Quantum systems are rarely isolated from their surroundings. These hidden environments can retain information and return it later, producing correlated errors in quantum computers and complicating the interpretation of experiments. On a quantum processor, for example, one job can therefore leave an environmental trace that affects the next, even after the qubits themselves have been reset. Learning how this hidden influence persists and affects later behavior is therefore important, but the environment is often impossible to measure directly, and fully reconstructing its effects through tomography rapidly becomes impractical. Here, we introduce a response matrix that uses controlled interventions and observations of the system alone to reveal the temporal structure of environmental memory. The matrix records how future observations change across different controlled histories. Its spectrum characterizes how memory is structured and how it persists under specified system resets, while suitable linear combinations of its entries can rule out specified classical models of that memory. Because its history-future queries can be evaluated independently, numerical response matrices can be assembled in parallel. We establish the framework theoretically, characterize response spectra in an interacting spin process, and certify quantum memory in a two-qubit benchmark. This approach turns system observations into a probe of inaccessible environments and could support the diagnosis of correlated noise, tests of open system models, and memory-aware control design.
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23 pages (18 main + 5 appendix), 9 figures (7 main + 2 appendix)
Quantum systems are rarely isolated from their surroundings. These hidden environments can retain information and return it later, producing correlated errors in quantum computers and complicating the interpretation of experiments. On a quantum processor, for example, one job can therefore leave an environmental trace that affects the next, even after the qubits themselves have been reset. Learning how this hidden influence persists and affects later behavior is therefore important, but the environment is often impossible to measure directly, and fully reconstructing its effects through tomography rapidly becomes impractical. Here, we introduce a response matrix that uses controlled interventions and observations of the system alone to reveal the temporal structure of environmental memory. The matrix records how future observations change across different controlled histories. Its spectrum characterizes how memory is structured and how it persists under specified system resets, while suitable linear combinations of its entries can rule out specified classical models of that memory. Because its history-future queries can be evaluated independently, numerical response matrices can be assembled in parallel. We establish the framework theoretically, characterize response spectra in an interacting spin process, and certify quantum memory in a two-qubit benchmark. This approach turns system observations into a probe of inaccessible environments and could support the diagnosis of correlated noise, tests of open system models, and memory-aware control design.
Understanding temporal processes and their correlations in time is of paramount importance for the development of near-term technologies that operate under realistic conditions. Capturing the complete multi-time statistics that define a stochastic process lies at the heart of any proper treatment of memory effects. This is well understood in classical theory, where a hierarchy of joint probability distributions completely characterizes the process at hand. However, attempting to generalize this notion to quantum mechanics is problematic: observing realizations of a quantum process necessarily disturbs the state of the system, breaking an implicit, and crucial, assumption in the classical setting. This issue can be overcome by separating the experimental interventions from the underlying process, enabling an unambiguous description of the process itself and accounting for all possible multi-time correlations for any choice of interrogating instruments. In this paper, using a novel framework for the characterization of quantum stochastic processes, we first solve the long standing question of unambiguously describing the memory length of a quantum processes. This is achieved by constructing a quantum Markov order condition, which naturally generalizes its classical counterpart for the quantification of finite-length memory effects. As measurements are inherently invasive in quantum mechanics, one has no choice but to define Markov order with respect to the interrogating instruments that are used to probe the process at hand: different memory effects are exhibited depending on how one addresses the system, in contrast to the standard classical setting. We then fully characterize the structural constraints imposed on quantum processes with finite Markov order, shedding light on a variety of memory effects that can arise through various examples. Finally, we introduce an instrument-specific notion of memory strength that allows for a meaningful quantification of the temporal correlations between the history and the future of a process for a given choice of experimental intervention. These findings are directly relevant to both characterizing and exploiting memory effects that persist for a finite duration. In particular, immediate applications range from developing efficient compression and recovery schemes for the description of quantum processes with memory to designing coherent control protocols that efficiently perform information-theoretic tasks, amongst a plethora of others.
Environmental noise is a major source of decoherence and errors in quantum systems. In a non-Markovian environment, the noise has a finite correlation time, and the system can retain information about its previous interactions with the environment. Here, we investigate how such environmental memory can be observed through a finite-duration coherent quantum operation. Coherent control dynamically reshapes the stochastic coupling operator and thereby changes how two-time noise correlations affect the quantum process. For Ornstein-Uhlenbeck noise, we show analytically that two different limits reduce the information on the correlation time. In the short-memory limit, the leading dynamics depend on the integrated-noise combination σ/τc, where σ is the noise variance and τc is the correlation time, making these parameters locally degenerate. In the long-memory limit, the stochastic field becomes quasi-static during the operation, and sensitivity to the correlation time is suppressed. Exact stochastic simulations for a driven single-qubit Xπ rotation and a two-qubit exchange gate show that independent correlation-time information becomes largest between these limits, when the environmental and control timescales are comparable. Noncommuting controls enhance this information, whereas commuting controls do not. Simulations at different gate durations tg further show that the information window approximately follows the dimensionless correlation ratio τc/tg. These results provide a timescale-matching principle for probing finite-correlation non-Markovian noise with coherent quantum control.
Abstract A central problem in open quantum systems is the characterization of non-Markovian processes, where an environment retains the memory of its interaction with the system. A key distinction is whether or not this memory can be simulated classically, as this can lead to efficient modelling and noise mitigation. Powerful tools have been developed recently within the process matrix formalism, a framework that conveniently characterizes all multi-time correlations through a sequence of measurements. This leads to a detailed classification of classical and quantum-memory processes and provides operational procedures to distinguish between them. However, these results leave open the question of what type of system-environment interactions lead to classical memory. More generally, process-matrix methods lack a direct connection to joint system-environment evolution, a cornerstone of open-system modelling. In this work, we characterize Hamiltonian and circuit-based models of system-environment interactions leading to classical memory. We show that general time-dependent Hamiltonians with product eigenstates, and where the environment’s eigenstates form a time-independent, orthonormal basis, always produce a particular type of classical memory: probabilistic mixtures of unitary processes. Equivalently, these Hamiltonians are characterized as commuting with a complete set of observables on the environment. Additionally, we show that the most general type of classical memory processes can be generated by a quantum circuit in which the system and environment interact through a specific class of controlled unitaries. Our results establish the first strong link between process-matrix methods and traditional Hamiltonian-based approaches to open quantum systems.
Every year, substantial theoretical and experimental progress is made towards the realisation of a genuinely new computational paradigm in the construction of a quantum computer. But progress is fractal; to make headway is to unearth the next set of obstacles. Decades of work has so far overcome physical, mathematical, engineering, and information theoretic obstacles to produce the remarkable high-fidelity devices we see today. But these devices must be near perfect to be useful. Indeed, advancements so far have precipitated sensitivity to a host of complex dynamical and control-based effects. Chief among these today are non-Markovian memory effects, where interactions between a quantum system and its surrounding environment can give rise to multi-time correlations. In this thesis, we address this issue and formally present a generalisation of quantum process tomography, called process tensor tomography (PTT). This establishes the ability to rigorously and systematically characterise non-Markovian open quantum systems, resolving many long-standing issues in the field. In the first part of this work, we present an original review of the literature and motivate the problem at hand. In the second, we develop the framework of PTT, including experiment design, post-processing algorithms, and both simulated and near-term device demonstrations. In particular, we demonstrate this as a tool for obtaining in-depth diagnostics about the nature and origin of temporal quantum correlations. Lastly, we dedicate our efforts to efficiency and self-consistency. To this effect, we explore theoretically processes with sparse memory structures. We then leverage this to develop various efficient estimation techniques tailored for different settings. The result is a robust and lightweight framework capable of both reconstructing and optimally controlling any non-Markovian open quantum dynamics.
Quantum density matrices provide a complete operational description of measurement outcomes at a given time, yet it is well known that identical reduced states can arise from inequivalent interaction histories. In non-Markovian settings, these hidden histories can influence future dynamics under re-interaction, leading to divergent evolution despite identical instantaneous observables. This work addresses the structural gap between measurement completeness and evolutionary predictability in standard quantum state descriptions. We formalize this gap by introducing a minimal extension of the quantum state that preserves all standard measurement statistics while explicitly tracking interaction memory. The construction is defined through an extended state space equipped with a canonical marginal map onto the usual density-matrix formalism, ensuring full compatibility with the Born rule, complete positivity, and no-signaling. Within this framework, quantum states are shown to be operationally equivalent for measurement yet generically non-equivalent for future evolution. A concrete two-qubit model is presented to illustrate the core mechanism. Two preparation histories yield identical reduced density matrices—one storing coherence in system-environment correlations and the other dissipating it through decoherence. Under a subsequent memory-coupling interaction, these states evolve differently, demonstrating that reduced quantum states are insufficient to determine future dynamics in the presence of interaction memory. This example mirrors experimentally accessible echo and recoherence protocols and can be simulated exactly using standard quantum tools. The results clarify that no modification of quantum mechanics is required: the observed divergence arises from untracked correlations rather than hidden variables or observer-dependent effects. By separating measurement completeness from evolutionary completeness, this work provides a precise language for non-Markovian dynamics, coherence recovery, and history-dependent qubit behavior. The framework offers a structured basis for analyzing decoherence, error mitigation, and memory effects in realistic quantum systems without altering the predictions of standard quantum theory.
Abstract Established methods for characterizing quantum information processes do not capture non-Markovian (history-dependent) behaviors that occur in real systems. These methods model a quantum process as a fixed map on the state space of a predefined system of interest. Such a map averages over the system’s environment, which may retain some effect of its past interactions with the system and thus have a history-dependent influence on the system. Although the theory of non-Markovian quantum dynamics is currently an active area of research, a systematic characterization method based on a general representation of non-Markovian dynamics has been lacking. In this article we present a systematic method for experimentally characterizing the dynamics of open quantum systems. Our method, which we call quantum process identification (QPI), is based on a general theoretical framework which relates the (non-Markovian) evolution of a system over an extended period of time to a time-local (Markovian) process involving the system and an effective environment. In practical terms, QPI uses time-resolved tomographic measurements of a quantum system to construct a dynamical model with as many dynamical variables as are necessary to reproduce the evolution of the system. Through numerical simulations, we demonstrate that QPI can be used to characterize qubit operations with non-Markovian errors arising from realistic dynamics including control drift, coherent leakage, and coherent interaction with material impurities.