Quantum Science and Technology

Solving computational fluid dynamics (CFD) problems requires the inversion of a linear system of equations, which can be done using a quantum algorithm for matrix inversion (Gilyén et al 2019 Proc. 51st Annual ACM SIGACT Symp. on Theory of Computing 193–204). However, the number of shots required to measure the output of the system can be prohibitive and remove any advantage obtained by quantum computing. In this work we propose a scheme for measuring the output of a quantum singular value transform (QSVT) matrix inversion algorithm specifically for the CFD use case. We use a quantum signal processing based amplitude estimation algorithm (Rall and Fuller 2023 Quantum 7 937) and show how it can be combined with the QSVT matrix inversion algorithm. We perform a detailed resource estimation of the amount of computational resources required for a single iteration of amplitude estimation, and compare the costs of amplitude estimation with the cost of not doing amplitude estimation and measuring the whole wavefunction. We also propose a measurement scheme to reduce the number of amplitudes measured in the CFD example by focussing on large amplitudes only. We simulate the whole CFD loop, finding that thus measuring only a small number of the total amplitudes in the output vector still results in an acceptable level of overall error.

With the demonstrated ability to perform calculations in seconds that would take classical supercomputers thousands of years, quantum computers namely hold the promise of radically advancing sustainable IT. However, quantum computers face challenges due to the inherent noise in physical qubits, necessitating error correction for reliable operation in solving industrial-scale problems, which will require more computation time, energy, and electronic components than initial laboratory-scale experiments. Yet, while researchers have modeled and analyzed the environmental impacts of classical computers using Life Cycle Assessment (LCA), the environmental performance of quantum computing remains unknown to date. This study contributes to filling this critical gap in two ways: (1) by establishing an environmental profile for quantum computers based on superconducting qubits; and (2) by comparing it to a functionally equivalent profile of a state-of-the-art supercomputer. With the comparison based on the problem size, the paper shows how the usage time can drive an environmental advantage for quantum computers under specific scaling conditions and quantum error correcting codes. The results emphasize that quantum error correction hardware has a substantial environmental impact due to the numerous electronic components needed to achieve 100 logical qubits. This paper can serve as a basis for designing more environmentally friendly quantum computers and for establishing their environmental profiles, as well as those of the human activities that will use them.

We propose using the dynamical invariants, also known as the Lewis–Riesenfeld invariants, to speed-up the equilibration of a driven open quantum system. This allows us to reverse engineer the time-dependent master equation that describes the dynamics of the open quantum system and systematically derive a protocol that realizes a shortcut to equilibration. The method does not require additional constraints on the timescale of the dynamics beside the Born–Markov approximation and can be generically applied to boost single particle quantum engines significantly. We demonstrate this with the damped harmonic oscillator, and show that our protocol can achieve high-fidelity control on shorter timescales than simple non-optimized protocols. We find that the system is heated during the dynamics to speed-up the equilibration, which can be considered as an analogue of the Mpemba effect in quantum control.

We establish an information gain-disturbance trade-off relation in local state discrimination. Our result demonstrates a fundamental limitation of local strategy to discriminate entangled quantum states without disturbance, which becomes more difficult as the entanglement of the states to be discriminated increases. For a set of maximally entangled states, the capability of local strategy is tightly suppressed, as random guessing without measurements saturates the bound provided by the trade-off relation. We also show that the trade-off can be circumvented when local operations are aided by pre-shared entanglement. To simultaneously achieve correct guessing of state and non-disturbance, an entirely different strategy from conventional state discrimination should be adopted to lower the cost of pre-shared entanglement. We explicitly propose an adaptive and non-destructive strategy based on the stabilizer formalism, which shows a strict advantage over conventional teleportation-based approaches in pre-shared entanglement cost for discriminating a set of maximally entangled states. As an application of the trade-off relation, we propose an entanglement certification protocol that is robust against depolarizing noise and generalize it to multipartite scenarios in a quantum network.

Information reconciliation (IR) is a crucial component in the post-processing stage of continuous-variable quantum key distribution (CV-QKD) systems. However, the requirement to process a large amount of information in IR has become the bottleneck of realizing high-throughput CV-QKD systems, and the phenomenon of classical channel overloads appears. To solve these issues, we first propose a lossy compression scheme based on polar codes for the Gaussian sequences, and then propose an efficient IR protocol by combining such a lossy compression. By compressing the Gaussian sequences obtained by Alice and Bob, the new proposed protocol reduces the amount of information to be processed in IR, effectively breaking the bottleneck of realizing high-throughput CV-QKD systems. Additionally, it reduces the information Alice and Bob need to transmit over classical channels, easing the classical channel load. The theoretical analysis conducted on the compression ratio of the protocol and throughput offers valuable guidance for IR. Simulations confirmed that the proposed protocol can achieve higher throughput over the other polar-code-based IR protocols.

We explore the energy content of superpositions of single-excitation current states. Specifically, we focus on the maximum energy that can be extracted from them through local unitary transformations. The figure of merit we employ is the local ergotropy. We consider an XY spin-chain model and perform a complete analysis in the whole range of the system parameters. This way, we prove that superpositions of two current states in spatially closed spin networks are characterized by specific peaks in extractable energy, generally overcoming the ergotropy of each of the two separate current states characterized by a single winding number. The many-body state dynamics entails to ergotropy evolving in a controlled fashion. The implementation we suggest is based on a Rydberg-atom platform. Optimal transformations able to extract locally the maximum possible amount of energy are sorted out.

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.

Quantum optimal control theory (QOCT) can be used to design the shape of electromagnetic pulses that implement operations on quantum devices. By using non-trivially shaped waveforms, gates can be made significantly faster than those built by concatenating monochromatic pulses. Recently, we applied this idea to the control of molecular spin qudits modelled with Schrödinger’s equation and showed it can speed up operations, helping mitigate the effects of decoherence [Phys. Rev. Appl. 17, 064028 (2022)]. However, short gate times require large optimal pulse amplitudes, which may not be experimentally accessible. Introducing bounds to the amplitudes then unavoidably leads to longer operation times, for which decoherence can no longer be neglected. Here, we study how to improve this procedure by applying QOCT on top of Lindblad’s equation, to design control pulses accounting for decoherence already in the optimization process. We define the control signal in terms of generic parameters, which permits the introduction of bounds and constraints. This is convenient, as amplitude and frequency limitations are inherent to waveform generators. The pulses that we obtain consistently enhance operation fidelities compared to those achieved with the optimization based on Schrödinger’s equation, demonstrating the flexibility and robustness of our method. The improvement is larger the shorter the spin coherence time T2.

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 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 Hamming weight 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.

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.

Efficient encoding of electronic operators into qubits is essential for quantum chemistry simulations. The majority of methods map single electron states to qubits, effectively handling electron interactions. Alternatively, pairs of electrons can be represented as quasi-particles and encoded into qubits, significantly simplifying calculations. This work presents a hybrid encoding that allows splitting the Fock space into Fermionic and Bosonic subspaces. By leveraging the strengths of both approaches, we provide a flexible framework for optimizing quantum simulations based on molecular characteristics and hardware constraints.

A fast and scalable scheme for multi-qubit resetting in superconducting quantum processors is proposed by exploiting the feasibility of frequency-tunable transmon qubits and transmon-like couplers to engineer a full programmable superconducting erasing head. We demonstrate the emergence of collective effects that lead to a decoherence-free subspace during the erasing process. The presence of such a subspace negatively impacts the device's performance and has been overlooked in other multi-qubit chips. To circumvent this issue and pave the way to the device's scalability, we employ tunable frequency couplers to identify a specific set of parameters that enables us to erase even those states within this subspace, ensuring the simultaneous multi-qubit resetting, verified here for the two-qubit case. In contrast, we show that collectivity effects can also emerge as an ingredient to speed up the erasing process. To end, we offer a proposal to build up integrated superconducting processors that can be efficiently connected to erasure heads in a scalable way.

We report on the experimental measurement of the work statistics of a genuinely open quantum system using a quantum computer. Such measurement has remained elusive thus far due to the inherent difficulty in measuring the total energy change of a system-bath compound (which is the work) in the open quantum system scenario. We overcome this difficulty by extending the interferometric scheme, originally conceived for closed systems, to the open system case and implement it on a superconducting quantum computer, taking advantage of the relatively high levels of noise on current quantum hardware to realize an open quantum system. We demonstrate that the method can be used as a diagnostic tool to probe physical properties of the system-bath compound, such as its temperature and specific transitions frequencies in its spectrum. Our experiments corroborate that the interferometric scheme is a promising tool to achieve the long-sought experimental validation of the Jarzynski equality for arbitrary open quantum systems.

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 notouriosly 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.

Feedback control of open quantum systems is of fundamental importance for practical applications in various contexts, ranging from quantum computation to quantum error correction and quantum metrology. Its use in the context of thermodynamics further enables the study of the interplay between information and energy. However, deriving optimal feedback control strategies is highly challenging, as it involves the optimal control of open quantum systems, the stochastic nature of quantum measurement, and the inclusion of policies that maximize a long-term time- and trajectory-averaged goal. In this work, we employ a reinforcement learning approach to automate and capture the role of a quantum Maxwell’s demon: the agent takes the literal role of discovering optimal feedback control strategies in qubit-based systems that maximize a trade-off between measurement-powered cooling and measurement efficiency. Considering weak or projective quantum measurements, we explore different regimes based on the ordering between the thermalization, the measurement, and the unitary feedback timescales, finding different and highly non-intuitive, yet interpretable, strategies. In the thermalization - dominated regime, we find strategies with elaborate finite-time thermalization protocols conditioned on measurement outcomes. In the measurement-dominated regime, we find that optimal strategies involve adaptively measuring different qubit observables reflecting the acquired information, and repeating multiple weak measurements until the quantum state is “sufficiently pure”, leading to random walks in state space. Finally, we study the case when all timescales are comparable, finding new feedback control strategies that considerably outperform more intuitive ones. We discuss a two-qubit example where we explore the role of entanglement and conclude discussing the scaling of our results to quantum many-body systems.

In an era of rapid technological advancements and unprecedented data inundation, sparsity has emerged as a key property with profound implications in various fields. One important application of sparsity is sparse signal recovery, which involves reconstructing signals from limited observations and is of great importance in medical imaging, communication systems, and data compression. However, traditional sparse signal recovery methods often require computationally intensive algorithms, especially for large-scale problems. In this paper, we investigate the application of the coherent Ising machine (CIM), a hybrid quantum computing paradigm, as a novel approach to efficiently solve several sparsity-related optimization problems, presenting significant contributions in terms of model development and experimental validation. Our proposed models surpass existing approaches by reducing the computational resource requirements and enhancing problem-solving capabilities. To further enhance the scalability and efficiency of the proposed model, we incorporate Benders Decomposition to decompose large-scale problems into smaller subproblems that can be solved more effectively. In addition, the efficiency and accuracy of the CIM-based sparse optimization approach are demonstrated through the experiments on the CIM platform, which highlights its potential to solve complex combinatorial optimization problems in practical scenarios.

In broadband quantum optical systems, nonlinear interactions among a large number of frequency components induce complex dynamics that may defy heuristic analysis. In this work we introduce a perturbative framework for factoring out reservoir degrees of freedom and establishing a concise effective model (effective field theory) for the remaining system. Our approach combines approximate diagonalization of judiciously partitioned subsystems with master equation techniques. We consider cascaded optical χ(2) (quadratic) nonlinearities as an example and show that the dynamics can be construed (to leading order) as self-phase modulations of dressed fundamental modes plus cross-phase modulations of dressed fundamental and second-harmonic modes. We then formally eliminate the second-harmonic degrees of freedom and identify emergent features of the fundamental wave dynamics, such as two-photon loss channels, and examine conditions for accuracy of the reduced model in dispersive and dissipative parameter regimes. Our results highlight the utility of system-reservoir methods for deriving accurate, intuitive reduced models for complex dynamics in broadband nonlinear quantum photonics, and may help guide quantum technological proposals in emerging systems where quantum effects become significant at the single-photon level.

The interference between two independent photons stands as a crucial aspect of numerous quantum information protocols and technologies. In this work, we leverage fiber-coupled devices, which encompass fibered photon pair-sources and off- the-shelf optics, to demonstrate Hong-Ou-Mandel interference. We employ two distinct single photon sources, namely an heralded single-photon source and a weak coherent laser source, both operating asynchronously in continuous-wave regime. We record two-photon coincidences, showing a state-of-art visibility of 91.9(5)%. This work, compliant with telecom technology provides realistic backbones for establishing long- range communication based on quantum teleportation in hybrid quantum networks.

The application of quantum algorithms to classical problems is generally accompanied by significant bottlenecks when transferring data between quantum and classical states, often negating any intrinsic quantum advantage. Here we address this challenge for a well-known algorithm for linear systems of equations, originally proposed by Harrow, Hassidim and Lloyd (HHL), by adapting it into a predictor-corrector instead of a direct solver. Rather than seeking the solution at the next time step, the goal now becomes determining the change between time steps. This strategy enables the intelligent omission of computationally costly steps commonly found in many classical algorithms, while simultaneously mitigating the notorious readout problems associated with extracting solutions from a quantum state. Random or regularly performed skips instead lead to simulation failure. We demonstrate that our methodology secures a useful polynomial advantage over a conventional application of the HHL algorithm. The practicality and versatility of the approach are illustrated through applications in various fields such as smoothed particle hydrodynamics, plasma simulations, and reactive flow configurations. Moreover, the proposed algorithm is well suited to run asynchronously on future heterogeneous hardware infrastructures and can effectively leverage the synergistic strengths of classical as well as quantum compute resources.

The simulation of molecular electronic structure is an important application of quantum devices. Recently, it has been shown that quantum devices can be effectively combined with classical supercomputing centers in the context of the sample-based quantum diagonalization (SQD) algorithm. This allowed the largest electronic structure quantum simulation to date (77 qubits) and opened near-term devices to practical use cases in chemistry toward the hundred-qubit mark. However, the description of many important physical and chemical properties of those systems, such as photo-absorption/-emission, requires a treatment that goes beyond the ground state alone. In this work, we extend the SQD algorithm to determine low-lying molecular excited states. The extended-SQD method improves over the original SQD method in accuracy, at the cost of an additional computational step. It also improves over quantum subspace expansion based on single and double electronic excitations, a widespread approach to excited states on pre-fault-tolerant quantum devices, in both accuracy and efficiency. We employ the extended SQD method to compute the first singlet (S1) and triplet (T1) excited states of the nitrogen molecule with a correlation-consistent basis set, and the ground- and excited-state properties of the [2Fe-2S] cluster.

Building scalable and secure quantum networks requires advanced quantum key distribution (QKD) protocols that support multi-user connectivity. Continuous-variable (CV) measurement-device-independent (MDI) QKD, which eliminates all detector side-channel attacks, is a promising candidate for creating various quantum network topologies-such as quantum access networks and star-type topologies-using standard technology and providing high secure key rates. However, its security has so far only been experimentally demonstrated in asymptotic regimes with limited secret key rates and complex experimental setups, limiting its practical applications. Here, we report an experimental validation of a CV MDI-QKD system, achieving a secure key rate of 2.6 Mbit s⁻¹ against collective attacks in the finite-size regime over a 10 km fiber link. This is achieved using a new system design, incorporating a locally generated local oscillator, a new relay structure, a real-time phase locking system, and a well-designed digital-signal-processing pipeline for quantum state preparation and CV Bell measurements at a symbol rate of 20 MBaud. Our results set a new benchmark for secure key exchange and open the possibility of establishing high-performance CV MDI-QKD networks, paving the way toward a scalable quantum network.

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.

Due to their non-iterative nature, fixed unitary coupled cluster (UCC) ansätze are attractive for performing quantum chemistry variational quantum eigensolver (VQE) computations as they avoid pre-circuit measurements on a quantum computer. However, achieving chemical accuracy for strongly correlated systems with UCC requires further inclusion of higher-order fermionic excitations beyond triples increasing circuit depth. We introduce k-non-iterative disentangled unitary coupled cluster (NI-DUCC), a fixed and non-iterative disentangled UCC compact ansatz, based on specific ‘k’ sets of ‘qubit’ excitations, eliminating the needs for fermionic-type excitations. These elements scale linearly ( O(n)) by leveraging Lie algebraic structures, with n being the number of qubits. The key excitations are screened through specific selection criteria, including the enforcement of all symmetries, to ensure the construction of a robust set of generators. NI-DUCC employs ‘k’ products of the exponential of O(n)- anti-Hermitian Pauli operators, where each single Pauli string has a length p. This results in a fewer two-qubit CNOT gates circuit scaling, O(knp), suitable for hardware implementations. Tested on LiH, H6 and BeH2, NI-DUCC-VQE achieves both chemical accuracy and rapid convergence even for molecules deviating significantly from equilibrium. It is hardware-efficient, reaching the exact full configuration interaction energy solution at specific layers, while reducing significantly the VQE optimization steps. NI-DUCC-VQE effectively addresses the gradient measurement bottleneck of ADAPT-VQE-like iterative algorithms, yet the classical computational cost of constructing the O(n) set of excitations increases exponentially with the number of qubits. We provide a first implementation for constructing the generators’ set, able to handle up to 20 qubits and discuss the efficiency perspectives.

Quantum direct coding or Schumacher compression generalised the ideas of Shannon theory, gave an operational meaning to the von Neumann entropy and established the term qubit. But remembering that information processing is carried out by physical processes prompts one to wonder what thermodynamic resources are required to compress quantum information and how they constrain one’s ability to perform this task. That is, if Alice and Bob only have access to thermal quantum states and clocks with finite accuracy, how well can they measure, encode and decode pure quantum state messages? In this work we examine these questions by modeling Alice’s typical measurement as a unitary involving a measurement probe, investigating imperfect timekeeping on encoding and decoding and considering the role of temperature in Bob’s appended qubits. In doing so, we derive fidelity bounds for this protocol involving the correlations Alice can form with their measurement probe, the variance of the clock’s ticks and the temperature of Bob’s qubits. Finally, we give an insight into the entropy produced by these two agents throughout the compression protocol by relating the resources they use to a quantum thermodynamic cooling protocol.

Quantum computing holds transformative potential for various domains including cheminformatics through advancements in quantum algorithms. The key to realizing improvements with quantum algorithms in cheminformatics is encoding chemical data like proteins as quantum states with quantum state preparation. In this work, we propose a computational framework to encode proteins as quantum states for efficient downstream quantum processing. Protein data representations are encoded as multi-qubit quantum states with iterative quantum sparse state preparation guided by the classical heuristic search method for optimal gate sequence identification. The validity and efficiency of the proposed method is demonstrated with various computational experiments to encode uniform random states as well as proteins. Several performance comparisons against the baselines of exact and variational state preparation methods, the proposed approach is able to encode proteins with 25% fewer controlled-NOT gates while performing orders of magnitude faster than the variational method.