Epistemic Noise as Computational Resource

In the paper's words

The scaling of quantum information processors is currently bottlenecked by the “isolationist” paradigm, which treats environmental decoherence as an irreversible entropy increase that must be suppressed. This manuscript proposes a radical engineering shift grounded in the Superdeterministic interpretation of quantum mechanics, where “random” measurement outcomes are viewed as epistemic limitations arising from a deterministic, computationally irreducible substrate. By modeling the environment not as a heat bath but as a correlated memory register, we demonstrate that what is conventionally discarded as noise contains recoverable information.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. This dramatic improvement (a 42% gain in recoverable information) provides the computational evidence for our central thesis: noise is signal .

   from 4.4 ML-Driven Correlation

2. Decoding is possible: Machine learning can extract this hidden information, converting “entropy” back into “state knowledge” with >90% accuracy.

   from 4.7 Summary of Computational

3. The cost of running an inference model (RNN) on a classical FPGA or ASIC at room temperature is orders of magnitude lower than the cost of maintaining additional superconducting qubits at 15 mK.

   from 5.5 Cost-Benefit Analysis

4. Table 1: Latency Budget for Correlation-Aware Feedback Loop Operation Component Estimated Duration Implementation Notes Measurement Readout 300 ns Standard dispersive readout (optimized) Signal Transmission 10 ns Cryo-to-FPGA (local interconnect) Inference (ML) 200 ns Target for Cryo-CMOS/FPGA inference accelerator Control Pulse Gen 20 ns DAC latency Total Loop Time ~530 ns Typical Coherence Time ($T_2$) ~100 $\mu$s Superconducting Transmon Qubit As Table 1 illustrates, the total loop time of ~530 ns is well within the typical 100 $\mu$s coherence window of a superconducting qubit, providing a generous margin for multiple correction cycles.

   from 5.6 Scalability Challenges

5. We define the CUI as the ratio of recoverable information to total entropy: $$ \text{CUI} = \frac{D_E}{H(O|Z)} = 1 - \frac{H(O | Z, \hat{\lambda}_{est})}{H(O|Z)} $$ In our simulations, the CUI reached approximately 0.61, indicating that 61% of the apparent entropy was actually recoverable signal.

   from 6.2 Standardizing Correlation

6. Simulations of a superdeterministic toy model show a 92% prediction accuracy for “random” errors and a positive information backflow ($\mathcal{N} \approx 0.232$), validating the detection protocol.

   from Abstract

How it is done

1. Superdeterministic Approach to Quantum Signal Processing Author: Rowan Brad Quni-Gudzinas Contact: rowan.quni@outlook.com ORCID: 0009-0002-4317-5604 ISNI: 0000000526456062 URL: https://papers.qnfo.org/papers/epistemic-noise-as-computational-resource/ Date: 2026-01-13 Version: 1.0

   from A

2. We introduce a “Spectroscopic Qubit” architecture and a machine-learning-driven decoding protocol capable of extracting this hidden signal.

   from Abstract

3. This brute-force approach neglects the possibility that what appears as stochastic noise is actually high-dimensional, deterministic correlation information that—if characterized—could be actively decoupled or even utilized.

   from 1.1 The Stagnation

4. This pragmatic approach allows us to explore the utility of hidden variables without getting entangled in the interpretational debates that have historically stalled progress in this domain.

   from 1.3 Research Objectives and

What would falsify it

1. Simulations of a superdeterministic toy model show a 92% prediction accuracy for “random” errors and a positive information backflow ($\mathcal{N} \approx 0.232$), validating the detection protocol.

   from Abstract

2. By employing advanced spectroscopy and machine learning, we can characterize the spectral density of the bath with sufficient precision to predict its dynamics, effectively converting “unknown noise” into “known interference” that can be algorithmically subtracted or controlled.

   from 1.4 The Signal-to-Noise

3. The essential insight here is that while the substrate is deterministic, it exhibits computational irreducibility : there is no “shortcut” algorithm to predict the future state of the system faster than simulating the system step-by-step.

   from 2.2 Cellular

Where the claim stops

1. It does not capture the full complexity of the Hilbert space tensor product structure, nor does it inherently model quantum phase interference or entanglement without the additional assumptions we imposed.

   from 6.5 Limitations of the Current

2. Consequently, while the principle of correlation recovery is validated, the efficiency of the ML decoding in a high-dimensional Hilbert space ($2^N$ dimensions) remains an open question.

   from 6.5 Limitations of the Current

3. Future work must transition from classical proxies to full density matrix simulations of non-Markovian master equations to verify scalability.

   from 6.5 Limitations of the Current

Where to read

1. A22 words · 1 min
2. Abstract170 words · 1 min
3. Keywords15 words · 1 min
4. 1.0 Introduction1,652 words · 7 min
5. 2.01,467 words · 6 min
6. 3.01,464 words · 6 min
7. 4.01,211 words · 5 min
8. 5.01,176 words · 5 min
9. 6.0 Discussion: Interpreting1,217 words · 5 min
10. 7.0 Conclusion918 words · 4 min
11. Appendices907 words · 4 min