Quantum Science and Technology

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regarded as machine learning models with remarkable expressive power. This Review presents the components of these models and discusses their application to a variety of data-driven tasks, such as supervised learning and generative modeling. With an increasing number of experimental demonstrations carried out on actual quantum hardware and with software being actively developed, this rapidly growing field is poised to have a broad spectrum of real-world applications.

Quantum transduction, the process of converting quantum signals from one form of energy to another, is an important area of quantum science and technology. The present perspective article reviews quantum transduction between microwave and optical photons, an area that has recently seen a lot of activity and progress because of its relevance for connecting superconducting quantum processors over long distances, among other applications. Our review covers the leading approaches to achieving such transduction, with an emphasis on those based on atomic ensembles, opto-electro-mechanics, and electro-optics. We briefly discuss relevant metrics from the point of view of different applications, as well as challenges for the future.

Due to the exponential growth of the Hilbert space dimension with system size, the simulation of quantum many-body systems has remained a persistent challenge until today. Here, we review a relatively new class of variational states for the simulation of such systems, namely neural quantum states (NQS), which overcome the exponential scaling by compressing the state in terms of the network parameters rather than storing all exponentially many coefficients needed for an exact parameterization of the state. We introduce the commonly used NQS architectures and their various applications for the simulation of ground and excited states, finite temperature and open system states as well as NQS approaches to simulate the dynamics of quantum states. Furthermore, we discuss NQS in the context of quantum state tomography.

Photon-mediated dipole–dipole interactions arise from atom-light interactions, which are universal and prevalent in a wide range of open quantum systems. This pairwise and long-range spin-exchange interaction results from multiple light scattering among the atoms. A recent surge of interests and progresses in both experiments and theories promises this core mechanism of collective interactions as a resource to study quantum science and to envision next-generation applications in quantum technology. Here we summarize recent developments in both theories and experiments, where we introduce several central theoretical approaches and focus on cooperative light scattering from small sample of free-space atoms, an atom-waveguide coupled interface that hosts the waveguide QED, and topological quantum optical platforms. The aim of this review is to manifest the essential and distinct features of collective dynamics induced by resonant dipole–dipole interactions and to reveal unprecedented opportunities in enhancing the performance or offering new applications in light manipulations, quantum metrology, quantum computations, and light harvesting innovations.

Universal fault-tolerant quantum computers will require error-free execution of long sequences of quantum gate operations, which is expected to involve millions of physical qubits. Before the full power of such machines will be available, near-term quantum devices will provide several hundred qubits and limited error correction. Still, there is a realistic prospect to run useful algorithms within the limited circuit depth of such devices. Particularly promising are optimization algorithms that follow a hybrid approach: the aim is to steer a highly entangled state on a quantum system to a target state that minimizes a cost function via variation of some gate parameters. This variational approach can be used both for classical optimization problems as well as for problems in quantum chemistry. The challenge is to converge to the target state given the limited coherence time and connectivity of the qubits. In this context, the quantum volume as a metric to compare the power of near-term quantum devices is discussed. With focus on chemistry applications, a general description of variational algorithms is provided and the mapping from fermions to qubits is explained. Coupled-cluster and heuristic trial wave-functions are considered for efficiently finding molecular ground states. Furthermore, simple error-mitigation schemes are introduced that could improve the accuracy of determining ground-state energies. Advancing these techniques may lead to near-term demonstrations of useful quantum computation with systems containing several hundred qubits.

Deep neural networks have demonstrated remarkable efficacy in extracting meaningful representations from complex datasets. This has propelled representation learning as a compelling area of research across diverse fields. One interesting open question is how beneficial representation learning can be for quantum many-body physics, with its notoriously high-dimensional state space. In this work, we showcase the capacity of a neural network that was trained on a subset of physical observables of a many-body system to partially acquire an implicit representation of the wave function. We illustrate this by demonstrating the effectiveness of reusing the representation learned by the neural network to enhance the learning process of another quantity derived from the quantum state. In particular, we focus on how the pre-trained neural network can enhance the learning of entanglement entropy. This is of particular interest as directly measuring the entanglement in a many-body system is very challenging, while a subset of physical observables can be easily measured in experiments. We show the pre-trained neural network learns the dynamics of entropy with fewer resources and higher precision in comparison with direct training on the entanglement entropy.

We have integrated single and coupled superconducting transmon qubits into flip-chip modules. Each module consists of two chips—one quantum chip and one control chip—that are bump-bonded together. We demonstrate time-averaged coherence times exceeding 90 μs, single-qubit gate fidelities exceeding 99.9%, and two-qubit gate fidelities above 98.6%. We also present device design methods and discuss the sensitivity of device parameters to variation in interchip spacing. Notably, the additional flip-chip fabrication steps do not degrade the qubit performance compared to our baseline state-of-the-art in single-chip, planar circuits. This integration technique can be extended to the realisation of quantum processors accommodating hundreds of qubits in one module as it offers adequate input/output wiring access to all qubits and couplers.

Since its advent in 2011, boson sampling has been a preferred candidate for demonstrating quantum advantage because of its simplicity and near-term requirements compared to other quantum algorithms. We propose to use a variant, called coarse-grained boson-sampling (CGBS), as a quantum proof-of-work (PoW) scheme for blockchain consensus. The miners perform boson sampling using input states that depend on the current block information and commit their samples to the network. Afterwards, CGBS strategies are determined which can be used to both validate samples and reward successful miners. By combining rewards for miners committing honest samples together with penalties for miners committing dishonest samples, a Nash equilibrium is found that incentivises honest miners. We provide numerical evidence that these validation tests are hard to spoof classically without knowing the binning scheme ahead of time and show the robustness of our protocol to small partial distinguishability of photons. The scheme works for both Fock state boson sampling and Gaussian boson sampling and provides dramatic speedup and energy savings relative to computation by classical hardware.

Quantum chemical calculations are among the most promising applications for quantum computing. Implementations of dedicated quantum algorithms on available quantum hardware were so far, however, mostly limited to comparatively simple systems without strong correlations. As such, they can also be addressed by classically efficient single-reference methods. Here we calculate the lowest energy eigenvalue of active space Hamiltonians of industrially relevant and strongly correlated metal chelates on trapped ion quantum hardware, and integrate the results into a typical industrial quantum chemical workflow to arrive at chemically meaningful properties. We are able to achieve chemical accuracy by training a variational quantum algorithm on quantum hardware, followed by a classical diagonalization in the subspace of states measured as outputs of the quantum circuit. This approach is particularly measurement-efficient, requiring 600 single-shot measurements per cost function evaluation on a ten qubit system, and allows for efficient post-processing to handle erroneous runs.

Low loss and high-speed processing of photons is important to photonic quantum information technologies. The speed with which quantum light generation can be modulated impacts the clock rate of photonic quantum computers, the data rate of quantum communication and applications of quantum enhanced radio-frequency sensors. Here we use lossy carrier depletion modulators in a silicon waveguide nonlinear interferometer to modulate photon pair generation probability at 1 gigahertz (GHz) without exposing the generated photons to the phase dependent parasitic loss of the modulators. The super sensitivity of nonlinear interferometers reduces power consumption compared to modulating the driving laser. This can be used for high-speed programmable nonlinearity in waveguide networks for quantum technologies and for optical quantum sensors.

In this paper, we introduce a new approach to quantum benchmarking inspired by quantum verification, motivating new paradigms in quantum benchmarking. Our proposed benchmark not only serves as a robust indicator of computational capability but also offers scalability, customizability, and universality. By providing formal statements regarding the quality of quantum devices while assuming device consistency, we eliminate the reliance on heuristics. We establish a deep connection between quantum verification and quantum benchmarking. For practical application, we present a concrete benchmarking protocol for bounded-error quantum polynomial time (BQP) computations derived from an efficient quantum verification protocol and prove it to match our redefined standards for quantum benchmarking.

Continuous-variable (CV) codes and their application in quantum communication have attracted increasing attention. In particular, one typical CV codes, cat-codes, has already been experimentally created using trapped atoms in cavities with relatively high fidelities. However, when these codes are used in a repeater protocol, the secret key rate (SKR) that can be extracted between two remote users is extremely low. Here we propose a quantum repeater protocol based on cat codes with a few quantum memories or graph states as additional resources. This allows us to considerably increase the SKR by several orders of magnitude. Our findings provide valuable insights for designing efficient quantum repeater systems, advancing the feasibility and performance of quantum communication over long distances.

The learner's ability to generate a hypothesis that closely approximates the target function is crucial in machine learning. Achieving this requires sufficient data; however, unauthorized access by an eavesdropping learner can lead to security risks. Thus, it is important to ensure the performance of the 'authorized' learner by limiting the quality of the training data accessible to eavesdroppers. Unlike previous studies focusing on encryption or access controls, we provide a theorem to ensure superior learning outcomes exclusively for the authorized learner with quantum label encoding. In this context, we use the probably-approximately-correct learning framework and introduce the concept of learning probability to quantitatively assess learner performance. Our theorem allows the condition that, given a training dataset, an authorized learner is guaranteed to achieve a certain quality of learning outcome, while eavesdroppers are not. Notably, this condition can be constructed based only on the authorized-learning-only measurable quantities of the training data, i.e. its size and noise degree. We validate our theoretical proofs and predictions through convolutional neural networks image classification learning.

Weak value amplification is crucial in quantum metrology because it enhances the detection of subtle interactions between quantum entities. However, current weak value quantum metrology techniques are only effective for extremely weak interactions, significantly narrowing its range of potential applications. In this work, we present the 'metrological weak value (MWV)', designed for use with quantum interactions of any strengths, making it unnecessary to have prior knowledge of how strong or weak a quantum interaction might be. Additionally, we demonstrate an adaptive estimation scheme for weak value quantum metrology tailored for gauging an undetermined interaction strength. This scheme, rooted in MWV measurements, aligns perfectly with the quantum Cramér–Rao lowest bound. The versatility and effectiveness of the MWV enables weak value quantum metrology beyond weak interactions, paving the way for precision in quantum measurement and broadening its utility across various quantum systems.

We report on the preparation of a large quantum register of 5612 qubits, with the unprecedented high global fidelity of $F\simeq 0.9956$. This was achieved by applying an improved cooperative quantum information erasure protocol (Buffoni and Campisi 2023 Quantum7 961) to a programmable network of superconducting qubits featuring a high connectivity. At variance with the standard method based on the individual reset of each qubit in parallel, here the quantum register is treated as a whole, thus avoiding the well-known orthogonality catastrophe whereby even an extremely high individual reset fidelity f results in vanishing global fidelities $F = f^N$ with growing number N of qubits.

Elementary quantum mechanics proposes that a closed physical system consistently evolves in a reversible manner. However, control and readout necessitate the coupling of the quantum system to the external environment, subjecting it to relaxation and decoherence. Consequently, system-environment interactions are indispensable for simulating physically significant theories. A broad spectrum of physical systems in condensed-matter and high-energy physics, vibrational spectroscopy, and circuit and cavity QED necessitates the incorporation of bosonic degrees of freedom, such as phonons, photons, and gluons, into optimized fermion algorithms for near-future quantum simulations. In particular, when a quantum system is surrounded by an external environment, its basic physics can usually be simplified to a spin or fermionic system interacting with bosonic modes. Nevertheless, troublesome factors such as the magnitude of the bosonic degrees of freedom typically complicate the direct quantum simulation of these interacting models, necessitating the consideration of a comprehensive plan. This strategy should specifically include a suitable fermion/boson-to-qubit mapping scheme to encode sufficiently large yet manageable bosonic modes, and a method for truncating and/or downfolding the Hamiltonian to the defined subspace for performing an approximate but highly accurate simulation, guided by rigorous error analysis. In this pedagogical tutorial review, we aim to provide such an exhaustive strategy, focusing on encoding and simulating certain bosonic-related model Hamiltonians, inclusive of their static properties and time evolutions. Specifically, we emphasize two aspects: (1) the discussion of recently developed quantum algorithms for these interacting models and the construction of effective Hamiltonians, and (2) a detailed analysis regarding a tightened error bound for truncating the bosonic modes for a class of fermion-boson interacting Hamiltonians.

Photon-mediated dipole–dipole interactions arise from atom-light interactions, which are universal and prevalent in a wide range of open quantum systems. This pairwise and long-range spin-exchange interaction results from multiple light scattering among the atoms. A recent surge of interests and progresses in both experiments and theories promises this core mechanism of collective interactions as a resource to study quantum science and to envision next-generation applications in quantum technology. Here we summarize recent developments in both theories and experiments, where we introduce several central theoretical approaches and focus on cooperative light scattering from small sample of free-space atoms, an atom-waveguide coupled interface that hosts the waveguide QED, and topological quantum optical platforms. The aim of this review is to manifest the essential and distinct features of collective dynamics induced by resonant dipole–dipole interactions and to reveal unprecedented opportunities in enhancing the performance or offering new applications in light manipulations, quantum metrology, quantum computations, and light harvesting innovations.

Neutral Atom Quantum Computing (NAQC) emerges as a promising hardware platform primarily due to its long coherence times and scalability. Additionally, NAQC offers computational advantages encompassing potential long-range connectivity, native multi-qubit gate support, and the ability to physically rearrange qubits with high fidelity. However, for the successful operation of a NAQC processor, one additionally requires new software tools to translate high-level algorithmic descriptions into a hardware executable representation, taking maximal advantage of the hardware capabilities. Realizing new software tools requires a close connection between tool developers and hardware experts to ensure that the corresponding software tools obey the corresponding physical constraints. This work aims to provide a basis to establish this connection by investigating the broad spectrum of capabilities intrinsic to the NAQC platform and its implications on the compilation process. To this end, we first review the physical background of NAQC and derive how it affects the overall compilation process by formulating suitable constraints and figures of merit. We then provide a summary of the compilation process and discuss currently available software tools in this overview. Finally, we present selected case studies and employ the discussed figures of merit to evaluate the different capabilities of NAQC and compare them between two hardware setups.

Magic states have been widely studied in recent years as resource states that help quantum computers achieve fault-tolerant universal quantum computing. The fault-tolerant quantum computing requires fault-tolerant implementation of a set of universal logical gates. Stabilizer code, as a commonly used error correcting code with good properties, can apply the Clifford gates transversally which is fault tolerant. But only Clifford gates cannot realize universal computation. Magic states are introduced to construct non-Clifford gates that combine with Clifford operations to achieve universal quantum computing. Since the preparation of quantum states is inevitably accompanied by noise, preparing the magic state with high fidelity and low overhead is the crucial problem to realizing universal quantum computation. In this paper, we survey the related literature in the past 20 years and introduce the common types of magic states, the protocols to obtain high-fidelity magic states, and overhead analysis for these protocols. Finally, we discuss the future directions of this field.

The field of propagating quantum microwaves is a relatively new area of research that is receiving increased attention due to its promising technological applications, both in communication and sensing. While formally similar to quantum optics, some key elements required by the aim of having a controllable quantum microwave interface are still on an early stage of development. Here, we argue where and why a fully operative toolbox for propagating quantum microwaves will be needed, pointing to novel directions of research along the way: from microwave quantum key distribution to quantum radar, bath-system learning, or direct dark matter detection. The article therefore functions both as a review of the state-of-the-art, and as an illustration of the wide reach of applications the future of quantum microwaves will open.

The entanglement distribution networks with various topologies are mainly implemented by active wavelength multiplexing routing strategies, which directly transmit entangled photons between quantum network nodes. However, designing an entanglement routing scheme, which achieves the maximized network connections and the optimal overall network efficiency simultaneously, remains a huge challenge for quantum networks. In this article, we propose a differentiated service entanglement routing (DSER) scheme, which firstly finds out the lowest loss paths and supported wavelength channels with the tensor-based path searching algorithm, and then allocates the paired channels with the differentiated routing strategies. The evaluation results show that the proposed DSER scheme can be performed for constructing various large scale quantum networks.

Quantum-information processing and computation with bosonic qubits are corruptible by noise channels. Using interferometers and photon-subtraction gadgets (PSGs) accompanied by linear amplification and attenuation, we establish linear-optical methods to mitigate and suppress bosonic noise channels. We first show that by employing amplifying and attenuating PSGs respectively at the input and output of either a thermal or random-displacement channel, probabilistic error cancellation (PEC) can be carried out to mitigate errors in expectation-value estimation. We also derive optimal physical estimators that are properly constrained to improve the sampling accuracy of PEC. Next, we prove that a purely-dephasing channel is coherently suppressible using a multimode Mach--Zehnder interferometer and conditional vacuum measurements (VMZ). In the limit of infinitely-many ancillas, with nonvanishing success rates, VMZ using either Hadamard or two-design interferometers turns any dephasing channel into a phase-space-rotated linear-attenuation channel that can subsequently be inverted with (rotated) linear amplification without Kerr nonlinearity. Moreover, for weak central-Gaussian dephasing, the suppression fidelity increases monotonically with the number of ancillas and most optimally with Hadamard interferometers. We demonstrate the performance of these linear-optical mitigation and suppression schemes on common noise channels (and their compositions) and popular bosonic codes. While the theoretical formalism pertains to idling noise channels, we also provide numerical evidence supporting mitigation and suppression capabilities with respect to noise from universal gate operations.

Long-duration and efficient quantum memories for photons are key components of quantum repeater and network applications. To achieve long-duration storage in atomic systems, a short-lived optical coherence can be mapped into a long lived spin coherence, which forms the basis for many quantum memory schemes. In this work, we present modeling and measurements of the back-and-forth, i.e. reversible, optical-to-spin conversion for an atomic frequency comb (AFC) memory. The AFC memory is implemented in ¹⁵¹Eu³⁺:Y₂SiO₅ with an applied magnetic field of 231 mT, to suppress time-domain interference effects in the conversion efficiency. By optimizing the conversion using the developed simulation tool, experimentally we achieve a total efficiency of up to 96%, including the spin echo sequence and spin dephasing, for a storage time of 500 µs. Our methods and results pave the way for long-duration storage of single photon states in ¹⁵¹Eu³⁺:Y₂SiO₅ with high signal-to-noise, at the millisecond timescale.

We study the dynamics of a qubit system interacting with thermalized bath-ancilla spins via a repeated interaction scheme. Considering generic initial conditions for the system and employing a Heisenberg-type interaction between the system and the ancillas, we analytically prove the following: (i) The population and coherences of the system qubit evolve independently toward a nonequilibrium steady-state solution, which is diagonal in the qubit's energy eigenbasis. The population relaxes to this state geometrically, whereas the coherences decay through a more compound behavior. (ii) In the long time limit, the system approaches a steady state that generally differs from the thermal state of the ancilla. We derive this steady-state solution and show its dependence on the interaction parameters and collision frequency. (iii) We bound the number of interaction steps required to achieve the steady state within a specified error tolerance, and we evaluate the energetic cost associated with the process. Our key finding is that deterministic system-ancilla interactions do not typically result in the system thermalizing to the thermal state of the ancilla. Instead, they generate a distinct nonequilibrium steady state, which we explicitly derive. However, we also identify an operational regime that leads to thermalization with a few long and possibly randomized collisions.

Running quantum algorithms protected by quantum error correction requires a real time, classical decoder. To prevent the accumulation of a backlog, this decoder must process syndromes from the quantum device at a faster rate than they are generated. Most prior work on real time decoding has focused on an isolated logical qubit encoded in the surface code. However, for surface code, quantum programs of utility will require multi-qubit interactions performed via lattice surgery. A large merged patch can arise during lattice surgery — possibly as large as the entire device. This puts a significant strain on a real time decoder, which must decode errors on this merged patch and maintain the level of fault-tolerance that it achieves on isolated logical qubits.
These requirements are relaxed by using spatially parallel decoding, which can be accomplished by dividing the physical qubits on the device into multiple overlapping groups and assigning a decoder module to each. We refer to this approach as spatially parallel windows. While previous work has explored similar ideas, none have addressed system-specific considerations pertinent to the task or the constraints from using hardware accelerators. In this work, we demonstrate how to configure spatially parallel windows, so that the scheme (1) is compatible with hardware accelerators, (2) supports general lattice surgery operations, (3) maintains the fidelity of the logical qubits, and (4) meets the throughput requirement for real time decoding. Furthermore, our results reveal the importance of optimally choosing the buffer width to achieve a balance between accuracy and throughput — a decision that should be influenced by the device's physical noise.

In this paper, we introduce a new approach to quantum benchmarking inspired by quantum verification, motivating new paradigms in quantum benchmarking. Our proposed benchmark not only serves as a robust indicator of computational capability but also offers scalability, customizability, and universality. By providing formal statements regarding the quality of quantum devices while assuming device consistency, we eliminate the reliance on heuristics. We establish a deep connection between quantum verification and quantum benchmarking. For practical application, we present a concrete benchmarking protocol for bounded-error quantum polynomial time (BQP) computations derived from an efficient quantum verification protocol and prove it to match our redefined standards for quantum benchmarking.

Continuous-variable (CV) codes and their application in quantum communication have attracted increasing attention. In particular, one typical CV codes, cat-codes, has already been experimentally created using trapped atoms in cavities with relatively high fidelities. However, when these codes are used in a repeater protocol, the secret key rate (SKR) that can be extracted between two remote users is extremely low. Here we propose a quantum repeater protocol based on cat codes with a few quantum memories or graph states as additional resources. This allows us to considerably increase the SKR by several orders of magnitude. Our findings provide valuable insights for designing efficient quantum repeater systems, advancing the feasibility and performance of quantum communication over long distances.

We report on the preparation of a large quantum register of 5612 qubits, with the unprecedented high global fidelity of $F\simeq 0.9956$. This was achieved by applying an improved cooperative quantum information erasure protocol (Buffoni and Campisi 2023 Quantum7 961) to a programmable network of superconducting qubits featuring a high connectivity. At variance with the standard method based on the individual reset of each qubit in parallel, here the quantum register is treated as a whole, thus avoiding the well-known orthogonality catastrophe whereby even an extremely high individual reset fidelity f results in vanishing global fidelities $F = f^N$ with growing number N of qubits.

Quantum-information processing and computation with bosonic qubits are corruptible by noise channels. Using interferometers and photon-subtraction gadgets (PSGs) accompanied by linear amplification and attenuation, we establish linear-optical methods to mitigate and suppress bosonic noise channels. We first show that by employing amplifying and attenuating PSGs respectively at the input and output of either a thermal or random-displacement channel, probabilistic error cancellation (PEC) can be carried out to mitigate errors in expectation-value estimation. We also derive optimal physical estimators that are properly constrained to improve the sampling accuracy of PEC. Next, we prove that a purely-dephasing channel is coherently suppressible using a multimode Mach--Zehnder interferometer and conditional vacuum measurements (VMZ). In the limit of infinitely-many ancillas, with nonvanishing success rates, VMZ using either Hadamard or two-design interferometers turns any dephasing channel into a phase-space-rotated linear-attenuation channel that can subsequently be inverted with (rotated) linear amplification without Kerr nonlinearity. Moreover, for weak central-Gaussian dephasing, the suppression fidelity increases monotonically with the number of ancillas and most optimally with Hadamard interferometers. We demonstrate the performance of these linear-optical mitigation and suppression schemes on common noise channels (and their compositions) and popular bosonic codes. While the theoretical formalism pertains to idling noise channels, we also provide numerical evidence supporting mitigation and suppression capabilities with respect to noise from universal gate operations.

Long-duration and efficient quantum memories for photons are key components of quantum repeater and network applications. To achieve long-duration storage in atomic systems, a short-lived optical coherence can be mapped into a long lived spin coherence, which forms the basis for many quantum memory schemes. In this work, we present modeling and measurements of the back-and-forth, i.e. reversible, optical-to-spin conversion for an atomic frequency comb (AFC) memory. The AFC memory is implemented in ¹⁵¹Eu³⁺:Y₂SiO₅ with an applied magnetic field of 231 mT, to suppress time-domain interference effects in the conversion efficiency. By optimizing the conversion using the developed simulation tool, experimentally we achieve a total efficiency of up to 96%, including the spin echo sequence and spin dephasing, for a storage time of 500 µs. Our methods and results pave the way for long-duration storage of single photon states in ¹⁵¹Eu³⁺:Y₂SiO₅ with high signal-to-noise, at the millisecond timescale.

We study the dynamics of a qubit system interacting with thermalized bath-ancilla spins via a repeated interaction scheme. Considering generic initial conditions for the system and employing a Heisenberg-type interaction between the system and the ancillas, we analytically prove the following: (i) The population and coherences of the system qubit evolve independently toward a nonequilibrium steady-state solution, which is diagonal in the qubit's energy eigenbasis. The population relaxes to this state geometrically, whereas the coherences decay through a more compound behavior. (ii) In the long time limit, the system approaches a steady state that generally differs from the thermal state of the ancilla. We derive this steady-state solution and show its dependence on the interaction parameters and collision frequency. (iii) We bound the number of interaction steps required to achieve the steady state within a specified error tolerance, and we evaluate the energetic cost associated with the process. Our key finding is that deterministic system-ancilla interactions do not typically result in the system thermalizing to the thermal state of the ancilla. Instead, they generate a distinct nonequilibrium steady state, which we explicitly derive. However, we also identify an operational regime that leads to thermalization with a few long and possibly randomized collisions.

Trapped-ion hardware based on the magnetic gradient induced coupling (MAGIC) scheme is emerging as a promising platform for quantum computing. Nevertheless, in this—as in any other—quantum-computing platform, many technical questions still have to be resolved before large-scale and error-tolerant applications are possible. In this work, we present a thorough discussion of the structure and effects of higher-order terms in the MAGIC setup, which can occur due to anharmonicities in the external potential of the ion crystal (e.g. through Coulomb repulsion) or through curvature of the applied magnetic field. These terms generate systematic shifts in the leading-order interactions and take the form of three-spin couplings, two-spin couplings, local fields, as well as diverse phonon–phonon conversion mechanisms. We find that most of these are negligible in realistic situations, with only two contributions that need careful attention. First, there are undesired longitudinal fields contributing shifts to the resonance frequency, whose strength increases with chain length and phonon occupation numbers; while their mean effect can easily be compensated by additional Z rotations, phonon number fluctuations need to be avoided for precise gate operations. Second, anharmonicities of the Coulomb interaction can lead to well-known two-to-one conversions of phonon excitations. Both of these error terms can be mitigated by sufficiently cooling the phonons to the ground-state. Our detailed analysis constitutes an important contribution on the way of making magnetic-gradient trapped-ion quantum technology fit for large-scale applications, and it may inspire new ways to purposefully design interaction terms.

Elementary quantum mechanics proposes that a closed physical system consistently evolves in a reversible manner. However, control and readout necessitate the coupling of the quantum system to the external environment, subjecting it to relaxation and decoherence. Consequently, system-environment interactions are indispensable for simulating physically significant theories. A broad spectrum of physical systems in condensed-matter and high-energy physics, vibrational spectroscopy, and circuit and cavity QED necessitates the incorporation of bosonic degrees of freedom, such as phonons, photons, and gluons, into optimized fermion algorithms for near-future quantum simulations. In particular, when a quantum system is surrounded by an external environment, its basic physics can usually be simplified to a spin or fermionic system interacting with bosonic modes. Nevertheless, troublesome factors such as the magnitude of the bosonic degrees of freedom typically complicate the direct quantum simulation of these interacting models, necessitating the consideration of a comprehensive plan. This strategy should specifically include a suitable fermion/boson-to-qubit mapping scheme to encode sufficiently large yet manageable bosonic modes, and a method for truncating and/or downfolding the Hamiltonian to the defined subspace for performing an approximate but highly accurate simulation, guided by rigorous error analysis. In this pedagogical tutorial review, we aim to provide such an exhaustive strategy, focusing on encoding and simulating certain bosonic-related model Hamiltonians, inclusive of their static properties and time evolutions. Specifically, we emphasize two aspects: (1) the discussion of recently developed quantum algorithms for these interacting models and the construction of effective Hamiltonians, and (2) a detailed analysis regarding a tightened error bound for truncating the bosonic modes for a class of fermion-boson interacting Hamiltonians.

Subspace preserving quantum circuits are a class of quantum algorithms that, relying on some symmetries in the computation, can offer theoretical guarantees for their training. Those algorithms have gained extensive interest as they can offer polynomial speed-up and can be used to mimic classical machine learning algorithms. In this work, we propose a novel convolutional neural network architecture model based on Hamming weight (HW) preserving quantum circuits. In particular, we introduce convolutional layers, and measurement based pooling layers that preserve the symmetries of the quantum states while realizing non-linearity using gates that are not subspace preserving. Our proposal offers significant polynomial running time advantages over classical deep-learning architecture. We provide an open source simulation library for HW preserving quantum circuits that can simulate our techniques more efficiently with GPU-oriented libraries. Using this code, we provide examples of architectures that highlight great performances on complex image classification tasks with a limited number of qubits, and with fewer parameters than classical deep-learning architectures.

Neural decoders for quantum error correction rely on neural networks to classify syndromes extracted from error correction codes and find appropriate recovery operators to protect logical information against errors. Its ability to adapt to hardware noise and long-term drifts make neural decoders promising candidates for inclusion in a fault-tolerant quantum architecture. However, given their limited scalability, it is prudent that small-scale (local) neural decoders are treated as first stages of multi-stage decoding schemes for fault-tolerant quantum computers with millions of qubits. In this case, minimizing the decoding time to match the stabilization measurements frequency and a tight co-integration with the QPUs is highly desired. Cryogenic realizations of neural decoders can not only improve the performance of higher stage decoders, but they can minimize communication delays, and alleviate wiring bottlenecks. In this work, we design and analyze a neural decoder based on an in-memory computation (IMC) architecture, where crossbar arrays of resistive memory devices are employed to both store the synaptic weights of the neural decoder and perform analog matrix–vector multiplications. In simulations supported by experimental measurements, we investigate the impact of TiO_x-based memristive devices' non-idealities on decoding fidelity. We develop hardware-aware re-training methods to mitigate the fidelity loss, restoring the ideal decoder's pseudo-threshold for the distance-3 surface code. This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated fault-tolerant quantum error correction.