QPAudioEraser: Quantum-Inspired Audio Unlearning

• QPAudioEraser is a quantum-inspired audio unlearning framework for privacy-preserving voice biometrics that selectively erases target speaker or accent features from a trained classifier.
• It employs a four-phase pipeline—including destructive interference, label transformation, entropy-maximizing loss, and weight mixing—to achieve complete erasure of designated class information.
• The framework’s architecture-agnostic design enables fine-tuning on the final classification layer, balancing near-zero forget accuracy with minimal degradation of retained class performance.

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QPAudioEraser is a quantum-inspired audio unlearning framework for privacy-preserving voice biometrics that selectively removes the influence of a target speaker or accent from an already-trained classifier without retraining from scratch. It is introduced in "Quantum-Inspired Audio Unlearning: Towards Privacy-Preserving Voice Biometrics" and is formulated around the operational requirements of the “right to be forgotten” in speech systems, including single-class, multi-class, sequential, and accent-level deletion requests. The framework is architecture-agnostic at the classifier level, acts on the final classification layer, and is evaluated on AudioMNIST, Speech Commands, LibriSpeech, and Speech Accent Archive, where it is reported to achieve complete erasure of target data with $0\%$ Forget Accuracy and retained-data degradation as low as $0.05\%$ in the best reported case (Pathak et al., 29 Jul 2025).

1. Formal problem and scope

The method is posed for a classifier with parameters $\theta^*$ trained on $K$ classes $C=\{c_1,\dots,c_K\}$, where classes denote speakers or accents. Given a forget class $c_F \in C$, the goal is to compute new parameters $\tilde{\theta}$ such that the post-unlearning accuracy on the forget class is driven to approximately zero while the accuracies on all retained classes remain close to those of the original model: $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$ In this formulation, “audio unlearning” or “machine unlearning” for voice biometrics means selectively erasing the influence of a target speaker or accent from a trained model while preserving the rest of the classifier.

The operational settings are broader than one-shot speaker deletion. The framework supports single-class forgetting, multi-class forgetting, sequential forgetting, and accent-level forgetting. The paper instantiates these settings as forgetting one speaker such as class 0 in AudioMNIST, Speech Commands, or LibriSpeech; forgetting multiple classes such as digits 0 and 4 or $10\%$ of all speakers; processing repeated deletion requests one by one, as in 6 of 60 AudioMNIST speakers; and removing one accent category, specifically Spanish in Speech Accent Archive.

The motivation is explicitly regulatory as well as technical. The target use case is compliance with privacy regulations such as GDPR’s right to be forgotten and India’s DPDP Act. The intended behavior after unlearning is chance-level response on forget samples, operationalized as $0\%$ observed accuracy and a uniform predictive distribution for the forget class in both single-shot and sequential settings.

The paper argues that audio unlearning differs materially from image unlearning. Speech introduces sequential and temporal structure, high-dimensional spectrogram or mel-spectrogram representations, inherent variability due to channel noise, speaking rate, prosody, and phonetic variation, and overlap of acoustic features across speakers and accents. Standard visual unlearning approaches, including Fisher-based scrubbing, gradient ascent, and synaptic dampening, are described as either failing to erase speaker or accent signatures or causing catastrophic forgetting on audio tasks.

2. Four-phase framework

QPAudioEraser is organized as a four-phase pipeline applied to an already-trained audio classifier. The method is explicitly described as architecture-agnostic and is implemented by manipulating the final classification layer.

Phase	Operation	Stated role
1	Destructive interference weight initialization	Nullify target features
2	Superposition-based label transformation	Obscure class identity
3	Uncertainty-maximizing quantum-inspired loss	Force uniform predictions on forget samples
4	Entanglement-inspired weight mixing	Retain model knowledge while blurring residual structure

The first phase applies destructive interference to the forget-class weights. In the neural-network analogue, the final-layer weights and bias associated with the forget class are phase-shifted and attenuated so that the forget-class logit is weakened before fine-tuning begins. With $0.05\%$0, the paper gives the transformed forget-class parameters as

$0.05\%$1

This initialization is intended to reduce recognition of the forget class while minimally perturbing the retained classes, and the $0.05\%$2 factor is used to avoid extreme negative logits that would destabilize subsequent optimization.

The second phase replaces one-hot targets for forget-class samples with a uniform distribution over all $0.05\%$3 classes: $0.05\%$4 The paper interprets this as a maximally mixed target in quantum terms and as a maximum-entropy target in information-theoretic terms. Retained classes preserve their ordinary one-hot labels.

The third phase introduces the quantum-inspired loss. For retained classes, the loss is ordinary cross-entropy. For forget-class samples, the objective switches to entropy maximization of the predictive distribution: $0.05\%$5 The effect is to drive $0.05\%$6 for forget-class inputs while preserving discriminability on retained classes.

The fourth phase applies an entanglement-inspired mixing matrix $0.05\%$7 to the final-layer weights. The final weights become

$0.05\%$8

with off-diagonal forget-class couplings controlled by $0.05\%$9, typically $\theta^*$0–$\theta^*$1. This mixing alters the decision geometry so that the forget-class hyperplane is no longer defined independently of the retained classes, thereby reducing residual discriminative structure for the forgotten speaker or accent.

3. Mathematical formulation and computational profile

The framework is specified for a final linear classifier with weights $\theta^*$2, bias $\theta^*$3, hidden representation $\theta^*$4, logits $\theta^*$5, and softmax outputs $\theta^*$6. Its intervention point is therefore narrow and explicit: QPAudioEraser does not alter the entire network indiscriminately, but instead acts on the terminal layer while fine-tuning the complete model for a small number of epochs.

The entropy-maximization term gives the forget distribution a specific attractor. The paper writes the forget-class gradient as

$\theta^*$7

and interprets the resulting field as pushing probabilities below $\theta^*$8 upward and probabilities above $\theta^*$9 downward. The intended fixed point is the uniform distribution, where predictions on forget samples become indistinguishable from random guessing among classes.

The weight-mixing phase has a similarly geometric reading. After applying the mixing matrix, the forget-class and retained-class logits include cross-contributions. For forget class $K$0,

$K$1

while for retained class $K$2,

$K$3

The paper’s interpretation is that decision boundaries are no longer separable in terms of a single forget-class weight vector, which reduces residual distinguishability of the forgotten class.

Algorithmically, the procedure is: initialize $K$4; apply destructive interference to the forget-class weights and bias; relabel forget samples with the uniform vector $K$5; fine-tune for $K$6 epochs on the full dataset using $K$7; then apply the mixing matrix $K$8 to the final-layer weights. The computational cost is correspondingly modest outside fine-tuning. Phases 1 and 4 require $K$9, relabeling requires $C=\{c_1,\dots,c_K\}$0, and the dominant term is Phase 3 fine-tuning at $C=\{c_1,\dots,c_K\}$1, where $C=\{c_1,\dots,c_K\}$2 is the cost of a forward-backward pass. Since $C=\{c_1,\dots,c_K\}$3 is chosen small, the method is presented as substantially cheaper than full retraining (Pathak et al., 29 Jul 2025).

4. Architectures, datasets, metrics, and results

The framework is evaluated with ResNet18, ViT-Tiny, and CNN architectures. ResNet18 and ViT-Tiny are ImageNet-pretrained and then fine-tuned on spectrograms for speaker-related tasks on AudioMNIST, Speech Commands, and LibriSpeech, while a standard CNN is used for accent classification on Speech Accent Archive. The audio inputs are preprocessed into spectrograms and then passed to the image backbones or the CNN. The text does not specify windowing, mel scaling, MFCC extraction, or padding, but indicates that standard spectrogram generation and resizing or cropping to match ImageNet input size are implied.

The evaluation uses several post-unlearning metrics. Forget Accuracy (FA) measures accuracy on the forget class and is lower-is-better, with the ideal equal to $C=\{c_1,\dots,c_K\}$4. Retain Accuracy (RA) measures accuracy on retained classes and is higher-is-better. Information Leakage (IL) is the mean softmax probability assigned to the forget class on its true samples. Privacy Erasure Rate (PER) is the percentage reduction in Forget Accuracy, with $C=\{c_1,\dots,c_K\}$5 representing complete erasure relative to the original model. False Acceptance Rate (FAR) measures how often non-forget samples are predicted as the forget class, while False Rejection Rate (FRR) measures how often forget samples are correctly rejected as non-forget classes. Erasing Retention Balance Score (ERB) is a balance metric adapted from prior work, and lower is better; with FA equal to zero, ERB becomes $C=\{c_1,\dots,c_K\}$6.

The single-class results are the paper’s strongest empirical claim. On LibriSpeech with ResNet18, the original model had FA $C=\{c_1,\dots,c_K\}$7, RA $C=\{c_1,\dots,c_K\}$8, and IL $C=\{c_1,\dots,c_K\}$9, whereas QPAudioEraser achieved FA $c_F \in C$0, FAR $c_F \in C$1, RA $c_F \in C$2, FRR $c_F \in C$3, PER $c_F \in C$4, IL $c_F \in C$5, and ERB $c_F \in C$6. On AudioMNIST with ResNet18, QPAudioEraser reached FA $c_F \in C$7, FAR $c_F \in C$8, RA $c_F \in C$9, IL $\tilde{\theta}$0, and PER $\tilde{\theta}$1. On Speech Commands with ResNet18, the reported outcome was FA $\tilde{\theta}$2, FAR $\tilde{\theta}$3, RA $\tilde{\theta}$4, PER $\tilde{\theta}$5, and IL $\tilde{\theta}$6, corresponding to only a $\tilde{\theta}$7 drop relative to the original RA of $\tilde{\theta}$8. ViT-Tiny was more sensitive, but still reached FA $\tilde{\theta}$9 on LibriSpeech, AudioMNIST, and Speech Commands with RAs of $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$0, $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$1, and $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$2, respectively.

The multi-class and sequential results are more utility-stressing. In AudioMNIST two-class forgetting of classes 0 and 4, ResNet18 moved from original FA $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$3, RA $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$4 to QPAudioEraser FA $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$5, RA $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$6, PER $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$7, IL $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$8; ViT-Tiny moved from original FA $$A_{c_F}(\tilde{\theta}) \approx 0 \quad \text{and} \quad A_{c_j}(\tilde{\theta}) \approx A_{c_j}(\theta^*) \;\; \forall c_j \neq c_F.$$9, RA $10\%$0 to FA $10\%$1, RA $10\%$2, PER $10\%$3. In sequential unlearning on AudioMNIST, after forgetting 6 of 60 speakers with ResNet18, QPAudioEraser reported RA $10\%$4, FA $10\%$5, and ERB $10\%$6. For accent unlearning on Speech Accent Archive, where Spanish was designated as the forget accent among arabic, dutch, english, french, german, italian, korean, mandarin, polish, portuguese, russian, spanish, and turkish, the original model had RA $10\%$7 and FA $10\%$8; QPAudioEraser yielded RA $10\%$9, FA $0\%$0, and ERB $0\%$1.

The baselines are informative because they expose the privacy–utility tension. Gradient Ascent and Negative Gradient often reached FA $0\%$2 but at the cost of near-collapse in RA, such as RA $0\%$3 or $0\%$4 on LibriSpeech/ResNet18 and RA $0\%$5 for accent unlearning. Fisher and Synaptic Dampening often preserved RA near the original model but failed to forget, with FA near $0\%$6. The paper therefore characterizes QPAudioEraser as the only tested method that simultaneously achieved complete erasure and high residual utility in several settings, and it specifically highlights the accent experiment as the first method to achieve accent erasure with high residual utility.

Ablation results assign distinct roles to the four phases. On AudioMNIST with ResNet18, the full method achieved FA $0\%$7, RA $0\%$8, PER $0\%$9; removing Phase 1 preserved FA $0.05\%$00 but lowered RA to $0.05\%$01; removing Phase 3 gave FA $0.05\%$02, RA $0.05\%$03; removing Phase 4 gave FA $0.05\%$04, RA $0.05\%$05. On ViT-Tiny, however, removing the mixing matrix $0.05\%$06 produced incomplete forgetting with FA $0.05\%$07, RA $0.05\%$08, and PER $0.05\%$09. Sensitivity analysis on $0.05\%$10 showed that for ResNet18, $0.05\%$11 yielded FA $0.05\%$12, RA $0.05\%$13, while $0.05\%$14 still gave FA $0.05\%$15 but reduced RA to $0.05\%$16; the paper therefore recommends tuning $0.05\%$17, $0.05\%$18, and the number of epochs $0.05\%$19 to balance privacy and utility (Pathak et al., 29 Jul 2025).

5. Privacy interpretation, guarantees, and limitations

QPAudioEraser’s privacy objective is empirical rather than formal. The method aims to remove speaker- or accent-specific information so that the model cannot reliably recognize or re-identify a given speaker or accent, to reduce membership information for the forget data by making outputs on forget samples indistinguishable from random among classes, and to support regulatory rights such as GDPR and the DPDP Act by enabling targeted erasure without full retraining.

The paper’s evidence for privacy is behavioral. It emphasizes FA $0.05\%$20, IL $0.05\%$21, predictions approximately uniform on forget data, and FAR near $0.05\%$22. These observations support the claim that the model no longer recognizes the forgotten class and rarely emits the forgotten label even for non-forget data. A common misconception is that such output behavior by itself constitutes a formal privacy guarantee. The paper explicitly rejects that interpretation: it provides no differential-privacy-style guarantee, no formal theorem bounding information leakage, and no proof that the unlearned model is equivalent to a hypothetically retrained model.

Several limitations follow directly from this evidentiary structure. The method requires access to the trained parameters and to the training data, including forget and retain subsets, because Phase 3 fine-tunes on the full dataset with modified labels. It is therefore not a purely black-box or parameter-only mechanism. The experiments are confined to benchmark closed-set classification tasks rather than open-set or verification-style biometrics, continuous speech recognition, or multimodal settings. Robustness under heavy distribution shift or adversarial evaluation is not studied. The privacy interpretation is also not stress-tested by explicit membership-inference or reconstruction attacks. A plausible implication is that QPAudioEraser should be regarded as a practical unlearning mechanism with strong empirical erasure behavior, rather than as a formally certified privacy primitive.

6. Related uses of the name in quantum-inspired audio processing

The name “QPAudioEraser” also appears in later quantum-inspired audio design discussions outside the specific unlearning framework. In "Quantum Fourier Transform Based Denoising: Unitary Filtering for Enhanced Speech Clarity," a standard single-channel denoising pipeline is reformulated by replacing FFT or STFT with a unitary Quantum Fourier Transform operator $0.05\%$23, implemented classically as dense matrix multiplication. The paper explicitly states that, for a “QPAudioEraser” system, the practical recipe is to keep the classical framing, gain estimation, inverse transform, and overlap-add structure while using a QFT-based spectral front-end. It reports mean SNR gains of approximately $0.05\%$24 dB for QFT-Wiener versus $0.05\%$25 dB for classical Wiener, and approximately $0.05\%$26 dB for QFT-spectral subtraction versus $0.05\%$27 dB for classical spectral subtraction, under matched hyperparameters such as $0.05\%$28, $0.05\%$29, frame size $0.05\%$30, $0.05\%$31 overlap, and Hamming windows (Tripathi et al., 5 Sep 2025).

A second adjacent usage appears in "Towards the Universal Defense for Query-Based Audio Adversarial Attacks." There, a “QPAudioEraser-style system” is described as a stateful front-end query monitor for ASR. The defense maintains a memory of the last $0.05\%$32 queries, extracts spectrogram-peak audio fingerprints, computes weighted cosine similarity between a new query and the memory, and flags a suspected adversarial-example generation process when the aggregate similarity exceeds a threshold. The experimentally chosen memory depth is $0.05\%$33, and the paper reports average defense performance above $0.05\%$34 detection success rate across four query-based attacks, while also analyzing adaptive random-noise and fake-query evasions (Guo et al., 2023).

These additional usages do not redefine the original unlearning framework, but they situate the term within a broader family of quantum-inspired, privacy- and security-oriented audio proposals. Across the cited literature, the recurring themes are selective removal or suppression of undesirable information, strict control of output uncertainty or similarity structure, and deployability on classical hardware without reliance on quantum devices.