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. We introduce a “Spectroscopic Qubit” architecture and a machine-learning-driven decoding protocol capable of extracting this hidden signal. 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. We define a new “Correlation Utility Index” (CUI) to standardize the measurement of recoverable non-Markovian resources and provide a detailed latency budget for real-time implementation. This work reframes the path to fault tolerance from a battle against ontology to a challenge of decryption.
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. We introduce a “Spectroscopic Qubit” architecture and a machine-learning-driven decoding protocol capable of extracting this hidden signal. Simulations of a superdeterministic toy model show a 92% prediction accuracy for “random” errors and a positive information backflow (N≈0.232), validating the detection protocol. We define a new “Correlation Utility Index” (CUI) to standardize the measurement of recoverable non-Markovian resources and provide a detailed latency budget for real-time implementation. This work reframes the path to fault tolerance from a battle against ontology to a challenge of decryption.
This review synthesizes current understanding of decoherence pathways across leading hardware platforms and explains why several experimentally observed noise features—drift, burst events, coherent components, correlated and non-Markovian structure, and leakage—can be disproportionately damaging for QEC. Decoherence remains the dominant impediment to scalable quantum computing because it is not a single error mechanism but a system-level phenomenon arising from materials defects, electromagnetic loss, control electronics, measurement backaction, and the broader environment in which a processor operates. Quantum error correction (QEC) is designed to algorithmically suppress physical noise, yet its practical success depends on how closely real devices satisfy the assumptions under which fault tolerance is proved: approximate locality, weak temporal correlations, sufficiently stochastic error statistics, low leakage, and reliable syndrome extraction. We then evaluate fundamental and engineering challenges that shape the viability of QEC at scale: syndrome measurement fidelity, correlated error suppression, decoding latency and classical co-processing, architectural constraints (connectivity, crosstalk, calibration overhead), and the resource cost of implementing a universal fault-tolerant gate set. Recent demonstrations of below-threshold behaviour and “break-even” regimes show that QEC is transitioning from theory to practice, but also clarify what remains unresolved: maintaining stable noise below fault-tolerance targets over long times, scaling to many logical qubits with low correlated-error rates, and integrating hardware-aware codes and decoders. We conclude with research priorities that treat decoherence and QEC as part of a co-designed stack spanning device physics, control, architecture, and algorithms.
The potential for scalable quantum computing depends on the viability of fault tolerance and quantum error correction, by which the entropy of environmental noise is removed during a quantum computation to maintain the physical reversibility of the computer’s logical qubits. However, the theory underlying quantum error correction applies a linguistic double standard to the words “noise” and “measurement” by treating environmental interactions during a quantum computation as inherently reversible, and environmental interactions at the end of a quantum computation as irreversible measurements. Specifically, quantum error correction theory models noise as interactions that are uncorrelated or that result in correlations that decay in space and/or time, thus embedding no permanent information to the environment. I challenge this assumption both on logical grounds and by discussing a hypothetical quantum computer based on “position qubits.” The technological difficulties of producing a useful scalable position-qubit quantum computer parallel the overwhelming difficulties in performing a double-slit interference experiment on an object comprising a million to a billion fermions.
Abstract Quantum decoherence, conventionally modeled as a stochastic process, remains the primary obstacle to scalable quantum computation. In this work, a conceptual framework grounded in the hydrodynamic formulation of quantum mechanics (Madelung-Bohm formalism) is presented, which reinterprets decoherence as a process governed by deterministic flow dynamics rather than fundamental randomness. A comprehensive numerical simulation of a qubit coupled to an environmental oscillator at strong coupling ( c = 1.5) has been performed, from which an ensemble of Bohmian trajectories has been extracted and analyzed. The strong coupling regime ensures conclusive, unambiguous demonstration of complete decoherence within accessible simulation times, with system purity exhibiting clear, monotonic decay from a pure to a maximally mixed state without residual oscillations. The results provide direct visualization of decoherence as a continuous dynamical evolution while perfectly reproducing the statistical predictions of standard open quantum systems theory, including Born rule agreement and decoherence-induced pointer state formation. The key finding is that the environmental influence—the ‘decoherence force’ F dec = − ∇ Q derived from the quantum potential—possesses a deterministic, non-random structure with identifiable spectral signatures. This insight provides the theoretical foundation for a class of Noise-Informed Control Strategies . It is argued that by experimentally characterizing this deterministic signature via quantum noise spectroscopy, bespoke control protocols can be designed to suppress or manipulate decoherence. This work shifts the perspective from combating fundamentally random noise to controlling a structured, deterministic interaction, aligning with emerging paradigms in quantum error correction and the information-selection principles of quantum Darwinism.
Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information. Building fault-tolerant architectures requires increasing the code distance, which in turn places growing demands on decoding accuracy, scalability, and practical deployability. While a wide range of decoding algorithms have been proposed and demonstrated, achieving reliable, scalable, and real-time decoding remains a significant challenge. Machine-learning (ML) approaches are particularly well suited to this setting, as quantum error decoding is fundamentally a problem of processing large volumes of classical data with complex spatiotemporal correlations. This chapter surveys ML-based methods for quantum error decoding, with a focus on topological codes and an emphasis on architectural principles, practical performance, and real-time considerations. We first frame decoding as a learning problem and outline key paradigms, including discriminative, generative, and reinforcement-learning formulations. We then introduce the neural network building blocks that underpin most contemporary neural decoders and discuss how these components can be integrated to balance expressivity, scalability, and latency. Building on this architectural perspective, we review recent progress and benchmarks in neural decoding for memory experiments, and discuss real-time decoding, open challenges, and future directions toward scalable fault-tolerant quantum computing.
The characterization of noisy quantum circuits is an important step in the development of large-scale quantum computers. As experimental quantum architectures approach the threshold for fault-tolerant quantum computing, obtaining a benchmark of process fidelities is needed to verify the integrity of the system and ensure functionality. Additionally, the ability to successfully implement a quantum code of choice may depend on the type of noise that is present in the system. It is therefore necessary to characterize not only the fidelity of the process, but also the structure of the noise. \n \nRandomized benchmarking is a characterization protocol which extracts partial information from a noisy quantum process. This thesis gives a detailed review of the randomized benchmarking protocol, and presents the results of three collaborative projects which use techniques similar to randomized benchmarking to obtain previously hidden information. The first protocol is designed to obtain a benchmark on loss errors. This protocol also provides information on the detector efficiency and an additional constraint on estimated parameters from randomized benchmarking. The second protocol is an extension of the first, under the special case of coherent leakage errors, for which we obtain an estimate of the leakage rate. The third protocol provides a method of testing for spatial correlations in noisy processes. This test may be used to determine the correlation structure of errors, and the error correcting codes needed for successful experiments in a particular architecture. All protocols presented in this thesis are scalable, platform-independent and insensitive to state preparation and measurement errors. We also provide results of numerical tests of these protocols for simulated noise, which show their accuracy and robustness.