Exploring Quantum Computer Side-Channel Attacks

# Exploration of Quantum Computer Power Side-Channels: Attacks and Mitigations

Quantum computing brings the promise of solving problems intractable for classical computers—cryptography, chemistry simulations, optimization, and more. However, the power of quantum hardware also introduces new cybersecurity risks. Among the most critical and emerging: **side-channel attacks** targeting the physical implementation of quantum systems. This technical blog post offers a comprehensive exploration of quantum computer side-channel vulnerabilities, referencing recent academic advancements, and integrating code samples and practical mitigation techniques.

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- [Introduction to Side-Channel Attacks](#introduction-to-side-channel-attacks)
- [Quantum Computing and Cryptography](#quantum-computing-and-cryptography)
- [What Are Power Side-Channels?](#what-are-power-side-channels)
- [Five New Quantum Power Side-Channel Attacks](#five-new-quantum-power-side-channel-attacks)
- [Evaluating Quantum Side-Channel Threats](#evaluating-quantum-side-channel-threats)
- [Example: Scanning and Parsing Output for Side-Channel Leakage](#example-scanning-and-parsing-output-for-side-channel-leakage)
- [Countermeasures: Mitigating Quantum Side-Channel Attacks](#countermeasures-mitigating-quantum-side-channel-attacks)
- [The Future of Quantum Security](#the-future-of-quantum-security)
- [References](#references)

---

## Introduction to Side-Channel Attacks

A **side-channel attack** doesn’t target cryptographic algorithms themselves; instead, it exploits indirect information leaked by the physical implementation of a system. Examples include timing, electromagnetic emissions, acoustic signals, or **power consumption** variations during computation.

In classical systems, side-channel attacks have allowed adversaries to recover cryptographic keys from smartcards, IoT devices, and secure chips by analyzing physical signals associated with cryptographic operations.

### Common Types of Side-Channels

- **Power Analysis** — Measuring instantaneous power consumption to infer processed data.
- **Timing Attacks** — Exploiting differences in execution time for different inputs.
- **Electromagnetic (EM) Attacks** — Capturing EM emissions during computations.
- **Acoustic and Thermal Attacks** — Using sounds or heat profiles.

**Quantum computers**—though fundamentally different—are not immune. Their hardware can leak information via side-channels, which become more critical as cloud-based quantum computers become available to adversaries.

---

## Quantum Computing and Cryptography

**Quantum attacks** threaten both classical asymmetric (e.g., RSA, ECC) and symmetric cryptography. For example:

- **Shor’s Algorithm** can factor large integers exponentially faster than classical methods, breaking RSA.
- **Grover’s Algorithm** offers a quadratic speedup for brute-force attacks on symmetric cryptography.
- **Quantum algorithms** can target hashing, digital signature verification, and other tasks.

_Reference: [theses.hal.science](https://theses.hal.science/tel-05050176v1/file/147210_SAAB_CHARTOUNI_2025_archivage.pdf)._

**Post-quantum cryptography** aims to create algorithms secure against quantum attacks. Yet, these don’t always address side-channel threats, which may compromise even mathematically secure schemes by exploiting their physical implementation.

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## What Are Power Side-Channels?

**Power side-channels** are attacks that analyze how the power consumption of a device varies with internal operations, potentially leaking sensitive information such as cryptographic keys, algorithmic behavior, or even detailed circuit states.

### In the Classical World

Consider **Differential Power Analysis (DPA)**: An attacker records power traces while a device processes different inputs, then applies statistical methods to correlate key-dependent operations to observable power variations.

```python
# Simplified example: Python code to parse power traces
import numpy as np
from scipy.stats import pearsonr

def find_leakage(trace_files, hypothetical_values):
    correlations = []
    for key_guess in hypothetical_values:
        traces = [np.loadtxt(f) for f in trace_files]
        hypothesis = [model(input_data, key_guess) for input_data in inputs]
        correlation = pearsonr(traces, hypothesis)[0]
        correlations.append((key_guess, correlation))
    return max(correlations, key=lambda x: abs(x[1]))

In the Quantum World

Quantum control hardware and readout electronics—oscillators, amplifiers, and converters—consume power in patterns that correlate with quantum gate operations, qubit control, and measurement activities. These patterns can leak:

Qubit states
Quantum circuit structure
Control pulse sequences

This presents a unique attack surface because quantum computers are often accessed remotely, with cloud providers exposing hardware to arbitrarily crafted circuits by untrusted users.

Five New Quantum Power Side-Channel Attacks

Drawing from Liang et al., 2023 (arxiv.org/abs/2304.03315), researchers have identified five new quantum computer power side-channel attacks. These methods leverage control pulse information—the precise sequences of electromagnetic pulses used to drive quantum gates.

1. Control Pulse Timing Attack

By submitting programmatically timed sequences, an attacker observes subtle variations in hardware power consumption, allowing them to deduce which quantum gates are being executed and their sequence.

2. Amplitude Modulation Leakage

Varying the amplitude of control pulses (whether to represent logical '0' or '1' or gate parameters) causes corresponding changes in the power signature. Monitoring these signatures can reveal the values being processed or the qubit states.

3. Inter-Gate Distinguishability Attack

Different gate types (e.g., X, Y, Z, H, CNOT) require different control pulse shapes. By analyzing the power profile, attackers can distinguish, for example, single-qubit versus multi-qubit operations.

4. Cross-User Inference Attack

On cloud-based quantum platforms, execution is multiplexed across different users. By correlating their own job’s timing and power usage with global patterns, an adversary can infer computations done by other users—potentially extracting information about "neighboring" processes.

5. State Preparation and Measurement (SPAM) Channel Leak

The power required for state initialization and measurement can differ based on both the intended operation and the underlying physical state of the hardware, providing yet another channel for inference.

Table: Attack Types and Leaked Information

Attack Type	Leaked Information
Control Pulse Timing	Gate sequence, gate timing
Amplitude Modulation Leakage	Input values, parameters, qubit states
Inter-Gate Distinguishability	Operation types, circuit structure
Cross-User Inference	Other users’ job characteristics
SPAM Channel Leak	Qubit initial and measurement states

Evaluating Quantum Side-Channel Threats

Liang et al. (2023) evaluated these attacks against real-world data collected from cloud-based quantum computers (e.g., IBM Q, Rigetti), focusing on control pulse information exposed to end-users.

Key observations:

Substantial Information Leakage: Even limited access to power traces or timing information can compromise the privacy of quantum computations.
Statistical Correlation: Standard power analysis methods from classical security research can often be adapted to analyze quantum control data.

Real-World Example: Inferring Quantum Circuit Structure

Suppose a user is allowed to download control pulse information for their own computations. An attacker submits "probe" circuits and records the corresponding control data. By cross-referencing this with known hardware characteristics, they infer:

Which quantum gates belong to which users
How many qubits are involved in computations
Measurement and state preparation times (potentially leaking computational outcomes)

Cloud Quantum Service: Example Data Download

For IBM Quantum Experience:

# Download experiment results including pulse data (fictional command)
ibm_quantum_client get-experiment --id <experiment_id> --include-pulse-data

Python Example: Parsing Pulse Information

Let’s say we have a set of JSON logs from a quantum provider describing pulse sequences:

import json

def parse_control_pulses(logfile):
    with open(logfile, 'r') as f:
        data = json.load(f)
    pulses = data['pulse_sequence']
    for pulse in pulses:
        print(
            f"Pulse at t={pulse['start_time']}ns, "
            f"duration={pulse['duration']}ns, "
            f"amplitude={pulse['amplitude']}, "
            f"channel={pulse['channel']}"
        )

An attacker could then build statistical models over these parameters (amplitude, timing, channeling) to infer the circuit being executed—even without knowing the original code.

Example: Scanning and Parsing Output for Side-Channel Leakage

Side-channel risks can be demonstrated by using simple tools (Bash, Python) to scan logs or monitor publicly exposed quantum system information.

Bash Example: Scanning for Amplitude Leaks in Pulse Logs

Imagine you have a directory with many pulse log files, and you suspect that certain high amplitudes correspond to sensitive quantum operations.

#!/bin/bash
for logfile in ./pulse_logs/*.json; do
    # Extract amplitude values, print those above a leak threshold (e.g., 0.8)
    jq '.pulse_sequence[] | select(.amplitude > 0.8) | {time: .start_time, amp: .amplitude}' "$logfile"
done

Explanation:
This loops over all JSON pulse logs. For each, it uses jq to print only those pulses whose amplitude exceeds 0.8—a value an adversary has determined is associated with key bits or privileged algorithm operations.

Extended Python Example: Side-Channel Correlation Analysis

import glob
import json
import numpy as np

def extract_amplitudes(directory):
    amplitudes = []
    for file in glob.glob(f"{directory}/*.json"):
        with open(file) as f:
            data = json.load(f)
            amplitudes.extend([
                pulse["amplitude"]
                for pulse in data.get("pulse_sequence", [])
            ])
    return np.array(amplitudes)

# Analyze the distribution of amplitudes to find clusters = potential leakage
amps = extract_amplitudes("./pulse_logs")
import matplotlib.pyplot as plt

plt.hist(amps, bins=50)
plt.title("Control Pulse Amplitudes")
plt.xlabel("Amplitude")
plt.ylabel("Count")
plt.show()

A sharp clustering around certain amplitudes or repeated outliers can be a sign of non-random, key-dependent leakage.

Countermeasures: Mitigating Quantum Side-Channel Attacks

While quantum cryptographic protocols are designed to be mathematically secure, their physical implementations must be hardened against side-channel attacks. This parallels lessons learned in classical cryptography, but with new challenges unique to quantum hardware and cloud environments.

1. Software Countermeasures

Blinding and Randomization

Randomizing the order and timing of quantum gate operations
Adding deliberate “dummy” pulses to obscure true circuit behavior

Masking

Randomly altering (masking) the logical states so that the relationship between control pulses and key bits is obfuscated

Example: Pseudocode for Randomized Circuit Padding

from qiskit import QuantumCircuit
import random

def pad_with_random_gates(circuit, n_qubits, pad_prob=0.2):
    for q in range(n_qubits):
        if random.random() < pad_prob:
            # Insert a random gate
            g = random.choice([circuit.x, circuit.y, circuit.z, circuit.h])
            g(q)
    return circuit

qc = QuantumCircuit(5)
qc = pad_with_random_gates(qc, 5)

Explanation:
This Python function pads a quantum circuit with extra gates at random, making timing and power profiles less directly correlated to the true computation.

2. Hardware Countermeasures

Constant Power Draw Design: Engineering microwave and voltage supplies to maintain a uniform power usage profile independent of logical operations.
Noise Insertion: Injecting non-informative electrical noise to mask real operation signatures.
Pulse Obfuscation: Varying pulse properties slightly even for identical logical operations, reducing distinguishability.

“Hardware-based countermeasures... can also include the addition of noise generators, constant current sources, and electromagnetic shielding.”
– Secure-IC Interview

3. Secure Job Scheduling and Isolation

Restricting low-level hardware access for users on multi-tenant quantum clouds.
Time-multiplexing and decoherence to break cross-user inference.
Monitoring for unusual patterns that resemble probing/side-channel analysis attempts.

4. Access Limitation

Preventing direct access to detailed pulse data or hardware logs by end-users, or providing obfuscated, noise-added data only.
Rate-limiting or throttling jobs that appear to be attempting exhaustive side-channel probing.

5. Post-Quantum Security Audits

Regularly testing systems against published and new side-channel techniques.
Adopting a red-teaming approach for quantum service providers.

The Future of Quantum Security

The arms race between cryptography and side-channel attack research will move rapidly into the quantum realm. As more organizations rely on cloud-based quantum computing, side-channel resistance must become a first-class concern, not an afterthought.

Quantum-resilient doesn’t just mean mathematically secure against quantum algorithms; it means physically engineered to resist the specific leaks of quantum hardware. New standards and best practices are needed, with cross-disciplinary teams of quantum physicists, engineers, and cybersecurity experts.

Action Points for Security Practitioners:

Demand transparency from quantum cloud providers regarding defenses against side-channel attacks.
Audit any access to low-level hardware logs, power, or electromagnetic data on quantum systems.
Incorporate quantum-aware side-channel analysis into vulnerability assessments for cryptographic implementations.

References

Liang, X., et al. (2023). Exploring Power Side Channels on Quantum Computers. arxiv.org/abs/2304.03315
Chartouni, S.A.A.B. (2025). Quantum and Side-Channel Attacks. theses.hal.science
Secure-IC. Mitigating Side-Channel Attacks in Post Quantum. secure-ic.com blog
IBM Quantum Pulse Documentation
Qiskit Pulse