulses for one qubit may "bleed" over, affecting others (crosstalk).
Why it works:
Implications:
Attack vector:
Leveraging the detailed control pulse timing data offered by cloud quantum providers for performance monitoring; attackers can mine this data for operational insights.
Why it works:
Implications:
A 2025 report by a University of Toronto Engineering team [1] exposed multi-dimensional (not just power, but timing, amplitude, phase, etc.) side channels that can persist in real-world quantum sources. These hidden channels can arise from device manufacturing imperfections, environmental factors, or quantum crosstalk.
Key Highlights:
The full security of quantum hardware requires holistic physical-layer vigilance.
Researchers using a publicly available quantum device to run proprietary algorithms may have their circuits revealed via side-channel analysis of the pulse data logs—potentially allowing an attacker to steal new quantum algorithms before public release.
In QKD, secure key establishment relies on the principles of quantum mechanics. Side-channels—such as power fluctuations or photon emission anomalies—could leak enough information for an eavesdropper to reconstruct portions of the secret key.
A state-level attacker with access to advanced sensing equipment could even observe EM and power signatures from a distance, gaining “multi-modal” insight into highly classified quantum computations.
Post-quantum cryptography (PQC) is designed to be resistant to quantum algorithmic attacks, but if physical implementations leak data via side-channels, PQC becomes moot.
Secure-IC’s blog highlights how overlooked side-channels can impact even leading-edge cryptography.
Best practice is defense-in-depth: combine hardware, software, and operational controls.
Detecting side-channels often requires first gathering and analyzing raw pulse data. Fortunately, with cloud quantum devices, pulse data can be accessed via APIs, and basic scanning/analysis can be done with open-source tools.
Suppose a cloud quantum API exposes a /pulse_logs endpoint:
curl -s -X GET \
-H "Authorization: Bearer $TOKEN" \
"https://api.quantumprovider.com/v1/devices/$DEVICEID/pulse_logs?job_id=$JOBID" \
> pulse_data.json
Assuming the data contains a sequence like:
[
{ "timestamp": 1683752500, "qubit": 0, "width": 40, "amplitude": 0.92, "freq": 5.3 },
{ "timestamp": 1683752504, "qubit": 0, "width": 24, "amplitude": 0.92, "freq": 5.0 }
]
Here’s how to analyze the pulse widths and frequencies:
import json
with open('pulse_data.json') as f:
pulses = json.load(f)
# Analyze pulse widths for qubit 0
pulse_widths = [p['width'] for p in pulses if p['qubit'] == 0]
print("Unique pulse widths for qubit 0:", set(pulse_widths))
# Frequency usage histogram
from collections import Counter
freqs = [p['freq'] for p in pulses if p['qubit'] == 0]
print("Frequency counts:", dict(Counter(freqs)))
import matplotlib.pyplot as plt
widths = [p['width'] for p in pulses]
amps = [p['amplitude'] for p in pulses]
plt.scatter(widths, amps, alpha=0.5)
plt.title("Pulse Width vs Amplitude")
plt.xlabel("Width (ns)")
plt.ylabel("Amplitude (arb. units)")
plt.show()
With more sophisticated models, you can cluster pulses by width/amplitude/frequency, attempting to reverse-engineer likely gate sequences or user programs!
Quantum computing’s promise must not blind us to new and subtle security risks. As this review showed, power side-channel attacks—from pulse width analysis to cloud-exposed timing data—can be real and present dangers. Both quantum hardware engineers and security professionals should build side-channel resistance into every layer: hardware, software, and cloud interface.
By proactively detecting, analyzing, and mitigating these risks, we can ensure that the quantum future is robust and secure.
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Disclaimer: This blog post is for educational purposes only and does not endorse or encourage unauthorized access to any quantum computing systems.
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