npj Quantum Information最新文献

Legitimate quantum operations must adhere to principles of quantum mechanics, particularly the requirements of complete positivity and trace preservation. Yet, non-completely positive maps, especially Hermitian-preserving maps, play a crucial role in quantum information science. Here, we introduce the Hermitian-preserving map exponentiation algorithm, which can effectively simulate the action of an arbitrary Hermitian-preserving map by exponentiating its output, ({mathcal{N}}(rho )), into a quantum process, ({e}^{-i{mathcal{N}}(rho )t}). We analyze the sample complexity of this algorithm and prove its optimality in certain cases. Utilizing positive but not completely positive maps, this algorithm provides exponential speedups in entanglement detection and quantification compared to protocols based on single-copy operations. In addition, it facilitates the encoding-free recovery of noiseless quantum states from multiple noisy ones by simulating the inverse map of the corresponding noise channel, providing a new approach to handling quantum noises. This algorithm acts as a building block of large-scale quantum algorithms and presents a pathway for exploring potential quantum speedups across a wide range of information-processing tasks.

Optical quantum communication technologies are making the prospect of unconditionally secure and efficient information transfer a reality. The possibility of generating and reliably detecting quantum states of light, with the further need of increasing the private data-rate is where most research efforts are focusing. The physical concept of entanglement is a solution guaranteeing the highest degree of security in device-independent schemes, yet its implementation and preservation over long communication links is hard to achieve. Lithium niobate-on-insulator has emerged as a revolutionising platform for high-speed classical telecommunication and is equally suited for quantum information applications owing to the large second-order nonlinearities that can efficiently produce entangled photon pairs. In this work, we generate maximally entangled quantum states in the time-bin basis using lithium niobate-on-insulator photonics at the fibre optics telecommunication wavelength, and reconstruct the density matrix by quantum tomography on a single photonic integrated circuit. We use on-chip periodically-poled lithium niobate as source of entangled qubits with a brightness of 242 MHz/mW and perform quantum tomography with a fidelity of 91.9 ± 1.0 %. Our results, combined with the established large electro-optic bandwidth of lithium niobate, showcase the platform as perfect candidate to realise fibre-coupled, high-speed time-bin quantum communication modules that exploit entanglement to achieve information security.

Fast and high-fidelity qubit measurement is essential for quantum error correction in universal quantum computing. This study examines dispersive measurement of a spin in a semiconductor double quantum dot using a nonlinear microwave resonator. By employing displaced squeezed vacuum states, we achieve rapid, high-fidelity readout for silicon spin qubits. Our results show that modest squeezing and mild nonlinearity significantly enhance the signal-to-noise ratio (SNR) and the fidelity of qubit-state readout. By optimally adjusting the phases of squeezing and nonlinearity, we reduce readout time to sub-microsecond ranges. With current technology parameters (κ ≈ 2χ_s, χ_s/(2π) ≈ 0.15 MHz), utilizing a displaced squeezed vacuum state with 30 photons and a modest squeezing parameter r ≈ 0.6, along with a nonlinear microwave resonator charactered by a strength of λ ≈ − 1.2χ_s, a readout fidelity of 98% can be attained within a readout time of around 0.6 μs.

We propose a fault-tolerant scheme for generating long-range entanglement at the ends of a rectangular array of qubits of length R with a square cross-section of (m=O({log }^{2}R)) qubits. It is realized by a constant-depth circuit producing a constant-fidelity Bell-pair (independent of R) for local stochastic noise of strength below an experimentally realistic threshold. The scheme can be viewed as a quantum bus in a quantum computing architecture where qubits are arranged on a rectangular 3D grid, and all operations are between neighboring qubits. Alternatively, it can be seen as a quantum repeater protocol along a line, with neighboring repeaters placed at a short distance to allow constant-fidelity nearest-neighbor operations. To show our protocol uses a number of qubits close to optimal, we show that any noise-resilient distance-R entanglement generation scheme realized by a constant-depth circuit needs at least (m=Omega (log R)) qubits per repeater.

Large-scale communication networks, such as the Internet, rely on routing packets of data through multiple intermediate nodes to transmit information from a sender to a receiver. In this paper, we develop a model of a quantum communication network that routes information simultaneously along multiple paths passing through intermediate stations. We demonstrate that a quantum routing approach can in principle extend the distance over which information can be transmitted reliably. Surprisingly, the benefit of quantum routing also applies to the transmission of classical information: even if the transmitted data is purely classical, delocalising it on multiple routes can enhance the achievable transmission distance. Our findings highlight the potential of a future quantum internet not only for achieving secure quantum communication and distributed quantum computing but also for extending the range of classical data transmission.

This paper improves and demonstrates the usefulness of the first quantized plane-wave algorithms for the quantum simulation of electronic structure. We describe our quantum algorithm for first quantized simulation that accurately includes pseudopotentials. We focus on the Goedecker-Tetter-Hutter pseudopotential, and despite its complicated form, we block encode the associated operator without significantly increasing the overall cost of quantum simulation. This is surprising since simulating the nuclear potential is much simpler without pseudopotentials, yet is still the bottleneck. We also generalize prior methods to enable the simulation of materials with non-cubic unit cells, which requires nontrivial modifications. Finally, we combine these techniques to estimate block-encoding costs for commercially relevant instances of heterogeneous catalysis (e.g. carbon monoxide adsorption) and compare to the quantum resources needed to simulate materials in second quantization. We conclude that for computational cells with many particles, first quantization often requires meaningfully less spacetime volume.

In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.

Hybrid quantum-classical approaches offer potential solutions to quantum chemistry problems, yet they often manifest as constrained optimization problems. Here, we explore the interconnection between constrained optimization and generalized eigenvalue problems through the Unitary Coupled Cluster (UCC) excitation generators. Inspired by the generator coordinate method, we employ these UCC excitation generators to construct non-orthogonal, overcomplete many-body bases, projecting the system Hamiltonian into an effective Hamiltonian, which bypasses issues such as barren plateaus that heuristic numerical minimizers often encountered in standard variational quantum eigensolver (VQE). Diverging from conventional quantum subspace expansion methods, we introduce an adaptive scheme that robustly constructs the many-body basis sets from a pool of the UCC excitation generators. This scheme supports the development of a hierarchical ADAPT quantum-classical strategy, enabling a balanced interplay between subspace expansion and ansatz optimization to address complex, strongly correlated quantum chemical systems cost-effectively, setting the stage for more advanced quantum simulations in chemistry.

Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing — a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.

Quantum computing platforms are subject to contradictory engineering requirements: qubits must be protected from mutual interactions when idling (‘doing nothing’), and strongly interacting when in operation. If idling qubits are not sufficiently protected, information ‘leaks’ into neighbouring qubits, becoming ultimately inaccessible. Candidate solutions to this dilemma include many-body localization, dynamical decoupling, and active error correction. However, no protocol exists to quantify this effect in a similar way to e.g. SPAM errors. We develop a scalable, device non-specific, protocol for quantifying idle information loss by exploiting tools from quantum information theory. We implement this protocol in over 3500 experiments carried out across 4 months (Dec 2023–Mar 2024) on IBM’s entire Falcon 5.11 processor series. After accounting for other error sources, we detect information loss to high degrees of statistical significance. This work thus provides a firm quantitative foundation from which the protection-operation dilemma can be investigated and ultimately resolved.